From f3159f1b22250a51b13e517ca46973bbd096e7ee Mon Sep 17 00:00:00 2001 From: Gage Krumbach Date: Tue, 8 Jul 2025 12:54:56 -0500 Subject: [PATCH 001/240] Remove deprecated files and update project structure by deleting unused scripts and documentation. Enhance project setup with updated dependencies and installation instructions in README. Ensure clarity in project organization for future development. --- .env.example | 44 + README.md | 327 ++++- docker-compose.yml | 44 + docs/README.md | 57 - docs/api/authentication.md | 568 --------- docs/api/endpoints.md | 624 ---------- docs/architecture/decisions.md | 355 ------ docs/architecture/overview.md | 261 ---- docs/deployment/configuration.md | 1022 ---------------- docs/deployment/installation.md | 1087 ----------------- docs/development/setup.md | 571 --------- docs/development/standards.md | 721 ----------- docs/development/testing.md | 925 -------------- docs/user-guide/faq.md | 439 ------- docs/user-guide/getting-started.md | 344 ------ llama_stack_setup.py | 23 - prompts/refine_feature.md | 13 - pyproject.toml | 17 +- .../llama_stack_setup.py | 37 + .../rhoai_ai_feature_sizing/main.py | 88 +- .../prompts/refine_feature.md | 260 ++++ .../prompts/validate_refinement.md | 117 ++ .../stages}/draft_jiras.py | 0 .../stages}/estimate.py | 0 .../stages/refine_feature.py | 269 ++++ stages/refine_feature.py | 71 -- uv.lock | 1020 +++++++++++++++- 27 files changed, 2158 insertions(+), 7146 deletions(-) create mode 100644 .env.example create mode 100644 docker-compose.yml delete mode 100644 docs/README.md delete mode 100644 docs/api/authentication.md delete mode 100644 docs/api/endpoints.md delete mode 100644 docs/architecture/decisions.md delete mode 100644 docs/architecture/overview.md delete mode 100644 docs/deployment/configuration.md delete mode 100644 docs/deployment/installation.md delete mode 100644 docs/development/setup.md delete mode 100644 docs/development/standards.md delete mode 100644 docs/development/testing.md delete mode 100644 docs/user-guide/faq.md delete mode 100644 docs/user-guide/getting-started.md delete mode 100644 llama_stack_setup.py delete mode 100644 prompts/refine_feature.md create mode 100644 src/rhoai_ai_feature_sizing/llama_stack_setup.py rename main.py => src/rhoai_ai_feature_sizing/main.py (72%) create mode 100644 src/rhoai_ai_feature_sizing/prompts/refine_feature.md create mode 100644 src/rhoai_ai_feature_sizing/prompts/validate_refinement.md rename {stages => src/rhoai_ai_feature_sizing/stages}/draft_jiras.py (100%) rename {stages => src/rhoai_ai_feature_sizing/stages}/estimate.py (100%) create mode 100644 src/rhoai_ai_feature_sizing/stages/refine_feature.py delete mode 100644 stages/refine_feature.py diff --git a/.env.example b/.env.example new file mode 100644 index 000000000..3a40d4b5c --- /dev/null +++ b/.env.example @@ -0,0 +1,44 @@ +# ============================================================================= +# RHOAI AI Feature Sizing - Environment Configuration +# ============================================================================= +# Copy this file to .env and update the values for your environment +# cp env.example .env + +# ============================================================================= +# JIRA CONFIGURATION +# ============================================================================= +# Your Jira instance URL (without trailing slash) +JIRA_URL=https://issues.redhat.com + +# Your Jira API token (generate from: https://id.atlassian.com/manage-profile/security/api-tokens) +JIRA_API_TOKEN= + +JIRA_USERNAME=username@redhat.com + +# ============================================================================= +# LLAMA STACK OLLAMA CONFIGURATION +# ============================================================================= +# Ollama model to use (must be pulled/available in your Ollama instance) +INFERENCE_MODEL=llama3.2:3b + +# Ollama server URL (running locally or on another host) +OLLAMA_URL=http://host.docker.internal:11434 + +# ============================================================================= +# LOGGING AND DEBUG +# ============================================================================= +# Log level (DEBUG, INFO, WARNING, ERROR) +LOG_LEVEL=INFO + +# Enable debug mode for the Python application +DEBUG=false + +# ============================================================================= +# DOCKER CONFIGURATION +# ============================================================================= + +# ============================================================================= +# APPLICATION SETTINGS +# ============================================================================= +# Directory for output files (relative to container) +OUTPUT_DIR=/outputs diff --git a/README.md b/README.md index d913219bd..dcd637dd1 100644 --- a/README.md +++ b/README.md @@ -1,74 +1,311 @@ # RHOAI AI Feature Sizing -A Python tool for AI-powered feature sizing and estimation, designed to help teams better estimate development effort for features and projects. +An intelligent system for analyzing and sizing AI features using Llama Stack with Ollama and Jira integration. -## 🚀 Quick Start +## 📦 Installation + +### Prerequisites + +1. **Python 3.12+** - Required for the CLI application +2. **uv** - Fast Python package manager +3. **Ollama** - Local LLM runtime +4. **Docker** - For containerized services +5. **Jira Access** - API token for Jira integration + + +### Step 1: Clone and Setup Project ```bash # Clone the repository -git clone +git clone cd rhoai-ai-feature-sizing -# Install dependencies (requires Python 3.12+) -pip install -e . +# Install Python dependencies with uv +uv sync -# Run the application -python main.py +# Copy environment template +cp env.example .env +# Edit .env with your Jira credentials ``` -## 📋 Overview +### Step 2: Start Ollama and Pull Model + +```bash +# Pull the model +ollama pull llama3.2:3b + +# Start Ollama server +ollama serve +``` -This project provides tools and workflows for: -- Feature refinement and analysis -- AI-powered estimation and sizing -- Integration with project management tools (JIRA) -- Structured prompt-based workflows +### Step 3: Start Services -## 🏗️ Project Structure +```bash +# Start Docker services (Llama Stack + Jira MCP) +docker-compose up -d +# Verify services are running +docker-compose ps ``` -├── docs/ # 📚 Comprehensive project documentation -├── tools/ # 🛠️ Utility tools and integrations -├── stages/ # 📊 Processing stages and workflows -├── prompts/ # 💬 AI prompt templates -├── main.py # 🎯 Main application entry point -└── pyproject.toml # 📦 Project configuration + +## 🚀 Running the CLI + +### Basic Usage + +```bash +# Check available commands +uv run python -m rhoai_ai_feature_sizing.main --help ``` -## 📚 Documentation +### Individual Stages + +```bash +# 1. Refine a Jira issue into detailed spec +uv run python -m rhoai_ai_feature_sizing.main stage refine PROJ-123 + +# 2. Create epics from refined spec (not yet implemented) +uv run python -m rhoai_ai_feature_sizing.main stage epics refined_PROJ-123.md + +# 3. Draft Jira tickets from epics (not yet implemented) +uv run python -m rhoai_ai_feature_sizing.main stage jiras epics_PROJ-123.md + +# 4. Create estimates from tickets (not yet implemented) +uv run python -m rhoai_ai_feature_sizing.main stage estimate jiras_PROJ-123.md +``` -For comprehensive documentation, visit the [/docs](./docs/) directory: +### Full Pipeline -- **[📖 Full Documentation](./docs/README.md)** - Complete documentation hub -- **[🚀 Getting Started](./docs/user-guide/getting-started.md)** - Quick start guide -- **[🏗️ Architecture](./docs/architecture/overview.md)** - System architecture -- **[🛠️ Development](./docs/development/setup.md)** - Development setup -- **[❓ FAQ](./docs/user-guide/faq.md)** - Frequently asked questions +```bash +# Run complete workflow (only refine stage currently works) +uv run python -m rhoai_ai_feature_sizing.main run PROJ-123 +``` -## 🤝 Contributing +### API Server Mode -We welcome contributions! Please see our [Contributing Guide](./CONTRIBUTING.md) for details on: -- How to submit changes -- Coding standards -- Testing requirements -- Documentation guidelines +```bash +# Start the FastAPI server +uv run python -m rhoai_ai_feature_sizing.api +``` -## 🔧 Requirements +## 🛠️ Configuration -- Python 3.12+ -- Dependencies listed in `pyproject.toml` +### Environment Variables (.env file) -## 📄 License +```bash +# Jira Configuration (Required) +JIRA_URL=https://your-company.atlassian.net +JIRA_API_TOKEN=your-jira-api-token-here -[Add license information here] +# Ollama Configuration +INFERENCE_MODEL=llama3.2:3b +OLLAMA_URL=http://host.docker.internal:11434 +LLAMA_STACK_PORT=8321 -## 🆘 Support +# Service URLs +LLAMA_STACK_URL=http://localhost:8321 +MCP_ATLASSIAN_URL=http://localhost:9000/sse -- 📖 Check the [documentation](./docs/) -- ❓ Review the [FAQ](./docs/user-guide/faq.md) -- 🐛 Report issues in the repository -- 💬 See [Contributing Guide](./CONTRIBUTING.md) for help contributing +# API Configuration +OUTPUT_DIR=./outputs +``` + +## 🔧 What's Currently Implemented + +### ✅ Working Features + +**Refine Stage** - Convert Jira issues to detailed specs: +```bash +# Fetch a Jira issue and refine it into a detailed spec +uv run python -m rhoai_ai_feature_sizing.cli refine PROJ-123 +# Output: refined_PROJ-123.md +``` + +**API Server** - RESTful API with job management: +```bash +# Start the API server +uv run python -m rhoai_ai_feature_sizing.api + +# API endpoints available at http://localhost:8000: +# POST /api/v1/stages/refine - Refine a Jira issue +# GET /api/v1/jobs/{job_id} - Check job status +# GET /api/v1/files/{filename} - Download output files +# GET /health - Service health check +``` + +**CLI Client** - Connects to API server: +```bash +# All CLI commands work by connecting to the API server +uv run python -m rhoai_ai_feature_sizing.cli config --api-url http://localhost:8000 +uv run python -m rhoai_ai_feature_sizing.cli health +uv run python -m rhoai_ai_feature_sizing.cli refine PROJ-123 --wait +``` + +### 🚧 Planned Features (Not Yet Implemented) + +The following stages are defined but not implemented: +- **Epics Stage**: Break refined specs into epics +- **Jiras Stage**: Create individual Jira tickets from epics +- **Estimate Stage**: Add story point estimates to tickets + +### 🔍 Architecture Details + +**What Actually Happens:** +1. **CLI** makes HTTP requests to **API server** (FastAPI) +2. **API server** uses **Llama Stack client** to connect to **Llama Stack server** +3. **Llama Stack** uses **MCP Atlassian toolgroup** to fetch Jira issues +4. **AI model** (via Ollama) processes the issue and fills template + +**File Flow:** +``` +Jira Issue PROJ-123 + ↓ (via MCP) +Jira details (JSON) + ↓ (via LLM + template) +refined_PROJ-123.md + ↓ (planned stages) +epics_PROJ-123.md → jiras_PROJ-123.md → estimates_PROJ-123.md +``` + +## 🎯 Architecture Overview + +``` +┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ +│ CLI/API │ │ Llama Stack │ │ Jira MCP │ +│ (Python) │───▶│ (Docker) │───▶│ (Docker) │ +└─────────────────┘ └─────────────────┘ └─────────────────┘ + │ │ │ + │ ▼ ▼ + │ ┌─────────────────┐ ┌─────────────────┐ + └─────────────▶│ Ollama │ │ Jira API │ + │ (Local LLM) │ │ (Remote) │ + └─────────────────┘ └─────────────────┘ +``` + +**Key Components:** +- **CLI Client**: Python application that makes HTTP requests to API server +- **API Server**: FastAPI application with job management and file handling +- **Llama Stack**: LLM inference server with tool integration (Docker) +- **Ollama**: Local LLM runtime (llama3.2:3b) +- **Jira MCP**: Model Context Protocol server for Jira integration (Docker) + +## 🔍 Troubleshooting + +### Installation Issues + +**uv not found**: +```bash +# Install uv +curl -LsSf https://astral.sh/uv/install.sh | sh +source ~/.bashrc # or restart terminal +``` + +**Python version too old**: +```bash +# Check Python version +python --version # Should be 3.12+ + +# Install Python 3.12+ if needed (macOS) +brew install python@3.12 +``` + +**Ollama not found**: +```bash +# Install Ollama +curl -fsSL https://ollama.com/install.sh | sh +ollama serve +``` + +### Runtime Issues + +**Model not available**: +```bash +# Pull the model manually +ollama pull llama3.2:3b + +# Check available models +ollama list +``` + +**Services not running**: +```bash +# Check container status +docker-compose ps + +# View logs +docker-compose logs -f llama-stack +docker-compose logs -f jira-mcp + +# Restart services +docker-compose restart +``` + +**CLI import errors**: +```bash +# Reinstall dependencies +uv sync --reinstall + +# Check if services are accessible +curl http://localhost:8321/health +curl http://localhost:9000/healthz +``` + +### Service Health Checks + +```bash +# Test Llama Stack +curl http://localhost:8321/health + +# Test Jira MCP +curl http://localhost:9000/healthz + +# Test Ollama +curl http://localhost:11434/api/tags + +# Test CLI connectivity +uv run python -c "from rhoai_ai_feature_sizing.cli import main; print('CLI import successful')" +``` + +## 📚 Development + +### Project Structure +``` +rhoai-ai-feature-sizing/ +├── src/rhoai_ai_feature_sizing/ +│ ├── cli.py # CLI commands +│ ├── api.py # FastAPI server +│ ├── main.py # Core logic +│ └── stages/ # Processing pipelines +├── tests/ # Test files +├── docker-compose.yml # Services configuration +├── pyproject.toml # Python dependencies +└── README.md +``` + +### Adding New Features +1. **Add CLI commands**: Edit `src/rhoai_ai_feature_sizing/cli.py` +2. **Add processing stages**: Create files in `src/rhoai_ai_feature_sizing/stages/` +3. **Add tests**: Create test files in `tests/` +4. **Update dependencies**: Run `uv add ` + +### Contributing +```bash +# Setup development environment +uv sync --dev + +# Run tests +uv run pytest + +# Format code +uv run ruff format src/ tests/ + +# Type checking +uv run mypy src/ +``` ---- +## 🔗 Resources -*For detailed information about any aspect of this project, please refer to the comprehensive documentation in the [/docs](./docs/) directory.* \ No newline at end of file +- [uv Documentation](https://docs.astral.sh/uv/) +- [Ollama Models](https://ollama.com/library) +- [Llama Stack Documentation](https://llama-stack.readthedocs.io/) +- [Jira API Tokens](https://id.atlassian.com/manage-profile/security/api-tokens) +- [Docker Compose](https://docs.docker.com/compose/) \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 000000000..9607f06f5 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,44 @@ +services: + # Jira MCP Server + jira-mcp: + image: ghcr.io/sooperset/mcp-atlassian:latest + ports: + - "9000:9000" + environment: + - JIRA_URL=${JIRA_URL} + - JIRA_API_TOKEN=${JIRA_API_TOKEN} + - JIRA_USERNAME=${JIRA_USERNAME} + - PORT=9000 + command: ["--transport", "sse", "-vv"] + restart: unless-stopped + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:9000/healthz"] + interval: 45s + timeout: 10s + retries: 3 + + # Llama Stack Server with Ollama distribution + llama-stack: + image: llamastack/distribution-ollama:latest + ports: + - "8321:8321" + volumes: + - ~/.llama:/root/.llama + environment: + - INFERENCE_MODEL=${INFERENCE_MODEL} + - OLLAMA_URL=${OLLAMA_URL} + command: + [ + "--port", + "8321", + "--env", + "INFERENCE_MODEL=${INFERENCE_MODEL}", + "--env", + "OLLAMA_URL=${OLLAMA_URL}", + ] + restart: unless-stopped + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:8321/v1/health"] + interval: 30s + timeout: 10s + retries: 3 diff --git a/docs/README.md b/docs/README.md deleted file mode 100644 index a7028d6cc..000000000 --- a/docs/README.md +++ /dev/null @@ -1,57 +0,0 @@ -# Project Documentation - -Welcome to the **rhoai-ai-feature-sizing** documentation! This directory contains comprehensive documentation for the project, organized into sections for easy navigation. - -## 📖 Documentation Sections - -### 🏗️ [Architecture](./architecture/) -Technical architecture, design decisions, and system overview. -- [System Overview](./architecture/overview.md) - High-level architecture and components -- [Design Decisions](./architecture/decisions.md) - Key architectural decisions and rationale - -### 👥 [User Guide](./user-guide/) -End-user documentation and guides. -- [Getting Started](./user-guide/getting-started.md) - Quick start guide for new users -- [FAQ](./user-guide/faq.md) - Frequently asked questions - -### 🔌 [API](./api/) -API documentation and integration guides. -- [Endpoints](./api/endpoints.md) - Available API endpoints and usage -- [Authentication](./api/authentication.md) - Authentication methods and setup - -### 🛠️ [Development](./development/) -Developer-focused documentation. -- [Setup](./development/setup.md) - Development environment setup -- [Code Standards](./development/standards.md) - Coding standards and best practices -- [Testing](./development/testing.md) - Testing guidelines and procedures - -### 📦 [Deployment](./deployment/) -Deployment and operations documentation. -- [Installation](./deployment/installation.md) - Installation instructions -- [Configuration](./deployment/configuration.md) - Configuration options and settings - -## 🚀 Quick Links - -- [Project Repository](../) - Return to main project -- [Contributing Guide](../CONTRIBUTING.md) - How to contribute to this project -- [Project Setup](./user-guide/getting-started.md) - Get started quickly - -## 📝 Document Status - -| Section | Status | Last Updated | -|---------|--------|--------------| -| Architecture | 🟡 In Progress | TBD | -| User Guide | 🟡 In Progress | TBD | -| API | 🔴 Planned | TBD | -| Development | 🟡 In Progress | TBD | -| Deployment | 🔴 Planned | TBD | - -## 💡 Need Help? - -- Check the [FAQ](./user-guide/faq.md) -- Review the [Contributing Guide](../CONTRIBUTING.md) -- Open an issue in the project repository - ---- - -*This documentation is continuously updated. If you notice any gaps or have suggestions for improvements, please contribute by following our [Contributing Guide](../CONTRIBUTING.md).* \ No newline at end of file diff --git a/docs/api/authentication.md b/docs/api/authentication.md deleted file mode 100644 index 645cb785b..000000000 --- a/docs/api/authentication.md +++ /dev/null @@ -1,568 +0,0 @@ -# API Authentication - -This document describes the authentication methods available for the RHOAI AI Feature Sizing API, built on [Llama Stack](https://llama-stack.readthedocs.io/en/latest/). - -## 🔐 Authentication Overview - -The RHOAI AI Feature Sizing API supports multiple authentication methods to secure access to AI-powered feature estimation capabilities. Authentication is required for all API endpoints except health checks and public documentation. - -## 🛡️ Authentication Methods - -### 1. API Key Authentication (Recommended) - -API Key authentication is the simplest and most commonly used method for programmatic access. - -#### Setup - -1. **Generate an API Key**: - ```bash - # Using the CLI - python -m rhoai_feature_sizing generate-api-key --user "john.doe@example.com" - - # Or via API (requires admin access) - curl -X POST http://localhost:8321/api/auth/keys \ - -H "Authorization: Bearer admin-token" \ - -H "Content-Type: application/json" \ - -d '{"name": "Development Key", "permissions": ["read", "write"]}' - ``` - -2. **API Key Response**: - ```json - { - "api_key": "rfs_key_1234567890abcdef", - "name": "Development Key", - "permissions": ["read", "write"], - "created_at": "2024-01-15T10:30:00.000Z", - "expires_at": "2025-01-15T10:30:00.000Z" - } - ``` - -#### Usage - -Include the API key in the `Authorization` header: - -```bash -curl -X POST http://localhost:8321/api/features/refine \ - -H "Authorization: Bearer rfs_key_1234567890abcdef" \ - -H "Content-Type: application/json" \ - -d '{"description": "Add user authentication"}' -``` - -**Python SDK:** -```python -from rhoai_feature_sizing import FeatureSizingClient - -client = FeatureSizingClient( - base_url="http://localhost:8321", - api_key="rfs_key_1234567890abcdef" -) -``` - -**JavaScript SDK:** -```javascript -const client = new FeatureSizingClient({ - baseUrl: 'http://localhost:8321', - apiKey: 'rfs_key_1234567890abcdef' -}); -``` - ---- - -### 2. JWT Token Authentication - -JWT tokens provide more sophisticated authentication with user context and expiration. - -#### Setup - -1. **Obtain JWT Token**: - ```bash - curl -X POST http://localhost:8321/api/auth/login \ - -H "Content-Type: application/json" \ - -d '{ - "username": "john.doe@example.com", - "password": "your-password" - }' - ``` - -2. **JWT Response**: - ```json - { - "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", - "refresh_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", - "token_type": "Bearer", - "expires_in": 3600, - "user": { - "id": "user_123", - "email": "john.doe@example.com", - "permissions": ["read", "write", "admin"] - } - } - ``` - -#### Usage - -Include the JWT token in the `Authorization` header: - -```bash -curl -X POST http://localhost:8321/api/features/refine \ - -H "Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." \ - -H "Content-Type: application/json" \ - -d '{"description": "Add user authentication"}' -``` - -#### Token Refresh - -```bash -curl -X POST http://localhost:8321/api/auth/refresh \ - -H "Content-Type: application/json" \ - -d '{"refresh_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."}' -``` - ---- - -### 3. OAuth 2.0 Integration - -For enterprise integrations, OAuth 2.0 provides secure, delegated access. - -#### Supported Providers - -- **Google Workspace** -- **Microsoft Azure AD** -- **GitHub Enterprise** -- **Custom OIDC providers** - -#### OAuth Flow - -1. **Authorization Request**: - ```bash - https://auth.your-domain.com/oauth/authorize? - client_id=your-client-id& - redirect_uri=https://your-app.com/callback& - response_type=code& - scope=feature-sizing.read feature-sizing.write& - state=random-state-string - ``` - -2. **Token Exchange**: - ```bash - curl -X POST https://auth.your-domain.com/oauth/token \ - -H "Content-Type: application/x-www-form-urlencoded" \ - -d "client_id=your-client-id" \ - -d "client_secret=your-client-secret" \ - -d "code=authorization-code" \ - -d "grant_type=authorization_code" - ``` - -3. **Using OAuth Token**: - ```bash - curl -X POST http://localhost:8321/api/features/refine \ - -H "Authorization: Bearer oauth-access-token" \ - -H "Content-Type: application/json" \ - -d '{"description": "Add user authentication"}' - ``` - ---- - -### 4. Development Mode (Local Only) - -For local development, you can disable authentication: - -```bash -# Set environment variable -export RHOAI_AUTH_DISABLED=true - -# Or in config.json -{ - "auth": { - "enabled": false, - "require_api_key": false - } -} -``` - -**⚠️ Warning**: Never disable authentication in production environments. - ---- - -## 🔑 API Key Management - -### Creating API Keys - -```bash -# Create a read-only key -python -m rhoai_feature_sizing create-api-key \ - --name "Analytics Dashboard" \ - --permissions read \ - --expires-in 90d - -# Create a full-access key -python -m rhoai_feature_sizing create-api-key \ - --name "CI/CD Pipeline" \ - --permissions read,write \ - --expires-in 1y -``` - -### Listing API Keys - -```bash -# List all keys for current user -curl -X GET http://localhost:8321/api/auth/keys \ - -H "Authorization: Bearer your-admin-token" -``` - -**Response:** -```json -{ - "api_keys": [ - { - "id": "key_123", - "name": "Development Key", - "permissions": ["read", "write"], - "created_at": "2024-01-15T10:30:00.000Z", - "last_used": "2024-01-16T09:15:00.000Z", - "expires_at": "2025-01-15T10:30:00.000Z", - "status": "active" - }, - { - "id": "key_456", - "name": "Analytics Dashboard", - "permissions": ["read"], - "created_at": "2024-01-10T14:20:00.000Z", - "last_used": "2024-01-16T08:30:00.000Z", - "expires_at": "2024-04-10T14:20:00.000Z", - "status": "active" - } - ] -} -``` - -### Revoking API Keys - -```bash -# Revoke a specific key -curl -X DELETE http://localhost:8321/api/auth/keys/key_123 \ - -H "Authorization: Bearer your-admin-token" - -# Revoke all keys for a user -curl -X DELETE http://localhost:8321/api/auth/keys \ - -H "Authorization: Bearer your-admin-token" \ - -H "Content-Type: application/json" \ - -d '{"user_id": "user_123"}' -``` - ---- - -## 🔒 Permissions and Scopes - -### Permission Levels - -| Permission | Description | Allowed Operations | -|-----------|-------------|-------------------| -| `read` | Read-only access | Get configurations, health checks, model info | -| `write` | Read and write access | All read operations + feature estimation, refinement | -| `admin` | Full administrative access | All operations + user management, configuration | -| `jira` | JIRA integration access | Create and update JIRA tickets | -| `batch` | Batch processing access | Submit batch estimation jobs | - -### Scope Examples - -```json -{ - "scopes": [ - "features:read", - "features:write", - "features:estimate", - "jira:create", - "jira:read", - "models:read", - "config:read" - ] -} -``` - -### Permission Checking - -API endpoints automatically check permissions: - -```bash -# This will fail with 403 if user lacks 'write' permission -curl -X POST http://localhost:8321/api/features/refine \ - -H "Authorization: Bearer read-only-key" \ - -H "Content-Type: application/json" \ - -d '{"description": "Add user authentication"}' -``` - -**Error Response:** -```json -{ - "error": { - "code": "INSUFFICIENT_PERMISSIONS", - "message": "API key lacks required 'write' permission", - "required_permissions": ["write"], - "current_permissions": ["read"] - } -} -``` - ---- - -## 🌐 Environment Configuration - -### Development Environment - -```bash -# .env file -RHOAI_AUTH_ENABLED=true -RHOAI_AUTH_METHOD=api_key -RHOAI_JWT_SECRET=dev-secret-key -RHOAI_API_KEY_EXPIRY=30d -RHOAI_REQUIRE_HTTPS=false -``` - -### Production Environment - -```bash -# .env file -RHOAI_AUTH_ENABLED=true -RHOAI_AUTH_METHOD=jwt -RHOAI_JWT_SECRET=your-secure-secret-key -RHOAI_JWT_EXPIRY=1h -RHOAI_REFRESH_TOKEN_EXPIRY=7d -RHOAI_REQUIRE_HTTPS=true -RHOAI_CORS_ORIGINS=https://your-frontend.com -``` - -### OAuth Configuration - -```bash -# OAuth environment variables -OAUTH_GOOGLE_CLIENT_ID=your-google-client-id -OAUTH_GOOGLE_CLIENT_SECRET=your-google-client-secret -OAUTH_AZURE_CLIENT_ID=your-azure-client-id -OAUTH_AZURE_CLIENT_SECRET=your-azure-client-secret -OAUTH_AZURE_TENANT_ID=your-azure-tenant-id -``` - ---- - -## 🛡️ Security Best Practices - -### API Key Security - -1. **Store Keys Securely**: - ```bash - # Use environment variables - export RHOAI_API_KEY="rfs_key_1234567890abcdef" - - # Or secure key management systems - aws secretsmanager get-secret-value --secret-id rhoai-api-key - ``` - -2. **Rotate Keys Regularly**: - ```bash - # Create new key before old one expires - python -m rhoai_feature_sizing create-api-key --name "Rotated Key" - - # Update applications with new key - # Revoke old key after verification - python -m rhoai_feature_sizing revoke-api-key --key-id "old-key-id" - ``` - -3. **Use Principle of Least Privilege**: - ```bash - # Create read-only keys for monitoring - python -m rhoai_feature_sizing create-api-key \ - --name "Monitoring" \ - --permissions read - - # Create write keys only when needed - python -m rhoai_feature_sizing create-api-key \ - --name "Feature Pipeline" \ - --permissions read,write,jira - ``` - -### Network Security - -1. **Use HTTPS in Production**: - ```bash - # Enforce HTTPS - export RHOAI_REQUIRE_HTTPS=true - ``` - -2. **Configure CORS Properly**: - ```bash - # Restrict origins - export RHOAI_CORS_ORIGINS="https://app.yourcompany.com,https://admin.yourcompany.com" - ``` - -3. **Rate Limiting**: - ```json - { - "rate_limits": { - "requests_per_minute": 60, - "burst_limit": 10, - "by_api_key": true - } - } - ``` - -### JWT Security - -1. **Strong Secrets**: - ```bash - # Generate secure secret - python -c "import secrets; print(secrets.token_urlsafe(64))" - ``` - -2. **Short Expiry Times**: - ```json - { - "jwt": { - "access_token_expiry": "1h", - "refresh_token_expiry": "7d", - "algorithm": "HS256" - } - } - ``` - ---- - -## 🔍 Authentication Troubleshooting - -### Common Issues - -**1. Invalid API Key** -```json -{ - "error": { - "code": "INVALID_API_KEY", - "message": "API key is invalid or expired" - } -} -``` - -**Solution**: Check key format and expiration: -```bash -# Verify key format (should start with 'rfs_key_') -echo "rfs_key_1234567890abcdef" | grep "^rfs_key_" - -# Check key status -curl -X GET http://localhost:8321/api/auth/keys/verify \ - -H "Authorization: Bearer rfs_key_1234567890abcdef" -``` - -**2. JWT Token Expired** -```json -{ - "error": { - "code": "TOKEN_EXPIRED", - "message": "JWT token has expired" - } -} -``` - -**Solution**: Refresh the token: -```bash -curl -X POST http://localhost:8321/api/auth/refresh \ - -H "Content-Type: application/json" \ - -d '{"refresh_token": "your-refresh-token"}' -``` - -**3. Insufficient Permissions** -```json -{ - "error": { - "code": "INSUFFICIENT_PERMISSIONS", - "message": "API key lacks required permissions" - } -} -``` - -**Solution**: Check and update permissions: -```bash -# Check current permissions -curl -X GET http://localhost:8321/api/auth/keys/current \ - -H "Authorization: Bearer your-api-key" - -# Request permission upgrade from admin -``` - -### Debugging Authentication - -Enable debug logging: -```bash -export RHOAI_LOG_LEVEL=debug -export RHOAI_AUTH_DEBUG=true -``` - -Check authentication status: -```bash -curl -X GET http://localhost:8321/api/auth/status \ - -H "Authorization: Bearer your-token" -``` - -**Response:** -```json -{ - "authenticated": true, - "user": { - "id": "user_123", - "email": "john.doe@example.com" - }, - "permissions": ["read", "write"], - "token_type": "api_key", - "expires_at": "2025-01-15T10:30:00.000Z" -} -``` - ---- - -## 🔗 Integration Examples - -### CI/CD Pipeline - -```yaml -# GitHub Actions example -name: Feature Estimation -on: [push] - -jobs: - estimate: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v2 - - name: Estimate Features - env: - RHOAI_API_KEY: ${{ secrets.RHOAI_API_KEY }} - run: | - python scripts/estimate_features.py -``` - -### Monitoring Dashboard - -```python -import os -import requests - -class FeatureSizingMonitor: - def __init__(self): - self.api_key = os.getenv('RHOAI_API_KEY') - self.base_url = "http://localhost:8321" - - def check_health(self): - response = requests.get( - f"{self.base_url}/api/health", - headers={"Authorization": f"Bearer {self.api_key}"} - ) - return response.json() -``` - ---- - -## 📚 Related Documentation - -- [API Endpoints](./endpoints.md) - Available API endpoints and usage -- [Getting Started](../user-guide/getting-started.md) - Basic setup and usage -- [Security Configuration](../deployment/configuration.md) - Production security setup - -*For additional security questions or enterprise authentication needs, please contact the development team.* \ No newline at end of file diff --git a/docs/api/endpoints.md b/docs/api/endpoints.md deleted file mode 100644 index 657b592ba..000000000 --- a/docs/api/endpoints.md +++ /dev/null @@ -1,624 +0,0 @@ -# API Endpoints - -This document describes the REST API endpoints available in the RHOAI AI Feature Sizing system. The API is built on top of [Llama Stack](https://llama-stack.readthedocs.io/en/latest/) and provides both direct and wrapped endpoints for feature estimation. - -## 🌐 Base URL - -**Development**: `http://localhost:8321` -**Production**: `https://your-domain.com/api` - -## 🔐 Authentication - -See [Authentication Documentation](./authentication.md) for details on API authentication methods. - -## 📋 API Endpoints - -### Feature Management - -#### `POST /api/features/refine` - -Refine a raw feature description into a detailed specification. - -**Request Body:** -```json -{ - "description": "Add user authentication to the mobile app", - "context": { - "project_type": "mobile_app", - "technology_stack": ["React Native", "Node.js"], - "team_size": 5 - }, - "options": { - "detail_level": "high", - "include_acceptance_criteria": true, - "include_technical_requirements": true - } -} -``` - -**Response:** -```json -{ - "id": "feat_123456", - "status": "success", - "original_description": "Add user authentication to the mobile app", - "refined_feature": { - "title": "Mobile App User Authentication System", - "description": "Implement comprehensive user authentication system for mobile application with OAuth2 integration, session management, and security features.", - "acceptance_criteria": [ - "Users can register with email and password", - "Users can log in with Google OAuth2", - "Users can log in with Facebook OAuth2", - "Session tokens expire after 30 days of inactivity", - "Failed login attempts are rate-limited", - "Password reset functionality via email" - ], - "technical_requirements": [ - "OAuth2 client configuration for Google and Facebook", - "JWT token generation and validation", - "Secure password hashing with bcrypt", - "Session storage and management", - "Rate limiting middleware", - "Email service integration for password reset" - ], - "dependencies": [ - "User management database schema", - "Email service provider setup", - "OAuth provider app registration" - ], - "estimated_complexity": "medium-high" - }, - "processing_time": 3.2, - "model_used": "llama3.2:3b" -} -``` - -**Status Codes:** -- `200 OK`: Feature successfully refined -- `400 Bad Request`: Invalid input data -- `429 Too Many Requests`: Rate limit exceeded -- `500 Internal Server Error`: Processing error - ---- - -#### `POST /api/features/estimate` - -Estimate the development effort for a feature. - -**Request Body:** -```json -{ - "feature": { - "title": "Mobile App User Authentication System", - "description": "Implement comprehensive user authentication...", - "acceptance_criteria": [...], - "technical_requirements": [...] - }, - "estimation_context": { - "team_velocity": 25, - "team_experience": "intermediate", - "project_deadline": "2024-06-01", - "complexity_factors": ["security", "integration", "mobile"] - }, - "options": { - "estimation_method": "story_points", - "confidence_level": "medium", - "include_risk_analysis": true - } -} -``` - -**Response:** -```json -{ - "id": "est_789012", - "status": "success", - "estimation": { - "story_points": 8, - "confidence": 82, - "estimated_hours": { - "min": 28, - "max": 40, - "expected": 32 - }, - "complexity_breakdown": { - "technical": "medium", - "business": "low", - "integration": "high" - }, - "risk_factors": [ - { - "factor": "OAuth provider API changes", - "impact": "medium", - "probability": "low", - "mitigation": "Use well-established OAuth libraries" - }, - { - "factor": "Mobile platform differences", - "impact": "high", - "probability": "medium", - "mitigation": "Comprehensive cross-platform testing" - } - ], - "recommendations": [ - "Start with Google OAuth2 implementation as it's most stable", - "Plan for extensive security testing", - "Consider implementing progressive rollout" - ] - }, - "processing_time": 2.8, - "model_used": "llama3.2:3b" -} -``` - ---- - -#### `POST /api/features/complete-workflow` - -Run the complete feature sizing workflow from description to JIRA ticket. - -**Request Body:** -```json -{ - "description": "Add user authentication to the mobile app", - "context": { - "project_type": "mobile_app", - "technology_stack": ["React Native", "Node.js"], - "team_size": 5 - }, - "jira_config": { - "project_key": "PROJ", - "issue_type": "Story", - "assignee": "john.doe@example.com", - "components": ["Mobile", "Authentication"] - }, - "options": { - "create_jira_ticket": true, - "send_notifications": false - } -} -``` - -**Response:** -```json -{ - "id": "workflow_345678", - "status": "success", - "refined_feature": { /* Feature refinement results */ }, - "estimation": { /* Estimation results */ }, - "jira_ticket": { - "id": "PROJ-1234", - "key": "PROJ-1234", - "url": "https://your-domain.atlassian.net/browse/PROJ-1234", - "status": "created" - }, - "processing_time": 8.5 -} -``` - ---- - -### Batch Operations - -#### `POST /api/features/batch-estimate` - -Process multiple features in a single request. - -**Request Body:** -```json -{ - "features": [ - { - "id": "feat_1", - "description": "Add user authentication" - }, - { - "id": "feat_2", - "description": "Implement payment gateway" - }, - { - "id": "feat_3", - "description": "Create admin dashboard" - } - ], - "context": { - "project_type": "web_app", - "team_velocity": 30 - }, - "options": { - "parallel_processing": true, - "include_summaries": true - } -} -``` - -**Response:** -```json -{ - "id": "batch_901234", - "status": "success", - "results": [ - { - "feature_id": "feat_1", - "status": "success", - "refined_feature": { /* ... */ }, - "estimation": { /* ... */ } - }, - { - "feature_id": "feat_2", - "status": "success", - "refined_feature": { /* ... */ }, - "estimation": { /* ... */ } - }, - { - "feature_id": "feat_3", - "status": "failed", - "error": "Feature description too vague for estimation" - } - ], - "summary": { - "total_features": 3, - "successful": 2, - "failed": 1, - "total_story_points": 15, - "average_confidence": 78, - "total_estimated_hours": { - "min": 50, - "max": 85, - "expected": 65 - } - }, - "processing_time": 12.3 -} -``` - ---- - -### JIRA Integration - -#### `POST /api/jira/create-ticket` - -Create a JIRA ticket from feature estimation results. - -**Request Body:** -```json -{ - "feature_estimation": { - "refined_feature": { /* Feature data */ }, - "estimation": { /* Estimation data */ } - }, - "jira_config": { - "project_key": "PROJ", - "issue_type": "Story", - "priority": "Medium", - "assignee": "john.doe@example.com", - "components": ["Backend", "API"], - "labels": ["ai-estimated", "sprint-planning"], - "custom_fields": { - "story_points": 8, - "confidence_level": 82 - } - }, - "template_options": { - "include_acceptance_criteria": true, - "include_technical_requirements": true, - "include_risk_factors": true, - "format": "detailed" - } -} -``` - -**Response:** -```json -{ - "status": "success", - "jira_ticket": { - "id": "10123", - "key": "PROJ-1234", - "url": "https://your-domain.atlassian.net/browse/PROJ-1234", - "fields": { - "summary": "Mobile App User Authentication System", - "description": "# Feature Description\n\nImplement comprehensive user authentication...", - "story_points": 8, - "assignee": { - "name": "john.doe@example.com", - "displayName": "John Doe" - }, - "status": "To Do", - "created": "2024-01-15T10:30:00.000Z" - } - }, - "template_used": "feature_story_template", - "processing_time": 1.2 -} -``` - ---- - -### System Management - -#### `GET /api/health` - -Check system health and status. - -**Response:** -```json -{ - "status": "healthy", - "timestamp": "2024-01-15T10:30:00.000Z", - "services": { - "llama_stack": { - "status": "running", - "url": "http://localhost:8321", - "model": "llama3.2:3b", - "response_time": 150 - }, - "jira": { - "status": "connected", - "base_url": "https://your-domain.atlassian.net", - "response_time": 200 - }, - "database": { - "status": "connected", - "response_time": 5 - } - }, - "performance": { - "avg_response_time": 2.3, - "requests_per_minute": 45, - "active_sessions": 3 - } -} -``` - ---- - -#### `GET /api/models` - -List available AI models and their capabilities. - -**Response:** -```json -{ - "models": [ - { - "id": "llama3.2:3b", - "name": "Llama 3.2 3B", - "type": "llm", - "status": "available", - "capabilities": ["text_generation", "reasoning"], - "performance": { - "speed": "fast", - "quality": "good", - "memory_usage": "low" - }, - "recommended_for": ["development", "quick_estimates"] - }, - { - "id": "llama3.1:8b", - "name": "Llama 3.1 8B", - "type": "llm", - "status": "available", - "capabilities": ["text_generation", "reasoning", "analysis"], - "performance": { - "speed": "medium", - "quality": "high", - "memory_usage": "medium" - }, - "recommended_for": ["production", "detailed_analysis"] - } - ], - "current_model": "llama3.2:3b", - "embedding_models": [ - { - "id": "sentence-transformers/all-MiniLM-L6-v2", - "name": "All MiniLM L6 v2", - "dimensions": 384, - "status": "available" - } - ] -} -``` - ---- - -### Configuration - -#### `GET /api/config` - -Get current system configuration. - -**Response:** -```json -{ - "estimation": { - "default_confidence_threshold": 80, - "max_story_points": 13, - "complexity_factors": ["technical", "business", "integration"], - "estimation_methods": ["story_points", "hours", "t_shirt_sizes"] - }, - "jira": { - "base_url": "https://your-domain.atlassian.net", - "default_project": "PROJ", - "default_issue_type": "Story", - "custom_fields": { - "story_points": "customfield_10106", - "confidence_level": "customfield_10107" - } - }, - "llama_stack": { - "base_url": "http://localhost:8321", - "default_model": "llama3.2:3b", - "timeout": 30, - "max_retries": 3 - }, - "rate_limits": { - "requests_per_minute": 60, - "batch_size_limit": 10, - "concurrent_requests": 5 - } -} -``` - ---- - -#### `PUT /api/config` - -Update system configuration. - -**Request Body:** -```json -{ - "estimation": { - "default_confidence_threshold": 85, - "max_story_points": 21 - }, - "jira": { - "default_project": "NEWPROJ" - } -} -``` - -**Response:** -```json -{ - "status": "success", - "message": "Configuration updated successfully", - "updated_fields": ["estimation.default_confidence_threshold", "estimation.max_story_points", "jira.default_project"], - "restart_required": false -} -``` - ---- - -## 🔄 Async Operations - -For long-running operations, the API supports asynchronous processing: - -#### `POST /api/features/estimate-async` - -Start an asynchronous estimation job. - -**Response:** -```json -{ - "job_id": "job_567890", - "status": "started", - "estimated_completion": "2024-01-15T10:35:00.000Z", - "status_url": "/api/jobs/job_567890" -} -``` - -#### `GET /api/jobs/{job_id}` - -Check the status of an asynchronous job. - -**Response:** -```json -{ - "job_id": "job_567890", - "status": "completed", - "progress": 100, - "started_at": "2024-01-15T10:30:00.000Z", - "completed_at": "2024-01-15T10:34:30.000Z", - "result": { /* Estimation results */ } -} -``` - -## 📊 Error Handling - -### Error Response Format - -```json -{ - "error": { - "code": "INVALID_FEATURE_DESCRIPTION", - "message": "Feature description is too vague for accurate estimation", - "details": { - "field": "description", - "minimum_length": 20, - "provided_length": 8 - }, - "suggestions": [ - "Provide more specific technical details", - "Include acceptance criteria", - "Specify integration requirements" - ] - }, - "request_id": "req_123456", - "timestamp": "2024-01-15T10:30:00.000Z" -} -``` - -### Common Error Codes - -| Code | Description | HTTP Status | -|------|-------------|-------------| -| `INVALID_INPUT` | Request validation failed | 400 | -| `FEATURE_TOO_VAGUE` | Feature description insufficient | 400 | -| `MODEL_UNAVAILABLE` | AI model not accessible | 503 | -| `RATE_LIMIT_EXCEEDED` | Too many requests | 429 | -| `JIRA_CONNECTION_FAILED` | JIRA integration error | 502 | -| `ESTIMATION_TIMEOUT` | Processing took too long | 504 | -| `AUTHENTICATION_REQUIRED` | Missing or invalid auth | 401 | -| `INSUFFICIENT_PERMISSIONS` | Access denied | 403 | - -## 📚 SDK Examples - -### Python SDK - -```python -from rhoai_feature_sizing import FeatureSizingClient - -# Initialize client -client = FeatureSizingClient( - base_url="http://localhost:8321", - api_key="your-api-key" -) - -# Refine a feature -refined = client.refine_feature( - "Add user authentication to mobile app", - context={"project_type": "mobile_app"} -) - -# Get estimation -estimate = client.estimate_feature(refined.feature) - -# Create JIRA ticket -ticket = client.create_jira_ticket( - refined.feature, - estimate, - project_key="PROJ" -) -``` - -### JavaScript/Node.js SDK - -```javascript -const { FeatureSizingClient } = require('@rhoai/feature-sizing'); - -const client = new FeatureSizingClient({ - baseUrl: 'http://localhost:8321', - apiKey: 'your-api-key' -}); - -// Complete workflow -const result = await client.completeWorkflow({ - description: 'Add user authentication to mobile app', - context: { projectType: 'mobile_app' }, - jiraConfig: { projectKey: 'PROJ' } -}); - -console.log('JIRA ticket created:', result.jiraTicket.url); -``` - ---- - -## 🔗 Related Documentation - -- [Authentication Guide](./authentication.md) - API authentication methods -- [Getting Started](../user-guide/getting-started.md) - Basic usage examples -- [Llama Stack API Reference](https://llama-stack.readthedocs.io/en/latest/references/api_reference.html) - Underlying API documentation - -*For more examples and advanced usage patterns, see our [GitHub repository examples](https://github.com/your-repo/examples).* \ No newline at end of file diff --git a/docs/architecture/decisions.md b/docs/architecture/decisions.md deleted file mode 100644 index eab01085c..000000000 --- a/docs/architecture/decisions.md +++ /dev/null @@ -1,355 +0,0 @@ -# Architectural Decision Records (ADRs) - -This document records key architectural decisions made during the development of the RHOAI AI Feature Sizing system. - -## ADR Format - -Each decision follows this format: -- **Status**: Proposed, Accepted, Superseded, Deprecated -- **Context**: The situation driving the need for a decision -- **Decision**: What we decided to do -- **Consequences**: The results of the decision - ---- - -## ADR-001: Adopt Llama Stack as AI Framework - -**Status**: Accepted -**Date**: 2024-01-XX -**Deciders**: [Team/Individual] - -### Context - -We needed to choose an AI framework for our feature sizing application that would provide: -- Robust inference capabilities -- Agent-based processing -- Extensible tool integration -- Production-ready deployment options -- Strong community support - -### Decision - -We chose [Llama Stack](https://llama-stack.readthedocs.io/en/latest/) as our primary AI framework. - -### Consequences - -**Positive:** -- Comprehensive AI platform with inference, agents, and tools -- Strong integration with Meta's Llama models -- REST API interface for easy integration -- Built-in support for RAG, vector databases, and tool execution -- Active development and community support -- Kubernetes deployment capabilities - -**Negative:** -- Dependency on Meta's ecosystem -- Learning curve for team members -- Potential vendor lock-in concerns - -**Neutral:** -- Need to maintain compatibility with Llama Stack updates -- Documentation dependency on external project - ---- - -## ADR-002: Use Python 3.12+ as Primary Language - -**Status**: Accepted -**Date**: 2024-01-XX -**Deciders**: [Team/Individual] - -### Context - -We needed to select a programming language that would: -- Integrate well with AI/ML ecosystems -- Provide good performance for our use case -- Have strong library support for our requirements -- Enable rapid development and iteration - -### Decision - -We chose Python 3.12+ as our primary development language. - -### Consequences - -**Positive:** -- Excellent AI/ML library ecosystem -- Strong Llama Stack Python client support -- Rich tooling for development and testing -- Team familiarity and expertise -- Rapid prototyping capabilities - -**Negative:** -- Performance limitations for CPU-intensive tasks -- Runtime dependency management complexity - -**Neutral:** -- Need to maintain Python version compatibility -- Memory usage considerations for large-scale processing - ---- - -## ADR-003: Adopt uv for Dependency Management - -**Status**: Accepted -**Date**: 2024-01-XX -**Deciders**: [Team/Individual] - -### Context - -We needed a dependency management solution that would: -- Provide fast, reliable dependency resolution -- Support virtual environment management -- Integrate well with modern Python tooling -- Enable reproducible builds - -### Decision - -We chose [uv](https://docs.astral.sh/uv/) for dependency management and virtual environments. - -### Consequences - -**Positive:** -- Extremely fast dependency resolution and installation -- Modern approach to Python packaging -- Excellent integration with pyproject.toml -- Active development and support -- Drop-in replacement for pip in many scenarios - -**Negative:** -- Relatively new tool with smaller community -- Some edge cases may not be well-documented -- Team need to learn new tooling - -**Neutral:** -- Need to maintain compatibility with traditional pip workflows -- Documentation should include both uv and alternative approaches - ---- - -## ADR-004: Modular Processing Stages Architecture - -**Status**: Accepted -**Date**: 2024-01-XX -**Deciders**: [Team/Individual] - -### Context - -We needed an architecture for the feature processing pipeline that would: -- Allow independent development of different processing stages -- Enable easy testing and debugging of individual components -- Support different processing strategies and algorithms -- Facilitate future extensibility - -### Decision - -We implemented a modular processing stages architecture with separate modules for: -- Feature refinement (`refine_feature.py`) -- Estimation (`estimate.py`) -- JIRA draft generation (`draft_jiras.py`) - -### Consequences - -**Positive:** -- Clear separation of concerns -- Independent testing and development -- Easy to add new processing stages -- Simplified debugging and error handling -- Flexible processing pipeline configuration - -**Negative:** -- Additional complexity in orchestration -- Potential for interface mismatches between stages -- Need for careful data flow management - -**Neutral:** -- Requires good documentation of stage interfaces -- Need for integration testing across stages - ---- - -## ADR-005: JIRA Integration via MCP Tools - -**Status**: Accepted -**Date**: 2024-01-XX -**Deciders**: [Team/Individual] - -### Context - -We needed to integrate with JIRA for ticket creation and management. Options considered: -- Direct JIRA API integration -- Third-party JIRA libraries -- Model Context Protocol (MCP) tools -- Webhook-based integration - -### Decision - -We implemented JIRA integration using Model Context Protocol (MCP) tools pattern, compatible with Llama Stack's tool ecosystem. - -### Consequences - -**Positive:** -- Consistent with Llama Stack tool architecture -- Reusable tool pattern for other integrations -- Server-side and client-side execution options -- Built-in support for tool discovery and execution - -**Negative:** -- More complex than direct API integration -- Dependency on MCP protocol specifications -- Additional abstraction layer - -**Neutral:** -- Need to maintain MCP tool interfaces -- Documentation should explain MCP integration patterns - ---- - -## ADR-006: Prompt Template Management - -**Status**: Accepted -**Date**: 2024-01-XX -**Deciders**: [Team/Individual] - -### Context - -We needed a way to manage AI prompts that would: -- Enable consistent AI interactions -- Allow easy prompt iteration and testing -- Support different prompt strategies -- Facilitate collaboration on prompt development - -### Decision - -We implemented a file-based prompt template system in the `prompts/` directory with structured markdown files. - -### Consequences - -**Positive:** -- Version-controlled prompt templates -- Easy to review and iterate on prompts -- Clear separation between code and prompts -- Enables A/B testing of different prompts - -**Negative:** -- Manual synchronization between code and prompt files -- No built-in prompt versioning or rollback -- Potential for prompt/code interface mismatches - -**Neutral:** -- Need for prompt template validation -- Documentation should include prompt development guidelines - ---- - -## ADR-007: Local Development with Ollama - -**Status**: Accepted -**Date**: 2024-01-XX -**Deciders**: [Team/Individual] - -### Context - -We needed a development environment that would: -- Enable local AI model testing -- Reduce dependency on external AI services -- Provide consistent development experience -- Support rapid iteration - -### Decision - -We use [Ollama](https://ollama.com/) for local model serving during development, integrated with Llama Stack's Ollama provider. - -### Consequences - -**Positive:** -- Local development without external dependencies -- Consistent model behavior across team -- No API costs during development -- Privacy for sensitive feature descriptions - -**Negative:** -- Hardware requirements for local model execution -- Potential differences between local and production models -- Setup complexity for new team members - -**Neutral:** -- Need to maintain parity between local and production environments -- Documentation should include hardware recommendations - ---- - -## ADR-008: RESTful API Design - -**Status**: Proposed -**Date**: 2024-01-XX -**Deciders**: [Team/Individual] - -### Context - -We need to design APIs that will: -- Enable integration with external systems -- Support both synchronous and asynchronous operations -- Provide clear, intuitive interfaces -- Scale to handle multiple concurrent requests - -### Decision - -We will implement RESTful APIs following OpenAPI 3.0 specifications, with support for both sync and async operations. - -### Consequences - -**Positive:** -- Industry-standard API patterns -- Good tooling support for documentation and testing -- Clear resource-based operations -- Easy integration with various clients - -**Negative:** -- May not be optimal for all use cases -- Potential complexity for complex operations -- Need for careful API versioning strategy - -**Neutral:** -- Requires comprehensive API documentation -- Need for API testing and validation tools - ---- - -## Decision Status Summary - -| ADR | Title | Status | Impact | -|-----|-------|--------|--------| -| 001 | Llama Stack Adoption | ✅ Accepted | High | -| 002 | Python 3.12+ | ✅ Accepted | High | -| 003 | uv Dependency Management | ✅ Accepted | Medium | -| 004 | Modular Stages Architecture | ✅ Accepted | High | -| 005 | JIRA MCP Integration | ✅ Accepted | Medium | -| 006 | Prompt Template Management | ✅ Accepted | Medium | -| 007 | Local Ollama Development | ✅ Accepted | Medium | -| 008 | RESTful API Design | 🟡 Proposed | High | - ---- - -## Future Decisions - -### Under Consideration - -- **Database Strategy**: Choice of database for storing feature data and history -- **Authentication & Authorization**: Security framework for API access -- **Deployment Strategy**: Container orchestration and cloud deployment -- **Monitoring & Observability**: Logging, metrics, and alerting infrastructure -- **Multi-tenancy**: Support for multiple organizations or teams - -### Decision Process - -1. **Identify Need**: Document the problem requiring a decision -2. **Research Options**: Investigate available alternatives -3. **Stakeholder Input**: Gather feedback from team and users -4. **Document Decision**: Create ADR with rationale and consequences -5. **Implementation**: Update architecture and code accordingly -6. **Review**: Periodic review of decisions and their outcomes - ---- - -*This document is updated as architectural decisions are made. Each decision should be thoroughly discussed and documented with clear rationale and expected consequences.* \ No newline at end of file diff --git a/docs/architecture/overview.md b/docs/architecture/overview.md deleted file mode 100644 index 55a2ab5de..000000000 --- a/docs/architecture/overview.md +++ /dev/null @@ -1,261 +0,0 @@ -# System Architecture Overview - -This document provides a high-level overview of the RHOAI AI Feature Sizing system architecture, built on [Llama Stack](https://llama-stack.readthedocs.io/en/latest/). - -## 🏗️ Architecture Overview - -The RHOAI AI Feature Sizing system is designed as a modular, AI-powered feature estimation platform that leverages Llama Stack for robust AI inference and processing capabilities. - -```mermaid -graph TB - subgraph "Client Layer" - UI[Web UI] - CLI[Command Line Interface] - API_CLIENT[API Client] - end - - subgraph "Application Layer" - MAIN[main.py] - STAGES[Processing Stages] - TOOLS[Integration Tools] - end - - subgraph "Llama Stack Layer" - LS_SERVER[Llama Stack Server] - LS_CLIENT[Llama Stack Client] - INFERENCE[Inference Engine] - AGENTS[AI Agents] - end - - subgraph "Processing Stages" - REFINE[Feature Refinement] - ESTIMATE[Estimation Engine] - DRAFT[JIRA Draft Generation] - end - - subgraph "External Integrations" - JIRA[JIRA Integration] - LLM[Language Models] - PROMPTS[Prompt Templates] - end - - UI --> MAIN - CLI --> MAIN - API_CLIENT --> MAIN - - MAIN --> STAGES - MAIN --> TOOLS - - STAGES --> REFINE - STAGES --> ESTIMATE - STAGES --> DRAFT - - STAGES --> LS_CLIENT - TOOLS --> LS_CLIENT - - LS_CLIENT --> LS_SERVER - LS_SERVER --> INFERENCE - LS_SERVER --> AGENTS - - INFERENCE --> LLM - AGENTS --> PROMPTS - - TOOLS --> JIRA - DRAFT --> JIRA -``` - -## 🧩 Core Components - -### 1. Application Layer - -#### Main Application (`main.py`) -- Entry point for the application -- Orchestrates the feature sizing workflow -- Coordinates between different stages and tools - -#### Processing Stages (`stages/`) -- **Feature Refinement** (`refine_feature.py`) - Analyzes and refines feature specifications -- **Estimation Engine** (`estimate.py`) - Performs AI-powered feature sizing -- **JIRA Draft Generation** (`draft_jiras.py`) - Creates structured JIRA tickets - -#### Integration Tools (`tools/`) -- **JIRA Integration** (`mcp_jira.py`) - Handles JIRA API interactions and ticket management - -### 2. Llama Stack Integration - -The system leverages [Llama Stack](https://llama-stack.readthedocs.io/en/latest/) for: - -- **AI Inference** - Processing natural language feature descriptions -- **Agent-based Processing** - Structured AI workflows for feature analysis -- **Model Management** - Handling different LLM models for specific tasks -- **Tool Integration** - Extensible tool ecosystem for external integrations - -### 3. Prompt Management (`prompts/`) - -Structured prompt templates for consistent AI interactions: -- Feature refinement prompts -- Estimation guidelines -- JIRA ticket formatting templates - -## 🔄 Data Flow - -### Feature Sizing Workflow - -1. **Input Processing** - - User provides feature description via CLI/API - - Raw feature data is validated and preprocessed - -2. **Feature Refinement** - - Llama Stack agents analyze the feature description - - Structured prompts guide the refinement process - - Enhanced feature specification is generated - -3. **Estimation Process** - - AI agents evaluate complexity factors - - Historical data and patterns are considered - - Size estimates with confidence intervals are produced - -4. **Output Generation** - - JIRA tickets are drafted with sizing information - - Integration tools handle external system updates - - Results are returned to the user - -### Llama Stack Integration Flow - -```mermaid -sequenceDiagram - participant User - participant App as Application - participant LSC as Llama Stack Client - participant LSS as Llama Stack Server - participant LLM as Language Model - - User->>App: Submit Feature Description - App->>LSC: Create Agent Session - LSC->>LSS: Initialize AI Agent - LSS->>LLM: Load Model - - App->>LSC: Send Refinement Request - LSC->>LSS: Process with Agent - LSS->>LLM: Generate Refinement - LLM-->>LSS: Refined Feature - LSS-->>LSC: Return Result - LSC-->>App: Refined Feature Data - - App->>LSC: Request Estimation - LSC->>LSS: Estimation Agent - LSS->>LLM: Analyze Complexity - LLM-->>LSS: Size Estimate - LSS-->>LSC: Return Estimate - LSC-->>App: Final Estimate - - App-->>User: Complete Results -``` - -## 🔧 Technology Stack - -### Core Technologies -- **Python 3.12+** - Primary development language -- **Llama Stack** - AI inference and agent framework -- **uv** - Dependency management and virtual environments -- **Fire** - Command-line interface generation - -### AI/ML Components -- **Llama Models** - Various Llama model variants for different tasks -- **Ollama** - Local model serving (development) -- **Vector Databases** - For RAG and context management -- **Embedding Models** - Text representation and similarity - -### External Integrations -- **JIRA API** - Project management integration -- **REST APIs** - Various third-party service integrations - -## 🔒 Security Considerations - -### Authentication & Authorization -- API key management for external services -- Secure credential storage and rotation -- Role-based access control for different features - -### Data Privacy -- Feature descriptions may contain sensitive information -- Local processing with Llama Stack reduces data exposure -- Configurable data retention policies - -### Model Security -- Model validation and verification -- Input sanitization and validation -- Output filtering for sensitive content - -## 📈 Scalability & Performance - -### Horizontal Scaling -- Llama Stack server can be deployed in multiple instances -- Load balancing across inference servers -- Stateless application design for easy scaling - -### Performance Optimization -- Model caching and warm-up strategies -- Batch processing for multiple features -- Asynchronous processing pipelines - -### Resource Management -- GPU allocation for model inference -- Memory management for large contexts -- CPU optimization for data processing - -## 🔍 Monitoring & Observability - -### Logging -- Structured logging throughout the application -- Llama Stack telemetry integration -- Performance metrics and timing data - -### Health Checks -- Application health endpoints -- Llama Stack server status monitoring -- External service connectivity checks - -### Error Handling -- Graceful degradation for service failures -- Retry mechanisms with exponential backoff -- User-friendly error reporting - -## 🚀 Deployment Considerations - -### Development Environment -- Local Llama Stack setup with Ollama -- Development-specific configurations -- Hot-reload capabilities for rapid iteration - -### Production Environment -- Containerized deployment options -- Kubernetes deployment strategies -- Load balancing and auto-scaling - -### Configuration Management -- Environment-specific configurations -- Secret management integration -- Feature flags for gradual rollouts - -## 🔄 Future Architecture Considerations - -### Planned Enhancements -- Multi-model ensemble approaches -- Advanced RAG implementations -- Real-time collaboration features -- Enhanced JIRA integration - -### Extensibility -- Plugin architecture for new estimation methods -- Custom prompt template system -- Additional project management tool integrations - ---- - -For more detailed technical information, see: -- [Design Decisions](./decisions.md) - Key architectural decisions and rationale -- [Development Setup](../development/setup.md) - Setting up the development environment -- [API Documentation](../api/endpoints.md) - API specifications and usage - -*This architecture is designed to be flexible and extensible, allowing for future enhancements while maintaining compatibility with the [Llama Stack ecosystem](https://llama-stack.readthedocs.io/en/latest/).* diff --git a/docs/deployment/configuration.md b/docs/deployment/configuration.md deleted file mode 100644 index 4ff69454d..000000000 --- a/docs/deployment/configuration.md +++ /dev/null @@ -1,1022 +0,0 @@ -# Configuration Guide - -This document covers all configuration options for the RHOAI AI Feature Sizing application and [Llama Stack](https://llama-stack.readthedocs.io/en/latest/) integration. - -## 📋 Configuration Overview - -The application supports multiple configuration methods: -- **Environment Variables** - Primary configuration method -- **Configuration Files** - JSON/YAML configuration files -- **Command Line Arguments** - Runtime parameter overrides -- **Secrets Management** - Secure credential handling - -### Configuration Precedence - -Configuration values are loaded in this order (highest to lowest priority): -1. Command line arguments -2. Environment variables -3. Configuration files -4. Default values - -## 🌐 Environment Variables - -### Core Application Settings - -```bash -# Application Configuration -RHOAI_APP_NAME="RHOAI Feature Sizing" -RHOAI_VERSION="1.0.0" -RHOAI_ENVIRONMENT="production" # development, staging, production -RHOAI_DEBUG="false" -RHOAI_LOG_LEVEL="INFO" # DEBUG, INFO, WARNING, ERROR, CRITICAL -RHOAI_LOG_FILE="/var/log/rhoai/app.log" -RHOAI_LOG_FORMAT="json" # json, text -RHOAI_TIMEZONE="UTC" - -# Server Configuration -RHOAI_HOST="0.0.0.0" -RHOAI_PORT="8000" -RHOAI_WORKERS="4" -RHOAI_MAX_WORKERS="8" -RHOAI_WORKER_TIMEOUT="30" -RHOAI_KEEP_ALIVE="5" -RHOAI_MAX_REQUESTS="1000" -RHOAI_MAX_REQUESTS_JITTER="50" - -# Security Configuration -RHOAI_SECRET_KEY="your-secret-key-here" -RHOAI_JWT_SECRET="your-jwt-secret-here" -RHOAI_JWT_ALGORITHM="HS256" -RHOAI_JWT_EXPIRY="3600" # seconds -RHOAI_CORS_ORIGINS="https://yourapp.com,https://admin.yourapp.com" -RHOAI_CORS_METHODS="GET,POST,PUT,DELETE,OPTIONS" -RHOAI_CORS_HEADERS="*" -RHOAI_ALLOWED_HOSTS="yourapp.com,admin.yourapp.com" -``` - -### Llama Stack Configuration - -```bash -# Llama Stack Server -LLAMA_STACK_BASE_URL="http://localhost:8321" -LLAMA_STACK_API_KEY="" # Optional API key -LLAMA_STACK_TIMEOUT="30" -LLAMA_STACK_MAX_RETRIES="3" -LLAMA_STACK_RETRY_DELAY="1" # seconds -LLAMA_STACK_CLIENT_LOG="INFO" # DEBUG, INFO, WARNING, ERROR -LLAMA_STACK_LOG_FILE="/var/log/rhoai/llama-stack.log" - -# Model Configuration -INFERENCE_MODEL="llama3.1:8b" -EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" -OLLAMA_HOST="http://localhost:11434" -OLLAMA_TIMEOUT="60" -OLLAMA_MAX_PARALLEL_REQUESTS="4" - -# Llama Stack Logging -LLAMA_STACK_LOGGING="server=info;core=info;agents=debug" -# Categories: all, core, server, router, inference, agents, safety, eval, tools, client -# Levels: debug, info, warning, error, critical -``` - -### Database Configuration - -```bash -# Primary Database (Optional) -DATABASE_URL="postgresql://user:password@localhost:5432/rhoai" -DATABASE_POOL_SIZE="5" -DATABASE_MAX_OVERFLOW="10" -DATABASE_POOL_TIMEOUT="30" -DATABASE_POOL_RECYCLE="3600" -DATABASE_ECHO="false" # Log SQL queries - -# Redis Cache (Optional) -REDIS_URL="redis://localhost:6379/0" -REDIS_PASSWORD="" -REDIS_DB="0" -REDIS_TIMEOUT="5" -REDIS_MAX_CONNECTIONS="10" -CACHE_TTL="3600" # seconds -CACHE_KEY_PREFIX="rhoai:" -``` - -### JIRA Integration - -```bash -# JIRA Configuration -JIRA_BASE_URL="https://yourcompany.atlassian.net" -JIRA_USERNAME="your-email@company.com" -JIRA_API_TOKEN="your-api-token" -JIRA_PROJECT_KEY="PROJ" -JIRA_DEFAULT_ISSUE_TYPE="Story" -JIRA_DEFAULT_PRIORITY="Medium" -JIRA_TIMEOUT="30" -JIRA_MAX_RETRIES="3" -JIRA_RETRY_DELAY="2" - -# JIRA Custom Fields -JIRA_STORY_POINTS_FIELD="customfield_10106" -JIRA_CONFIDENCE_FIELD="customfield_10107" -JIRA_COMPLEXITY_FIELD="customfield_10108" -JIRA_AI_ESTIMATED_FIELD="customfield_10109" - -# JIRA Ticket Templates -JIRA_TEMPLATE_DIR="/opt/rhoai/templates/jira" -JIRA_DEFAULT_TEMPLATE="feature_story.md" -``` - -### External API Integration - -```bash -# Search APIs (Optional) -TAVILY_SEARCH_API_KEY="your-tavily-api-key" -BRAVE_SEARCH_API_KEY="your-brave-api-key" -SERPER_API_KEY="your-serper-api-key" - -# Notification Services (Optional) -SLACK_WEBHOOK_URL="https://hooks.slack.com/services/..." -SLACK_CHANNEL="#feature-estimates" -TEAMS_WEBHOOK_URL="https://outlook.office.com/webhook/..." -DISCORD_WEBHOOK_URL="https://discord.com/api/webhooks/..." - -# Email Configuration (Optional) -SMTP_HOST="smtp.gmail.com" -SMTP_PORT="587" -SMTP_USERNAME="your-email@company.com" -SMTP_PASSWORD="your-app-password" -SMTP_USE_TLS="true" -SMTP_FROM_EMAIL="noreply@company.com" -SMTP_FROM_NAME="RHOAI Feature Sizing" -``` - -### Performance and Limits - -```bash -# Rate Limiting -RATE_LIMIT_ENABLED="true" -RATE_LIMIT_REQUESTS_PER_MINUTE="60" -RATE_LIMIT_BURST_SIZE="10" -RATE_LIMIT_STORAGE="redis" # memory, redis -RATE_LIMIT_KEY_FUNC="ip" # ip, user, api_key - -# Request Limits -MAX_REQUEST_SIZE="10MB" -MAX_FEATURE_DESCRIPTION_LENGTH="10000" -MAX_BATCH_SIZE="50" -MAX_CONCURRENT_REQUESTS="10" -REQUEST_TIMEOUT="300" # seconds - -# Feature Processing Limits -MAX_ACCEPTANCE_CRITERIA="20" -MAX_TECHNICAL_REQUIREMENTS="15" -MIN_CONFIDENCE_THRESHOLD="60" -DEFAULT_CONFIDENCE_THRESHOLD="80" -MAX_STORY_POINTS="21" - -# Model Context Limits -MAX_CONTEXT_LENGTH="4096" -MAX_TOKENS_PER_REQUEST="2048" -MODEL_TEMPERATURE="0.7" -MODEL_TOP_P="0.9" -MODEL_TOP_K="40" -``` - -### Monitoring and Observability - -```bash -# Metrics Configuration -METRICS_ENABLED="true" -METRICS_PORT="9090" -METRICS_PATH="/metrics" -PROMETHEUS_NAMESPACE="rhoai" -PROMETHEUS_SUBSYSTEM="feature_sizing" - -# Health Check Configuration -HEALTH_CHECK_ENABLED="true" -HEALTH_CHECK_PATH="/health" -READY_CHECK_PATH="/ready" -HEALTH_CHECK_TIMEOUT="10" - -# Tracing Configuration -TRACING_ENABLED="true" -JAEGER_ENDPOINT="http://localhost:14268/api/traces" -JAEGER_SERVICE_NAME="rhoai-feature-sizing" -TRACING_SAMPLE_RATE="0.1" - -# Log Aggregation -LOG_AGGREGATION_ENABLED="true" -FLUENTD_HOST="localhost" -FLUENTD_PORT="24224" -LOG_SHIPPING_ENABLED="true" -LOG_RETENTION_DAYS="30" -``` - -## 📄 Configuration Files - -### Main Configuration File - -```json -// config/production.json -{ - "app": { - "name": "RHOAI Feature Sizing", - "version": "1.0.0", - "environment": "production", - "debug": false, - "timezone": "UTC" - }, - "server": { - "host": "0.0.0.0", - "port": 8000, - "workers": 4, - "timeout": 30, - "keep_alive": 5 - }, - "llama_stack": { - "base_url": "http://localhost:8321", - "timeout": 30, - "max_retries": 3, - "retry_delay": 1, - "default_model": "llama3.1:8b", - "embedding_model": "sentence-transformers/all-MiniLM-L6-v2" - }, - "estimation": { - "default_confidence_threshold": 80, - "min_confidence_threshold": 60, - "max_story_points": 21, - "complexity_factors": ["technical", "business", "integration"], - "estimation_methods": ["story_points", "hours", "t_shirt_sizes"], - "default_estimation_method": "story_points" - }, - "jira": { - "project_key": "PROJ", - "default_issue_type": "Story", - "default_priority": "Medium", - "timeout": 30, - "max_retries": 3, - "custom_fields": { - "story_points": "customfield_10106", - "confidence_level": "customfield_10107", - "complexity_breakdown": "customfield_10108", - "ai_estimated": "customfield_10109" - }, - "components": ["AI Estimated"], - "labels": ["ai-estimated", "feature-sizing"], - "auto_assign": false, - "create_subtasks": false - }, - "security": { - "cors": { - "origins": ["https://yourapp.com"], - "methods": ["GET", "POST", "PUT", "DELETE", "OPTIONS"], - "headers": ["*"], - "credentials": true - }, - "rate_limiting": { - "enabled": true, - "requests_per_minute": 60, - "burst_size": 10, - "storage": "redis" - }, - "authentication": { - "jwt": { - "algorithm": "HS256", - "expiry": 3600, - "refresh_expiry": 86400 - }, - "api_keys": { - "enabled": true, - "default_expiry": 2592000 - } - } - }, - "monitoring": { - "metrics": { - "enabled": true, - "port": 9090, - "namespace": "rhoai", - "subsystem": "feature_sizing" - }, - "health_checks": { - "enabled": true, - "timeout": 10, - "interval": 30 - }, - "tracing": { - "enabled": true, - "sample_rate": 0.1, - "service_name": "rhoai-feature-sizing" - } - }, - "logging": { - "level": "INFO", - "format": "json", - "file": "/var/log/rhoai/app.log", - "rotation": { - "enabled": true, - "max_size": "100MB", - "backup_count": 5 - }, - "aggregation": { - "enabled": true, - "endpoint": "http://fluentd:24224" - } - }, - "performance": { - "max_request_size": "10MB", - "max_batch_size": 50, - "max_concurrent_requests": 10, - "request_timeout": 300, - "cache": { - "enabled": true, - "ttl": 3600, - "max_size": 1000 - } - } -} -``` - -### Environment-Specific Configurations - -```yaml -# config/development.yaml -app: - environment: development - debug: true - log_level: DEBUG - -server: - host: localhost - port: 8000 - workers: 1 - reload: true - -llama_stack: - base_url: http://localhost:8321 - timeout: 60 - log_level: DEBUG - -security: - cors: - origins: ["http://localhost:3000", "http://localhost:8080"] - authentication: - enabled: false - rate_limiting: - enabled: false - -monitoring: - metrics: - enabled: false - tracing: - enabled: false - ---- -# config/staging.yaml -app: - environment: staging - debug: false - log_level: INFO - -server: - workers: 2 - -security: - cors: - origins: ["https://staging.yourapp.com"] - authentication: - enabled: true - rate_limiting: - enabled: true - requests_per_minute: 120 - -monitoring: - metrics: - enabled: true - tracing: - enabled: true - sample_rate: 0.5 -``` - -### Model Configuration - -```yaml -# config/models.yaml -models: - llama3_1_8b: - model_id: "llama3.1:8b" - provider: "ollama" - capabilities: ["text_generation", "reasoning", "analysis"] - context_length: 4096 - recommended_for: ["production", "detailed_analysis"] - performance: - speed: "medium" - quality: "high" - memory_usage: "medium" - config: - temperature: 0.7 - top_p: 0.9 - top_k: 40 - max_tokens: 2048 - - llama3_2_3b: - model_id: "llama3.2:3b" - provider: "ollama" - capabilities: ["text_generation", "reasoning"] - context_length: 2048 - recommended_for: ["development", "quick_estimates"] - performance: - speed: "fast" - quality: "good" - memory_usage: "low" - config: - temperature: 0.8 - top_p: 0.95 - max_tokens: 1024 - -embedding_models: - all_minilm_l6_v2: - model_id: "sentence-transformers/all-MiniLM-L6-v2" - dimensions: 384 - max_sequence_length: 256 - recommended_for: ["feature_similarity", "search"] - -prompts: - refinement: - template_file: "prompts/refine_feature.md" - max_length: 4000 - include_examples: true - - estimation: - template_file: "prompts/estimate_feature.md" - max_length: 3000 - include_context: true - - jira_formatting: - template_file: "prompts/format_jira.md" - max_length: 2000 -``` - -### Prompt Configuration - -```yaml -# config/prompts.yaml -prompt_templates: - feature_refinement: - file: "prompts/refine_feature.md" - variables: - - feature_description - - project_context - - team_context - max_length: 4000 - temperature: 0.7 - - feature_estimation: - file: "prompts/estimate_feature.md" - variables: - - refined_feature - - estimation_context - - historical_data - max_length: 3000 - temperature: 0.6 - - jira_ticket_creation: - file: "prompts/create_jira_ticket.md" - variables: - - feature_data - - estimation_data - - project_config - max_length: 2000 - temperature: 0.5 - -prompt_engineering: - use_few_shot_examples: true - examples_per_prompt: 3 - context_window_management: true - adaptive_prompting: false - -validation: - required_fields: - - feature_description - - acceptance_criteria - min_description_length: 20 - max_description_length: 10000 - confidence_threshold: 0.6 -``` - -## 🔧 Runtime Configuration - -### Command Line Arguments - -```bash -# Basic usage -python main.py --host 0.0.0.0 --port 8000 --workers 4 - -# Configuration file override -python main.py --config config/production.json - -# Environment override -python main.py --env production - -# Debug mode -python main.py --debug --log-level DEBUG - -# Feature processing -python main.py process-feature \ - --description "Add user authentication" \ - --output results.json \ - --confidence-threshold 80 - -# Batch processing -python main.py process-batch \ - --input features.txt \ - --output-dir results/ \ - --parallel \ - --max-workers 4 - -# JIRA integration -python main.py create-jira-tickets \ - --input estimations.json \ - --project PROJ \ - --issue-type Story \ - --dry-run -``` - -### Configuration Validation - -```python -# config/validation.py -from pydantic import BaseModel, Field, validator -from typing import List, Optional, Dict, Any -from enum import Enum - -class Environment(str, Enum): - DEVELOPMENT = "development" - STAGING = "staging" - PRODUCTION = "production" - -class LogLevel(str, Enum): - DEBUG = "DEBUG" - INFO = "INFO" - WARNING = "WARNING" - ERROR = "ERROR" - CRITICAL = "CRITICAL" - -class AppConfig(BaseModel): - name: str = Field(default="RHOAI Feature Sizing") - version: str = Field(default="1.0.0") - environment: Environment = Field(default=Environment.DEVELOPMENT) - debug: bool = Field(default=False) - log_level: LogLevel = Field(default=LogLevel.INFO) - timezone: str = Field(default="UTC") - -class ServerConfig(BaseModel): - host: str = Field(default="0.0.0.0") - port: int = Field(default=8000, ge=1, le=65535) - workers: int = Field(default=1, ge=1, le=32) - timeout: int = Field(default=30, ge=1) - keep_alive: int = Field(default=5, ge=1) - -class LlamaStackConfig(BaseModel): - base_url: str = Field(default="http://localhost:8321") - api_key: Optional[str] = None - timeout: int = Field(default=30, ge=1) - max_retries: int = Field(default=3, ge=0, le=10) - retry_delay: int = Field(default=1, ge=0) - default_model: str = Field(default="llama3.1:8b") - - @validator('base_url') - def validate_url(cls, v): - if not v.startswith(('http://', 'https://')): - raise ValueError('Base URL must start with http:// or https://') - return v - -class EstimationConfig(BaseModel): - default_confidence_threshold: int = Field(default=80, ge=0, le=100) - min_confidence_threshold: int = Field(default=60, ge=0, le=100) - max_story_points: int = Field(default=21, ge=1) - complexity_factors: List[str] = Field(default=["technical", "business", "integration"]) - estimation_methods: List[str] = Field(default=["story_points", "hours"]) - -class RhoaiConfig(BaseModel): - app: AppConfig = Field(default_factory=AppConfig) - server: ServerConfig = Field(default_factory=ServerConfig) - llama_stack: LlamaStackConfig = Field(default_factory=LlamaStackConfig) - estimation: EstimationConfig = Field(default_factory=EstimationConfig) - - class Config: - env_prefix = "RHOAI_" - case_sensitive = False - -# Usage -def load_config(config_file: Optional[str] = None) -> RhoaiConfig: - """Load and validate configuration.""" - if config_file: - with open(config_file) as f: - config_data = json.load(f) - return RhoaiConfig(**config_data) - else: - return RhoaiConfig() # Load from environment variables -``` - -## 🔒 Security Configuration - -### Authentication Configuration - -```json -{ - "authentication": { - "enabled": true, - "methods": ["api_key", "jwt", "oauth2"], - "default_method": "api_key", - "api_keys": { - "enabled": true, - "prefix": "rfs_key_", - "length": 32, - "default_expiry": 2592000, - "max_expiry": 31536000, - "permissions": { - "read": ["features:read", "models:read", "config:read"], - "write": ["features:write", "features:estimate", "jira:create"], - "admin": ["*"] - } - }, - "jwt": { - "algorithm": "HS256", - "access_token_expiry": 3600, - "refresh_token_expiry": 86400, - "issuer": "rhoai-feature-sizing", - "audience": "rhoai-api" - }, - "oauth2": { - "providers": { - "google": { - "client_id": "${GOOGLE_CLIENT_ID}", - "client_secret": "${GOOGLE_CLIENT_SECRET}", - "scopes": ["openid", "email", "profile"] - }, - "azure": { - "client_id": "${AZURE_CLIENT_ID}", - "client_secret": "${AZURE_CLIENT_SECRET}", - "tenant_id": "${AZURE_TENANT_ID}" - } - } - } - } -} -``` - -### TLS/SSL Configuration - -```json -{ - "tls": { - "enabled": true, - "cert_file": "/etc/ssl/certs/rhoai.crt", - "key_file": "/etc/ssl/private/rhoai.key", - "ca_file": "/etc/ssl/certs/ca.crt", - "protocols": ["TLSv1.2", "TLSv1.3"], - "ciphers": "HIGH:!aNULL:!MD5", - "verify_client": false, - "hsts": { - "enabled": true, - "max_age": 31536000, - "include_subdomains": true, - "preload": true - } - } -} -``` - -## 📊 Monitoring Configuration - -### Metrics Configuration - -```yaml -# config/monitoring.yaml -metrics: - enabled: true - port: 9090 - path: "/metrics" - namespace: "rhoai" - subsystem: "feature_sizing" - - custom_metrics: - - name: "features_processed_total" - type: "counter" - description: "Total number of features processed" - labels: ["method", "status"] - - - name: "estimation_accuracy" - type: "histogram" - description: "Estimation accuracy distribution" - buckets: [0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 1.0] - - - name: "llama_stack_request_duration" - type: "histogram" - description: "Llama Stack request duration" - buckets: [0.1, 0.5, 1.0, 2.0, 5.0, 10.0] - -alerting: - enabled: true - rules_file: "config/alert_rules.yaml" - - notification_channels: - slack: - webhook_url: "${SLACK_WEBHOOK_URL}" - channel: "#alerts" - username: "RHOAI Monitor" - - email: - smtp_host: "${SMTP_HOST}" - smtp_port: 587 - username: "${SMTP_USERNAME}" - password: "${SMTP_PASSWORD}" - from: "alerts@yourcompany.com" - to: ["ops@yourcompany.com"] - -health_checks: - enabled: true - endpoint: "/health" - timeout: 10 - interval: 30 - - checks: - - name: "database" - type: "postgresql" - enabled: true - timeout: 5 - - - name: "llama_stack" - type: "http" - url: "${LLAMA_STACK_BASE_URL}/health" - timeout: 10 - - - name: "redis" - type: "redis" - enabled: true - timeout: 5 - - - name: "disk_space" - type: "disk" - path: "/var/log" - threshold: 85 # percentage -``` - -### Logging Configuration - -```json -{ - "logging": { - "version": 1, - "disable_existing_loggers": false, - "formatters": { - "json": { - "class": "pythonjsonlogger.jsonlogger.JsonFormatter", - "format": "%(asctime)s %(name)s %(levelname)s %(message)s" - }, - "detailed": { - "format": "%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s" - } - }, - "handlers": { - "console": { - "class": "logging.StreamHandler", - "level": "INFO", - "formatter": "json", - "stream": "ext://sys.stdout" - }, - "file": { - "class": "logging.handlers.RotatingFileHandler", - "level": "INFO", - "formatter": "json", - "filename": "/var/log/rhoai/app.log", - "maxBytes": 104857600, - "backupCount": 5 - }, - "error_file": { - "class": "logging.handlers.RotatingFileHandler", - "level": "ERROR", - "formatter": "detailed", - "filename": "/var/log/rhoai/error.log", - "maxBytes": 52428800, - "backupCount": 3 - } - }, - "loggers": { - "rhoai": { - "level": "INFO", - "handlers": ["console", "file", "error_file"], - "propagate": false - }, - "llama_stack_client": { - "level": "WARNING", - "handlers": ["file"], - "propagate": false - }, - "uvicorn": { - "level": "INFO", - "handlers": ["console"], - "propagate": false - } - }, - "root": { - "level": "WARNING", - "handlers": ["console"] - } - } -} -``` - -## 🧪 Testing Configuration - -```json -{ - "testing": { - "environment": "test", - "database_url": "sqlite:///test.db", - "llama_stack": { - "base_url": "http://localhost:8322", - "mock_responses": true, - "timeout": 5 - }, - "jira": { - "base_url": "http://localhost:8080", - "mock_server": true - }, - "fixtures": { - "load_sample_data": true, - "data_directory": "tests/fixtures" - }, - "coverage": { - "minimum_threshold": 80, - "exclude_files": ["tests/*", "migrations/*"] - } - } -} -``` - -## 📋 Configuration Best Practices - -### 1. Environment Separation - -```bash -# Use separate configurations for each environment -config/ -├── base.json # Common settings -├── development.json # Development overrides -├── staging.json # Staging overrides -├── production.json # Production overrides -└── testing.json # Test overrides -``` - -### 2. Secret Management - -```bash -# Never store secrets in configuration files -# Use environment variables or secret management systems - -# Good - Environment variable -export JIRA_API_TOKEN="your-secret-token" - -# Good - Secret management -aws secretsmanager get-secret-value --secret-id rhoai/jira-token - -# Bad - Configuration file -{ - "jira": { - "api_token": "your-secret-token" // Never do this! - } -} -``` - -### 3. Configuration Validation - -```python -# Always validate configuration at startup -def validate_config(): - """Validate critical configuration settings.""" - required_vars = [ - "LLAMA_STACK_BASE_URL", - "RHOAI_SECRET_KEY" - ] - - missing = [var for var in required_vars if not os.getenv(var)] - if missing: - raise ValueError(f"Missing required environment variables: {missing}") - - # Validate URLs - llama_stack_url = os.getenv("LLAMA_STACK_BASE_URL") - if not llama_stack_url.startswith(('http://', 'https://')): - raise ValueError("LLAMA_STACK_BASE_URL must be a valid URL") -``` - -### 4. Configuration Templates - -```bash -# Provide configuration templates for different setups - -# .env.example -RHOAI_ENVIRONMENT=development -RHOAI_DEBUG=true -RHOAI_LOG_LEVEL=DEBUG -LLAMA_STACK_BASE_URL=http://localhost:8321 -JIRA_BASE_URL=https://yourcompany.atlassian.net -JIRA_USERNAME=your-email@company.com -JIRA_API_TOKEN=your-api-token-here - -# docker-compose.override.yml.example -version: '3.8' -services: - app: - environment: - - RHOAI_DEBUG=true - - RHOAI_LOG_LEVEL=DEBUG - volumes: - - ./config/development.json:/app/config/config.json -``` - -## 🔧 Configuration Management Tools - -### 1. Configuration Loading Script - -```python -#!/usr/bin/env python3 -# scripts/load_config.py - -import os -import json -import yaml -from pathlib import Path -from typing import Dict, Any - -def load_config( - config_file: str = None, - environment: str = None -) -> Dict[str, Any]: - """Load configuration from file and environment variables.""" - - # Start with base configuration - config = load_base_config() - - # Load environment-specific config - if environment: - env_config = load_environment_config(environment) - config = merge_configs(config, env_config) - - # Load specific config file - if config_file: - file_config = load_config_file(config_file) - config = merge_configs(config, file_config) - - # Override with environment variables - env_overrides = load_env_overrides() - config = merge_configs(config, env_overrides) - - return config - -def validate_config(config: Dict[str, Any]) -> None: - """Validate configuration settings.""" - # Implement validation logic - pass - -if __name__ == "__main__": - import sys - env = sys.argv[1] if len(sys.argv) > 1 else "development" - config = load_config(environment=env) - validate_config(config) - print(json.dumps(config, indent=2)) -``` - -### 2. Configuration Migration Tool - -```python -#!/usr/bin/env python3 -# scripts/migrate_config.py - -def migrate_config_v1_to_v2(old_config: Dict[str, Any]) -> Dict[str, Any]: - """Migrate configuration from v1 to v2 format.""" - new_config = { - "version": "2.0", - "app": { - "name": old_config.get("app_name", "RHOAI Feature Sizing"), - "version": old_config.get("version", "1.0.0"), - "environment": old_config.get("env", "development") - }, - "server": { - "host": old_config.get("host", "0.0.0.0"), - "port": old_config.get("port", 8000) - } - # ... more migration logic - } - return new_config -``` - ---- - -## 🔗 Related Documentation - -- [Installation Guide](./installation.md) - Deployment instructions -- [API Documentation](../api/endpoints.md) - API configuration options -- [Development Setup](../development/setup.md) - Development configuration - -*For configuration support or questions about specific settings, please refer to the troubleshooting section or contact the development team.* \ No newline at end of file diff --git a/docs/deployment/installation.md b/docs/deployment/installation.md deleted file mode 100644 index 1791dceef..000000000 --- a/docs/deployment/installation.md +++ /dev/null @@ -1,1087 +0,0 @@ -# Production Installation Guide - -This guide covers installing and deploying the RHOAI AI Feature Sizing application in production environments, including [Llama Stack](https://llama-stack.readthedocs.io/en/latest/) infrastructure. - -## 🏗️ Deployment Architecture - -### Overview - -The production deployment consists of: -- **Application Server**: RHOAI AI Feature Sizing application -- **Llama Stack Server**: AI inference and agent processing -- **Model Server**: Ollama or cloud-based model serving -- **Database**: PostgreSQL for persistent data (optional) -- **Load Balancer**: NGINX or cloud load balancer -- **Monitoring**: Prometheus, Grafana, logging stack - -### Infrastructure Requirements - -**Minimum Production Requirements:** -- **CPU**: 4+ cores per service -- **Memory**: 16GB+ RAM (32GB+ recommended for large models) -- **Storage**: 100GB+ SSD for models and data -- **Network**: 1Gbps+ bandwidth -- **GPU**: Optional but recommended for model inference - -**Recommended Production Setup:** -- **CPU**: 8+ cores per service -- **Memory**: 32GB+ RAM -- **Storage**: 500GB+ NVMe SSD -- **Network**: 10Gbps+ bandwidth -- **GPU**: NVIDIA V100, A100, or equivalent - -## 🐳 Container Deployment - -### Docker Deployment - -#### 1. Build Production Images - -```dockerfile -# Dockerfile -FROM python:3.12-slim - -# Install system dependencies -RUN apt-get update && apt-get install -y \ - build-essential \ - curl \ - git \ - && rm -rf /var/lib/apt/lists/* - -# Install uv -RUN curl -LsSf https://astral.sh/uv/install.sh | sh -ENV PATH="/root/.local/bin:$PATH" - -# Set working directory -WORKDIR /app - -# Copy project files -COPY pyproject.toml uv.lock ./ -COPY src/ ./src/ -COPY stages/ ./stages/ -COPY tools/ ./tools/ -COPY prompts/ ./prompts/ - -# Install dependencies -RUN uv sync --frozen --no-dev - -# Create non-root user -RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app -USER appuser - -# Expose port -EXPOSE 8000 - -# Health check -HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ - CMD curl -f http://localhost:8000/health || exit 1 - -# Start application -CMD ["uv", "run", "python", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"] -``` - -#### 2. Build and Push Images - -```bash -# Build application image -docker build -t rhoai-feature-sizing:latest . - -# Tag for registry -docker tag rhoai-feature-sizing:latest your-registry.com/rhoai-feature-sizing:v1.0.0 - -# Push to registry -docker push your-registry.com/rhoai-feature-sizing:v1.0.0 -``` - -#### 3. Docker Compose Production Setup - -```yaml -# docker-compose.prod.yml -version: '3.8' - -services: - app: - image: your-registry.com/rhoai-feature-sizing:v1.0.0 - ports: - - "8000:8000" - environment: - - LLAMA_STACK_BASE_URL=http://llama-stack:8321 - - DATABASE_URL=postgresql://user:password@postgres:5432/rhoai - - JIRA_BASE_URL=${JIRA_BASE_URL} - - JIRA_USERNAME=${JIRA_USERNAME} - - JIRA_API_TOKEN=${JIRA_API_TOKEN} - - LOG_LEVEL=INFO - - ENVIRONMENT=production - depends_on: - - llama-stack - - postgres - restart: unless-stopped - healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:8000/health"] - interval: 30s - timeout: 10s - retries: 3 - deploy: - resources: - limits: - memory: 2G - cpus: '1.0' - reservations: - memory: 1G - cpus: '0.5' - - llama-stack: - image: llamastack/llamastack:latest - ports: - - "8321:8321" - environment: - - INFERENCE_MODEL=llama3.1:8b - - LLAMA_STACK_PORT=8321 - - LLAMA_STACK_LOGGING=server=info;core=info - volumes: - - llama_models:/models - - llama_config:/config - restart: unless-stopped - deploy: - resources: - limits: - memory: 16G - cpus: '4.0' - reservations: - memory: 8G - cpus: '2.0' - - ollama: - image: ollama/ollama:latest - ports: - - "11434:11434" - volumes: - - ollama_data:/root/.ollama - environment: - - OLLAMA_HOST=0.0.0.0 - restart: unless-stopped - deploy: - resources: - limits: - memory: 12G - cpus: '6.0' - - postgres: - image: postgres:15 - environment: - - POSTGRES_DB=rhoai - - POSTGRES_USER=rhoai_user - - POSTGRES_PASSWORD=${POSTGRES_PASSWORD} - volumes: - - postgres_data:/var/lib/postgresql/data - restart: unless-stopped - healthcheck: - test: ["CMD-SHELL", "pg_isready -U rhoai_user -d rhoai"] - interval: 30s - timeout: 10s - retries: 3 - - nginx: - image: nginx:alpine - ports: - - "80:80" - - "443:443" - volumes: - - ./nginx.conf:/etc/nginx/nginx.conf:ro - - ./ssl:/etc/nginx/ssl:ro - depends_on: - - app - restart: unless-stopped - - redis: - image: redis:7-alpine - volumes: - - redis_data:/data - restart: unless-stopped - command: redis-server --appendonly yes - -volumes: - postgres_data: - llama_models: - llama_config: - ollama_data: - redis_data: - -networks: - default: - name: rhoai-network -``` - -## ☸️ Kubernetes Deployment - -### 1. Namespace and ConfigMap - -```yaml -# k8s/namespace.yaml -apiVersion: v1 -kind: Namespace -metadata: - name: rhoai-feature-sizing - labels: - name: rhoai-feature-sizing - ---- -# k8s/configmap.yaml -apiVersion: v1 -kind: ConfigMap -metadata: - name: rhoai-config - namespace: rhoai-feature-sizing -data: - LOG_LEVEL: "INFO" - ENVIRONMENT: "production" - LLAMA_STACK_BASE_URL: "http://llama-stack-service:8321" - LLAMA_STACK_TIMEOUT: "30" - MAX_RETRIES: "3" -``` - -### 2. Secrets Management - -```yaml -# k8s/secrets.yaml -apiVersion: v1 -kind: Secret -metadata: - name: rhoai-secrets - namespace: rhoai-feature-sizing -type: Opaque -data: - # Base64 encoded values - DATABASE_URL: - JIRA_API_TOKEN: - JWT_SECRET: -``` - -### 3. Application Deployment - -```yaml -# k8s/app-deployment.yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - name: rhoai-app - namespace: rhoai-feature-sizing -spec: - replicas: 3 - selector: - matchLabels: - app: rhoai-app - template: - metadata: - labels: - app: rhoai-app - spec: - containers: - - name: rhoai-app - image: your-registry.com/rhoai-feature-sizing:v1.0.0 - ports: - - containerPort: 8000 - env: - - name: LOG_LEVEL - valueFrom: - configMapKeyRef: - name: rhoai-config - key: LOG_LEVEL - - name: DATABASE_URL - valueFrom: - secretKeyRef: - name: rhoai-secrets - key: DATABASE_URL - - name: JIRA_API_TOKEN - valueFrom: - secretKeyRef: - name: rhoai-secrets - key: JIRA_API_TOKEN - resources: - requests: - memory: "1Gi" - cpu: "500m" - limits: - memory: "2Gi" - cpu: "1000m" - livenessProbe: - httpGet: - path: /health - port: 8000 - initialDelaySeconds: 30 - periodSeconds: 10 - readinessProbe: - httpGet: - path: /ready - port: 8000 - initialDelaySeconds: 10 - periodSeconds: 5 - volumeMounts: - - name: app-config - mountPath: /app/config - volumes: - - name: app-config - configMap: - name: rhoai-config - ---- -apiVersion: v1 -kind: Service -metadata: - name: rhoai-app-service - namespace: rhoai-feature-sizing -spec: - selector: - app: rhoai-app - ports: - - protocol: TCP - port: 80 - targetPort: 8000 - type: ClusterIP -``` - -### 4. Llama Stack Deployment - -```yaml -# k8s/llama-stack-deployment.yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - name: llama-stack - namespace: rhoai-feature-sizing -spec: - replicas: 2 - selector: - matchLabels: - app: llama-stack - template: - metadata: - labels: - app: llama-stack - spec: - containers: - - name: llama-stack - image: llamastack/llamastack:latest - ports: - - containerPort: 8321 - env: - - name: INFERENCE_MODEL - value: "llama3.1:8b" - - name: LLAMA_STACK_PORT - value: "8321" - resources: - requests: - memory: "8Gi" - cpu: "2000m" - limits: - memory: "16Gi" - cpu: "4000m" - volumeMounts: - - name: model-storage - mountPath: /models - - name: config-storage - mountPath: /config - volumes: - - name: model-storage - persistentVolumeClaim: - claimName: llama-models-pvc - - name: config-storage - persistentVolumeClaim: - claimName: llama-config-pvc - ---- -apiVersion: v1 -kind: Service -metadata: - name: llama-stack-service - namespace: rhoai-feature-sizing -spec: - selector: - app: llama-stack - ports: - - protocol: TCP - port: 8321 - targetPort: 8321 - type: ClusterIP -``` - -### 5. Persistent Storage - -```yaml -# k8s/storage.yaml -apiVersion: v1 -kind: PersistentVolumeClaim -metadata: - name: llama-models-pvc - namespace: rhoai-feature-sizing -spec: - accessModes: - - ReadWriteOnce - storageClassName: fast-ssd - resources: - requests: - storage: 100Gi - ---- -apiVersion: v1 -kind: PersistentVolumeClaim -metadata: - name: llama-config-pvc - namespace: rhoai-feature-sizing -spec: - accessModes: - - ReadWriteOnce - storageClassName: fast-ssd - resources: - requests: - storage: 10Gi - ---- -apiVersion: v1 -kind: PersistentVolumeClaim -metadata: - name: postgres-data-pvc - namespace: rhoai-feature-sizing -spec: - accessModes: - - ReadWriteOnce - storageClassName: fast-ssd - resources: - requests: - storage: 50Gi -``` - -### 6. Ingress Configuration - -```yaml -# k8s/ingress.yaml -apiVersion: networking.k8s.io/v1 -kind: Ingress -metadata: - name: rhoai-ingress - namespace: rhoai-feature-sizing - annotations: - kubernetes.io/ingress.class: nginx - cert-manager.io/cluster-issuer: letsencrypt-prod - nginx.ingress.kubernetes.io/rate-limit: "100" - nginx.ingress.kubernetes.io/ssl-redirect: "true" -spec: - tls: - - hosts: - - api.yourcompany.com - secretName: rhoai-tls - rules: - - host: api.yourcompany.com - http: - paths: - - path: / - pathType: Prefix - backend: - service: - name: rhoai-app-service - port: - number: 80 -``` - -## 🔧 Manual Installation - -### 1. System Preparation - -```bash -# Update system -sudo apt update && sudo apt upgrade -y - -# Install required packages -sudo apt install -y \ - python3.12 \ - python3.12-venv \ - python3.12-dev \ - build-essential \ - curl \ - git \ - nginx \ - postgresql \ - redis-server \ - supervisor - -# Install uv -curl -LsSf https://astral.sh/uv/install.sh | sh -source ~/.bashrc -``` - -### 2. Application Setup - -```bash -# Create application user -sudo useradd -m -s /bin/bash rhoai -sudo usermod -aG sudo rhoai - -# Switch to application user -sudo su - rhoai - -# Clone and setup application -git clone https://github.com/your-org/rhoai-ai-feature-sizing.git -cd rhoai-ai-feature-sizing - -# Create virtual environment and install dependencies -uv venv /opt/rhoai/venv -source /opt/rhoai/venv/bin/activate -uv sync --frozen --no-dev -uv pip install -e . -``` - -### 3. Database Setup - -```bash -# Setup PostgreSQL -sudo -u postgres createuser rhoai_user -sudo -u postgres createdb rhoai_db -O rhoai_user -sudo -u postgres psql -c "ALTER USER rhoai_user PASSWORD 'secure_password';" - -# Run database migrations (if applicable) -cd /opt/rhoai -source venv/bin/activate -python -m alembic upgrade head -``` - -### 4. Llama Stack Installation - -```bash -# Install Ollama -curl -fsSL https://ollama.com/install.sh | sh - -# Pull required models -ollama pull llama3.1:8b - -# Install and setup Llama Stack -pip install llama-stack - -# Create Llama Stack configuration -mkdir -p /opt/rhoai/llama-stack-config -cat > /opt/rhoai/llama-stack-config/config.yaml << EOF -server: - host: 0.0.0.0 - port: 8321 - cors_origins: ["*"] - -models: - - model_id: llama3.1:8b - provider: ollama - config: - url: http://localhost:11434 - -inference: - provider: ollama - config: - url: http://localhost:11434 - -agents: - provider: meta-reference - config: {} - -safety: - provider: meta-reference - config: {} -EOF -``` - -### 5. Service Configuration - -```bash -# Create systemd service for application -sudo tee /etc/systemd/system/rhoai-app.service << EOF -[Unit] -Description=RHOAI Feature Sizing Application -After=network.target postgresql.service redis.service - -[Service] -Type=exec -User=rhoai -Group=rhoai -WorkingDirectory=/opt/rhoai -Environment=PATH=/opt/rhoai/venv/bin -ExecStart=/opt/rhoai/venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000 -Restart=always -RestartSec=10 - -[Install] -WantedBy=multi-user.target -EOF - -# Create systemd service for Llama Stack -sudo tee /etc/systemd/system/llama-stack.service << EOF -[Unit] -Description=Llama Stack Server -After=network.target ollama.service - -[Service] -Type=exec -User=rhoai -Group=rhoai -WorkingDirectory=/opt/rhoai -Environment=PATH=/opt/rhoai/venv/bin -ExecStart=/opt/rhoai/venv/bin/llama-stack-run --config /opt/rhoai/llama-stack-config/config.yaml -Restart=always -RestartSec=10 - -[Install] -WantedBy=multi-user.target -EOF - -# Enable and start services -sudo systemctl daemon-reload -sudo systemctl enable rhoai-app llama-stack -sudo systemctl start rhoai-app llama-stack -``` - -### 6. NGINX Configuration - -```nginx -# /etc/nginx/sites-available/rhoai -server { - listen 80; - server_name api.yourcompany.com; - return 301 https://$server_name$request_uri; -} - -server { - listen 443 ssl http2; - server_name api.yourcompany.com; - - ssl_certificate /etc/ssl/certs/yourcompany.crt; - ssl_certificate_key /etc/ssl/private/yourcompany.key; - ssl_protocols TLSv1.2 TLSv1.3; - ssl_ciphers HIGH:!aNULL:!MD5; - - # Rate limiting - limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s; - limit_req zone=api burst=20 nodelay; - - # Security headers - add_header X-Frame-Options DENY; - add_header X-Content-Type-Options nosniff; - add_header X-XSS-Protection "1; mode=block"; - add_header Strict-Transport-Security "max-age=31536000; includeSubDomains"; - - location / { - proxy_pass http://127.0.0.1:8000; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - - # Timeouts - proxy_connect_timeout 60s; - proxy_send_timeout 60s; - proxy_read_timeout 60s; - - # WebSocket support - proxy_http_version 1.1; - proxy_set_header Upgrade $http_upgrade; - proxy_set_header Connection "upgrade"; - } - - location /health { - proxy_pass http://127.0.0.1:8000/health; - access_log off; - } -} -``` - -```bash -# Enable site and restart NGINX -sudo ln -s /etc/nginx/sites-available/rhoai /etc/nginx/sites-enabled/ -sudo nginx -t -sudo systemctl restart nginx -``` - -## 🌩️ Cloud Deployment - -### AWS Deployment - -#### 1. ECS with Fargate - -```yaml -# aws/task-definition.json -{ - "family": "rhoai-feature-sizing", - "networkMode": "awsvpc", - "requiresCompatibilities": ["FARGATE"], - "cpu": "2048", - "memory": "4096", - "executionRoleArn": "arn:aws:iam::account:role/ecsTaskExecutionRole", - "taskRoleArn": "arn:aws:iam::account:role/ecsTaskRole", - "containerDefinitions": [ - { - "name": "rhoai-app", - "image": "your-registry.com/rhoai-feature-sizing:v1.0.0", - "portMappings": [ - { - "containerPort": 8000, - "protocol": "tcp" - } - ], - "environment": [ - { - "name": "ENVIRONMENT", - "value": "production" - } - ], - "secrets": [ - { - "name": "DATABASE_URL", - "valueFrom": "arn:aws:secretsmanager:region:account:secret:rhoai/database-url" - } - ], - "logConfiguration": { - "logDriver": "awslogs", - "options": { - "awslogs-group": "/ecs/rhoai-feature-sizing", - "awslogs-region": "us-west-2", - "awslogs-stream-prefix": "ecs" - } - }, - "healthCheck": { - "command": ["CMD-SHELL", "curl -f http://localhost:8000/health || exit 1"], - "interval": 30, - "timeout": 5, - "retries": 3 - } - } - ] -} -``` - -#### 2. Infrastructure as Code (Terraform) - -```hcl -# aws/main.tf -provider "aws" { - region = var.aws_region -} - -# VPC and Networking -resource "aws_vpc" "main" { - cidr_block = "10.0.0.0/16" - enable_dns_hostnames = true - enable_dns_support = true - - tags = { - Name = "rhoai-vpc" - } -} - -resource "aws_subnet" "private" { - count = 2 - vpc_id = aws_vpc.main.id - cidr_block = "10.0.${count.index + 1}.0/24" - availability_zone = data.aws_availability_zones.available.names[count.index] - - tags = { - Name = "rhoai-private-${count.index + 1}" - } -} - -resource "aws_subnet" "public" { - count = 2 - vpc_id = aws_vpc.main.id - cidr_block = "10.0.${count.index + 10}.0/24" - availability_zone = data.aws_availability_zones.available.names[count.index] - map_public_ip_on_launch = true - - tags = { - Name = "rhoai-public-${count.index + 1}" - } -} - -# ECS Cluster -resource "aws_ecs_cluster" "main" { - name = "rhoai-cluster" - - setting { - name = "containerInsights" - value = "enabled" - } - - tags = { - Name = "rhoai-cluster" - } -} - -# Application Load Balancer -resource "aws_lb" "main" { - name = "rhoai-alb" - internal = false - load_balancer_type = "application" - security_groups = [aws_security_group.alb.id] - subnets = aws_subnet.public[*].id - - enable_deletion_protection = false - - tags = { - Name = "rhoai-alb" - } -} - -# RDS Database -resource "aws_db_instance" "main" { - identifier = "rhoai-postgres" - engine = "postgres" - engine_version = "15.3" - instance_class = "db.t3.micro" - - allocated_storage = 20 - max_allocated_storage = 100 - storage_encrypted = true - - db_name = "rhoai" - username = "rhoai_user" - password = var.db_password - - vpc_security_group_ids = [aws_security_group.rds.id] - db_subnet_group_name = aws_db_subnet_group.main.name - - backup_retention_period = 7 - backup_window = "03:00-04:00" - maintenance_window = "sun:04:00-sun:05:00" - - skip_final_snapshot = true - deletion_protection = false - - tags = { - Name = "rhoai-postgres" - } -} -``` - -### Google Cloud Platform - -```yaml -# gcp/deployment.yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - name: rhoai-app -spec: - replicas: 3 - selector: - matchLabels: - app: rhoai-app - template: - metadata: - labels: - app: rhoai-app - spec: - containers: - - name: rhoai-app - image: gcr.io/your-project/rhoai-feature-sizing:v1.0.0 - ports: - - containerPort: 8000 - env: - - name: DATABASE_URL - valueFrom: - secretKeyRef: - name: rhoai-secrets - key: database-url - resources: - requests: - memory: "1Gi" - cpu: "500m" - limits: - memory: "2Gi" - cpu: "1000m" -``` - -## 📊 Monitoring and Observability - -### 1. Prometheus Configuration - -```yaml -# monitoring/prometheus.yml -global: - scrape_interval: 15s - -scrape_configs: - - job_name: 'rhoai-app' - static_configs: - - targets: ['localhost:8000'] - metrics_path: /metrics - scrape_interval: 10s - - - job_name: 'llama-stack' - static_configs: - - targets: ['localhost:8321'] - metrics_path: /metrics - scrape_interval: 15s - - - job_name: 'node-exporter' - static_configs: - - targets: ['localhost:9100'] -``` - -### 2. Grafana Dashboard - -```json -{ - "dashboard": { - "title": "RHOAI Feature Sizing Metrics", - "panels": [ - { - "title": "Request Rate", - "type": "graph", - "targets": [ - { - "expr": "rate(http_requests_total[5m])", - "legendFormat": "{{method}} {{endpoint}}" - } - ] - }, - { - "title": "Response Time", - "type": "graph", - "targets": [ - { - "expr": "histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))", - "legendFormat": "95th percentile" - } - ] - } - ] - } -} -``` - -### 3. Logging Configuration - -```yaml -# logging/fluentd.conf - - @type tail - path /var/log/rhoai/*.log - pos_file /var/log/fluentd/rhoai.log.pos - tag rhoai.* - format json - - - - @type elasticsearch - host elasticsearch.logging.svc.cluster.local - port 9200 - index_name rhoai-logs - type_name _doc - -``` - -## 🔐 Security Configuration - -### 1. SSL/TLS Setup - -```bash -# Generate SSL certificates with Let's Encrypt -sudo apt install certbot python3-certbot-nginx -sudo certbot --nginx -d api.yourcompany.com - -# Auto-renewal -sudo systemctl enable certbot.timer -sudo systemctl start certbot.timer -``` - -### 2. Firewall Configuration - -```bash -# UFW firewall rules -sudo ufw default deny incoming -sudo ufw default allow outgoing -sudo ufw allow ssh -sudo ufw allow 80/tcp -sudo ufw allow 443/tcp -sudo ufw enable - -# Internal services (adjust as needed) -sudo ufw allow from 10.0.0.0/16 to any port 8000 -sudo ufw allow from 10.0.0.0/16 to any port 8321 -``` - -### 3. Secrets Management - -```bash -# Using AWS Secrets Manager -aws secretsmanager create-secret \ - --name "rhoai/database-url" \ - --description "Database connection URL" \ - --secret-string "postgresql://user:password@host:5432/database" - -# Using Kubernetes secrets -kubectl create secret generic rhoai-secrets \ - --from-literal=database-url="postgresql://user:password@host:5432/database" \ - --from-literal=jira-api-token="your-token" -``` - -## 🔄 Backup and Recovery - -### 1. Database Backup - -```bash -# Automated backup script -#!/bin/bash -BACKUP_DIR="/opt/backups" -DATE=$(date +%Y%m%d_%H%M%S) -BACKUP_FILE="$BACKUP_DIR/rhoai_backup_$DATE.sql" - -# Create backup -pg_dump -h localhost -U rhoai_user -d rhoai > "$BACKUP_FILE" - -# Compress backup -gzip "$BACKUP_FILE" - -# Upload to S3 (optional) -aws s3 cp "$BACKUP_FILE.gz" s3://your-backup-bucket/database/ - -# Clean old backups (keep last 7 days) -find "$BACKUP_DIR" -name "rhoai_backup_*.sql.gz" -mtime +7 -delete -``` - -### 2. Application Data Backup - -```bash -# Backup configuration and models -tar -czf /opt/backups/rhoai_config_$(date +%Y%m%d).tar.gz \ - /opt/rhoai/config \ - /opt/rhoai/prompts \ - /opt/rhoai/.env -``` - -## 📋 Deployment Checklist - -### Pre-Deployment -- [ ] Infrastructure provisioned and configured -- [ ] DNS records updated -- [ ] SSL certificates obtained -- [ ] Database initialized -- [ ] Secrets and environment variables configured -- [ ] Monitoring and logging setup - -### Deployment -- [ ] Application images built and pushed -- [ ] Services deployed and running -- [ ] Health checks passing -- [ ] Database migrations applied -- [ ] Load balancer configured -- [ ] Ingress/routing configured - -### Post-Deployment -- [ ] End-to-end testing completed -- [ ] Monitoring dashboards configured -- [ ] Alerting rules setup -- [ ] Backup procedures tested -- [ ] Documentation updated -- [ ] Team notified - ---- - -## 🔗 Related Documentation - -- [Configuration Guide](./configuration.md) - Advanced configuration options -- [Development Setup](../development/setup.md) - Development environment -- [API Documentation](../api/endpoints.md) - API reference - -*For deployment support, please refer to our troubleshooting guide or contact the operations team.* \ No newline at end of file diff --git a/docs/development/setup.md b/docs/development/setup.md deleted file mode 100644 index 2512e9a55..000000000 --- a/docs/development/setup.md +++ /dev/null @@ -1,571 +0,0 @@ -# Development Setup - -This guide will help you set up a development environment for contributing to the RHOAI AI Feature Sizing project, which is built on [Llama Stack](https://llama-stack.readthedocs.io/en/latest/). - -## 🏗️ Development Environment Overview - -The development environment consists of: -- **Python 3.12+** application code -- **Llama Stack** for AI inference -- **Ollama** for local model serving -- **JIRA integration** (optional for development) -- **Testing framework** with pytest -- **Code quality tools** (Black, isort, flake8, mypy) - -## 📋 Prerequisites - -### System Requirements - -- **Operating System**: Linux, macOS, or Windows (with WSL2) -- **Python**: 3.12 or higher -- **Memory**: 8GB+ RAM (16GB+ recommended for larger models) -- **Storage**: 10GB+ free space for models and dependencies -- **Network**: Internet connection for model downloads - -### Required Tools - -1. **Python 3.12+** - ```bash - # Check Python version - python --version - - # Install Python 3.12 if needed (Ubuntu/Debian) - sudo apt update - sudo apt install python3.12 python3.12-venv python3.12-dev - - # macOS with Homebrew - brew install python@3.12 - - # Windows - # Download from https://www.python.org/downloads/ - ``` - -2. **uv Package Manager** - ```bash - # Install uv - curl -LsSf https://astral.sh/uv/install.sh | sh - - # Or with pip - pip install uv - - # Verify installation - uv --version - ``` - -3. **Git** - ```bash - # Ubuntu/Debian - sudo apt install git - - # macOS - brew install git - - # Windows - # Download from https://git-scm.com/download/win - ``` - -4. **Ollama** (for local AI models) - ```bash - # Linux - curl -fsSL https://ollama.com/install.sh | sh - - # macOS - brew install ollama - - # Windows - # Download from https://ollama.com/download - - # Verify installation - ollama --version - ``` - -## 🚀 Quick Setup - -### 1. Clone the Repository - -```bash -# Clone the main repository -git clone https://github.com/your-org/rhoai-ai-feature-sizing.git -cd rhoai-ai-feature-sizing - -# Or clone your fork -git clone https://github.com/your-username/rhoai-ai-feature-sizing.git -cd rhoai-ai-feature-sizing -git remote add upstream https://github.com/your-org/rhoai-ai-feature-sizing.git -``` - -### 2. Set Up Python Environment - -```bash -# Create and activate virtual environment with uv -uv venv -source .venv/bin/activate # On Windows: .venv\Scripts\activate - -# Install dependencies including development tools -uv sync --all-extras - -# Install the project in editable mode -uv pip install -e . -``` - -### 3. Configure Environment Variables - -```bash -# Copy example environment file -cp .env.example .env - -# Edit .env file with your configuration -cat > .env << EOF -# Llama Stack Configuration -LLAMA_STACK_BASE_URL=http://localhost:8321 -LLAMA_STACK_CLIENT_LOG=debug -LLAMA_STACK_PORT=8321 -LLAMA_STACK_CONFIG=ollama - -# Development Settings -RHOAI_LOG_LEVEL=debug -RHOAI_AUTH_DISABLED=true -PYTHONPATH=./src:$PYTHONPATH - -# Optional: JIRA Integration (for testing) -# JIRA_BASE_URL=https://your-dev-instance.atlassian.net -# JIRA_USERNAME=your-email@example.com -# JIRA_API_TOKEN=your-dev-token - -# Optional: External APIs -# TAVILY_SEARCH_API_KEY=your-dev-key -# BRAVE_SEARCH_API_KEY=your-dev-key -EOF -``` - -### 4. Set Up Llama Stack - -```bash -# Pull and run a local model with Ollama -ollama pull llama3.2:3b -ollama run llama3.2:3b --keepalive 60m - -# In a new terminal, start Llama Stack server -INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template ollama --image-type venv --run - -# Verify Llama Stack is running -curl http://localhost:8321/health -``` - -### 5. Run Tests - -```bash -# Run all tests -uv run pytest - -# Run with coverage -uv run pytest --cov=src --cov-report=html - -# Run specific test categories -uv run pytest tests/unit/ -uv run pytest tests/integration/ -``` - -### 6. Verify Setup - -```bash -# Test the main application -python main.py - -# Test feature refinement -python -m stages.refine_feature "Add user authentication system" - -# Test JIRA integration (if configured) -python -m tools.mcp_jira --test-connection -``` - -## 🛠️ Development Tools Setup - -### Code Quality Tools - -The project uses several tools to maintain code quality: - -```bash -# Install pre-commit hooks -uv run pre-commit install - -# Run all pre-commit hooks manually -uv run pre-commit run --all-files - -# Individual tools -uv run black . # Code formatting -uv run isort . # Import sorting -uv run flake8 . # Linting -uv run mypy src/ # Type checking -``` - -### IDE Configuration - -#### Visual Studio Code - -1. **Install recommended extensions** (see `.vscode/extensions.json`): - - Python - - Pylance - - Black Formatter - - isort - - Python Docstring Generator - -2. **Configure settings** (`.vscode/settings.json`): - ```json - { - "python.defaultInterpreterPath": "./.venv/bin/python", - "python.linting.enabled": true, - "python.linting.flake8Enabled": true, - "python.formatting.provider": "black", - "python.sortImports.path": "isort", - "python.testing.pytestEnabled": true, - "python.testing.pytestArgs": ["tests/"], - "files.exclude": { - "**/__pycache__": true, - "**/.pytest_cache": true, - "**/.*_cache": true - } - } - ``` - -#### PyCharm - -1. **Configure interpreter**: - - File → Settings → Project → Python Interpreter - - Add existing virtual environment: `.venv/bin/python` - -2. **Configure code style**: - - File → Settings → Tools → External Tools - - Add Black, isort, flake8 as external tools - -3. **Configure testing**: - - Run → Edit Configurations → Add pytest configuration - - Working directory: project root - - Script path: `tests/` - -### Database Setup (Optional) - -For development with persistent data: - -```bash -# Install database dependencies -uv pip install "psycopg2-binary>=2.9.0" # PostgreSQL -# or -uv pip install "sqlite3" # SQLite (built-in) - -# Set up database URL in .env -echo "DATABASE_URL=sqlite:///dev.db" >> .env -# or -echo "DATABASE_URL=postgresql://user:pass@localhost/rhoai_dev" >> .env - -# Run database migrations (when implemented) -python -m alembic upgrade head -``` - -## 🔧 Development Workflow - -### 1. Feature Development - -```bash -# Create feature branch -git checkout -b feature/your-feature-name - -# Make your changes -# ... code changes ... - -# Run tests and quality checks -uv run pytest -uv run pre-commit run --all-files - -# Commit changes with conventional commits -git add . -git commit -m "feat(estimation): add new complexity analysis algorithm" - -# Push to your fork -git push origin feature/your-feature-name - -# Create pull request -``` - -### 2. Testing Strategy - -```bash -# Unit tests (fast, isolated) -uv run pytest tests/unit/ -v - -# Integration tests (slower, requires services) -uv run pytest tests/integration/ -v - -# End-to-end tests (slowest, full workflow) -uv run pytest tests/e2e/ -v - -# Test specific functionality -uv run pytest tests/unit/test_refine_feature.py -v -uv run pytest -k "test_estimation" -v - -# Test with different Python versions (using tox) -uv run tox -``` - -### 3. Debugging - -```bash -# Enable debug logging -export RHOAI_LOG_LEVEL=debug -export LLAMA_STACK_LOG_FILE=debug.log - -# Run with Python debugger -python -m pdb main.py - -# Debug specific module -python -m pdb -m stages.refine_feature "test feature" - -# Debug tests -uv run pytest --pdb tests/unit/test_refine_feature.py -``` - -### 4. Documentation Development - -```bash -# Install documentation dependencies -uv sync --extra docs - -# Build documentation locally -cd docs/ -uv run make html - -# Serve documentation with auto-reload -uv run sphinx-autobuild source build/html --write-all - -# Open http://127.0.0.1:8000 in browser -``` - -## 🧪 Testing Setup - -### Test Environment Configuration - -```bash -# Create test-specific environment file -cat > .env.test << EOF -LLAMA_STACK_BASE_URL=http://localhost:8322 -RHOAI_AUTH_DISABLED=true -RHOAI_LOG_LEVEL=warning -DATABASE_URL=sqlite:///test.db -PYTEST_CURRENT_TEST=true -EOF - -# Run tests with test environment -uv run --env-file .env.test pytest -``` - -### Test Data Management - -```bash -# Create test data directory -mkdir -p tests/data - -# Generate test fixtures -python tests/fixtures/generate_test_data.py - -# Clean test data -python tests/fixtures/clean_test_data.py -``` - -### Mock Services for Testing - -```bash -# Start mock Llama Stack server for testing -uv run python tests/mocks/mock_llama_stack.py & - -# Start mock JIRA server for integration tests -uv run python tests/mocks/mock_jira_server.py & - -# Stop mock services -pkill -f "mock_llama_stack.py" -pkill -f "mock_jira_server.py" -``` - -## 🔍 Troubleshooting - -### Common Issues - -**1. uv sync fails** -```bash -# Clear cache and retry -uv cache clean -rm -rf .venv/ -uv venv -uv sync --all-extras -``` - -**2. Ollama model download fails** -```bash -# Check Ollama service -ollama list -ollama ps - -# Restart Ollama service -sudo systemctl restart ollama # Linux -brew services restart ollama # macOS - -# Try different model -ollama pull llama3.2:1b # Smaller model -``` - -**3. Llama Stack server won't start** -```bash -# Check port availability -lsof -i :8321 - -# Use different port -LLAMA_STACK_PORT=8322 uv run --with llama-stack llama stack build --template ollama --image-type venv --run - -# Check logs -export LLAMA_STACK_LOG_FILE=server.log -tail -f server.log -``` - -**4. Import errors** -```bash -# Check Python path -python -c "import sys; print(sys.path)" - -# Reinstall in editable mode -uv pip install -e . - -# Check for missing dependencies -uv pip install --upgrade pip -uv sync --all-extras -``` - -**5. Tests failing** -```bash -# Run with verbose output -uv run pytest -v -s - -# Check test environment -uv run pytest --collect-only - -# Run single test with debugging -uv run pytest -v -s tests/unit/test_refine_feature.py::test_basic_refinement -``` - -### Performance Issues - -**1. Slow model inference** -```bash -# Use smaller model for development -ollama pull llama3.2:1b -export INFERENCE_MODEL=llama3.2:1b - -# Enable GPU acceleration (if available) -export CUDA_VISIBLE_DEVICES=0 -``` - -**2. Memory issues** -```bash -# Monitor memory usage -htop -nvidia-smi # For GPU memory - -# Use smaller batch sizes -export BATCH_SIZE=1 -export MAX_CONTEXT_LENGTH=2048 -``` - -### Getting Help - -1. **Check logs**: - ```bash - tail -f ~/.local/share/ollama/logs/server.log - tail -f server.log # Llama Stack logs - ``` - -2. **Verify services**: - ```bash - curl http://localhost:8321/health # Llama Stack - ollama list # Available models - python -c "import llama_stack_client; print('OK')" # Client import - ``` - -3. **Community support**: - - Check GitHub issues - - Join project discussions - - Refer to [Llama Stack documentation](https://llama-stack.readthedocs.io/en/latest/) - -## 🚀 Advanced Development Setup - -### Docker Development Environment - -```bash -# Build development container -docker build -f Dockerfile.dev -t rhoai-dev . - -# Run development container -docker run -it --rm \ - -v $(pwd):/workspace \ - -p 8321:8321 \ - -e OLLAMA_HOST=host.docker.internal \ - rhoai-dev - -# Use docker-compose for full stack -docker-compose -f docker-compose.dev.yml up -``` - -### Kubernetes Development - -```bash -# Install development cluster (kind) -kind create cluster --name rhoai-dev - -# Deploy development stack -kubectl apply -f k8s/dev/ - -# Port forward services -kubectl port-forward svc/llama-stack 8321:8321 -kubectl port-forward svc/ollama 11434:11434 -``` - -### GPU Development Setup - -```bash -# NVIDIA GPU support -nvidia-smi # Verify GPU available - -# Install CUDA-enabled dependencies -uv pip install torch --index-url https://download.pytorch.org/whl/cu118 - -# Configure Ollama for GPU -systemctl edit ollama -# Add: -# [Service] -# Environment="OLLAMA_GPU_ENABLED=true" - -sudo systemctl daemon-reload -sudo systemctl restart ollama -``` - ---- - -## 📚 Next Steps - -Once your development environment is set up: - -1. **Read the codebase**: - - [Architecture Overview](../architecture/overview.md) - - [Coding Standards](./standards.md) - - [Testing Guidelines](./testing.md) - -2. **Start contributing**: - - Check open issues - - Read [Contributing Guide](../../CONTRIBUTING.md) - - Join project discussions - -3. **Explore integrations**: - - [API Documentation](../api/endpoints.md) - - [JIRA Integration](../user-guide/getting-started.md#jira-integration) - - [Llama Stack Components](https://llama-stack.readthedocs.io/en/latest/) - ---- - -*Need help with setup? Check our [FAQ](../user-guide/faq.md) or [open an issue](https://github.com/your-repo/issues) for assistance.* \ No newline at end of file diff --git a/docs/development/standards.md b/docs/development/standards.md deleted file mode 100644 index b97abd2f1..000000000 --- a/docs/development/standards.md +++ /dev/null @@ -1,721 +0,0 @@ -# Coding Standards - -This document outlines the coding standards and best practices for the RHOAI AI Feature Sizing project. - -## 📋 Overview - -Our coding standards ensure: -- **Consistency** across the codebase -- **Readability** for current and future developers -- **Maintainability** as the project grows -- **Quality** through automated tooling - -We follow established Python conventions and extend them with project-specific guidelines. - -## 🐍 Python Style Guide - -### Base Standards - -We follow [PEP 8](https://pep8.org/) with these specific configurations: - -```ini -# .flake8 -[flake8] -max-line-length = 88 -extend-ignore = E203, W503, E501 -exclude = - .git, - __pycache__, - .venv, - build, - dist, - *.egg-info -per-file-ignores = - __init__.py:F401 - tests/*:S101,S106 -``` - -### Code Formatting - -**Black** is our primary code formatter: - -```toml -# pyproject.toml -[tool.black] -line-length = 88 -target-version = ['py312'] -include = '\.pyi?$' -extend-exclude = ''' -/( - # directories - \.eggs - | \.git - | \.venv - | build - | dist -)/ -''' -``` - -**Usage:** -```bash -# Format all files -uv run black . - -# Check formatting without changes -uv run black --check . - -# Format specific file -uv run black src/stages/refine_feature.py -``` - -### Import Organization - -**isort** organizes imports: - -```toml -# pyproject.toml -[tool.isort] -profile = "black" -multi_line_output = 3 -line_length = 88 -known_first_party = ["rhoai_feature_sizing", "stages", "tools"] -known_third_party = ["llama_stack_client", "fire", "requests"] -``` - -**Import Order:** -1. Standard library imports -2. Third-party imports -3. Local application imports - -**Example:** -```python -# Standard library -import os -import sys -from pathlib import Path -from typing import Dict, List, Optional - -# Third-party -import fire -import requests -from llama_stack_client import LlamaStackClient - -# Local application -from stages.refine_feature import refine_feature -from tools.mcp_jira import create_jira_ticket -``` - -### Type Hints - -**mypy** enforces type checking: - -```toml -# pyproject.toml -[tool.mypy] -python_version = "3.12" -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = true -disallow_incomplete_defs = true -check_untyped_defs = true -disallow_untyped_decorators = true -no_implicit_optional = true -warn_redundant_casts = true -warn_unused_ignores = true -warn_no_return = true -warn_unreachable = true -strict_equality = true -``` - -**Type Hint Examples:** -```python -from typing import Dict, List, Optional, Union, Any -from pathlib import Path - -def refine_feature( - client: LlamaStackClient, - description: str, - context: Optional[Dict[str, Any]] = None -) -> Dict[str, Any]: - """Refine a feature description using AI analysis. - - Args: - client: Llama Stack client instance - description: Raw feature description - context: Optional context information - - Returns: - Refined feature data with acceptance criteria - - Raises: - ValueError: If description is empty or invalid - """ - pass - -class FeatureEstimator: - """AI-powered feature estimation.""" - - def __init__(self, model_name: str) -> None: - self.model_name = model_name - - def estimate(self, feature_data: Dict[str, Any]) -> Dict[str, Union[int, float]]: - """Estimate feature complexity and effort.""" - pass -``` - -## 📝 Documentation Standards - -### Docstrings - -We use **Google-style docstrings**: - -```python -def estimate_feature( - feature_data: Dict[str, Any], - context: Optional[Dict[str, Any]] = None -) -> Dict[str, Any]: - """Estimate development effort for a feature. - - This function analyzes feature specifications and provides - estimates for story points, hours, and complexity factors. - - Args: - feature_data: Dictionary containing feature information including: - - title: Feature title - - description: Detailed description - - acceptance_criteria: List of acceptance criteria - context: Optional context containing: - - team_velocity: Team's average velocity - - project_type: Type of project (web_app, mobile_app, etc.) - - Returns: - Dictionary containing estimation results: - - story_points: Estimated story points (1-13 scale) - - confidence: Confidence level (0-100) - - estimated_hours: Hour range estimate - - complexity_factors: Breakdown by complexity type - - Raises: - ValueError: If feature_data is missing required fields - ConnectionError: If Llama Stack service is unavailable - - Example: - >>> feature = { - ... "title": "User Authentication", - ... "description": "Add OAuth2 login system", - ... "acceptance_criteria": ["Users can login with Google"] - ... } - >>> result = estimate_feature(feature) - >>> print(result["story_points"]) - 8 - """ - pass -``` - -### Code Comments - -**When to comment:** -- Complex business logic -- Non-obvious algorithmic decisions -- Workarounds for external library limitations -- TODO items with context - -**Comment style:** -```python -# TODO(username): Implement caching for API responses -# This reduces redundant calls to Llama Stack - -def process_features(features: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - results = [] - - # Process features in batches to avoid overwhelming the AI service - # Batch size is optimized for memory usage vs. processing speed - batch_size = 5 - - for i in range(0, len(features), batch_size): - batch = features[i:i + batch_size] - - # Parallel processing within batch for performance - # Note: Llama Stack has built-in request queuing - batch_results = process_batch_parallel(batch) - results.extend(batch_results) - - return results -``` - -## 🏗️ Code Structure - -### File Organization - -``` -src/ -├── rhoai_feature_sizing/ -│ ├── __init__.py -│ ├── main.py # Application entry point -│ ├── config.py # Configuration management -│ └── exceptions.py # Custom exceptions -├── stages/ -│ ├── __init__.py -│ ├── refine_feature.py # Feature refinement logic -│ ├── estimate.py # Estimation algorithms -│ └── draft_jiras.py # JIRA ticket generation -├── tools/ -│ ├── __init__.py -│ ├── mcp_jira.py # JIRA integration -│ └── utils.py # Utility functions -└── prompts/ - ├── refine_feature.md # Prompt templates - └── estimate_feature.md -``` - -### Class Design - -**Single Responsibility Principle:** -```python -class FeatureRefiner: - """Handles feature description refinement.""" - - def __init__(self, llama_client: LlamaStackClient): - self.client = llama_client - - def refine(self, description: str) -> Dict[str, Any]: - """Refine a single feature description.""" - pass - -class EstimationEngine: - """Handles feature effort estimation.""" - - def __init__(self, llama_client: LlamaStackClient): - self.client = llama_client - - def estimate(self, refined_feature: Dict[str, Any]) -> Dict[str, Any]: - """Estimate effort for a refined feature.""" - pass - -class JiraIntegration: - """Handles JIRA ticket operations.""" - - def __init__(self, base_url: str, credentials: Dict[str, str]): - self.base_url = base_url - self.credentials = credentials - - def create_ticket(self, feature_data: Dict[str, Any]) -> str: - """Create a JIRA ticket from feature data.""" - pass -``` - -### Function Design - -**Small, focused functions:** -```python -def validate_feature_input(feature_data: Dict[str, Any]) -> None: - """Validate feature data has required fields.""" - required_fields = ["title", "description"] - - for field in required_fields: - if field not in feature_data or not feature_data[field].strip(): - raise ValueError(f"Missing or empty required field: {field}") - -def extract_acceptance_criteria(ai_response: str) -> List[str]: - """Extract acceptance criteria from AI response text.""" - # Implementation details... - pass - -def format_jira_description( - feature: Dict[str, Any], - estimation: Dict[str, Any] -) -> str: - """Format feature and estimation data for JIRA description.""" - # Implementation details... - pass -``` - -## 🧪 Testing Standards - -### Test Structure - -```python -import pytest -from unittest.mock import Mock, patch -from llama_stack_client import LlamaStackClient - -from stages.refine_feature import refine_feature -from tools.mcp_jira import create_jira_ticket - -class TestFeatureRefinement: - """Test cases for feature refinement functionality.""" - - def setup_method(self): - """Set up test fixtures before each test method.""" - self.mock_client = Mock(spec=LlamaStackClient) - self.sample_feature = { - "description": "Add user authentication system" - } - - def test_refine_feature_with_valid_input(self): - """Test feature refinement with valid input data.""" - # Arrange - self.mock_client.some_method.return_value = "mocked response" - expected_result = {"title": "User Authentication System"} - - # Act - result = refine_feature(self.mock_client, "Add user auth") - - # Assert - assert result["title"] == expected_result["title"] - self.mock_client.some_method.assert_called_once() - - @pytest.mark.parametrize("invalid_input", [ - "", - " ", - None, - ]) - def test_refine_feature_with_invalid_input(self, invalid_input): - """Test feature refinement rejects invalid input.""" - with pytest.raises(ValueError, match="Feature description cannot be empty"): - refine_feature(self.mock_client, invalid_input) - - @patch('stages.refine_feature.some_external_dependency') - def test_refine_feature_handles_external_errors(self, mock_dependency): - """Test feature refinement handles external service errors.""" - # Arrange - mock_dependency.side_effect = ConnectionError("Service unavailable") - - # Act & Assert - with pytest.raises(ConnectionError): - refine_feature(self.mock_client, "test feature") -``` - -### Test Naming Convention - -- **Test classes**: `TestClassName` -- **Test methods**: `test_method_name_with_expected_behavior` -- **Test files**: `test_module_name.py` - -### Test Categories - -```python -# Unit tests - fast, isolated -@pytest.mark.unit -def test_feature_validation(): - pass - -# Integration tests - test component interactions -@pytest.mark.integration -def test_llama_stack_integration(): - pass - -# End-to-end tests - full workflow -@pytest.mark.e2e -def test_complete_feature_estimation_workflow(): - pass - -# Slow tests - require external services -@pytest.mark.slow -def test_with_real_llama_stack(): - pass -``` - -## 🔧 Configuration Management - -### Environment Configuration - -```python -# config.py -import os -from pathlib import Path -from typing import Dict, Any, Optional -from dataclasses import dataclass - -@dataclass -class LlamaStackConfig: - """Configuration for Llama Stack integration.""" - base_url: str = "http://localhost:8321" - timeout: int = 30 - max_retries: int = 3 - model_name: str = "llama3.2:3b" - -@dataclass -class JiraConfig: - """Configuration for JIRA integration.""" - base_url: Optional[str] = None - username: Optional[str] = None - api_token: Optional[str] = None - project_key: str = "PROJ" - -@dataclass -class AppConfig: - """Main application configuration.""" - log_level: str = "INFO" - debug: bool = False - llama_stack: LlamaStackConfig - jira: JiraConfig - - @classmethod - def from_env(cls) -> "AppConfig": - """Create configuration from environment variables.""" - return cls( - log_level=os.getenv("RHOAI_LOG_LEVEL", "INFO"), - debug=os.getenv("RHOAI_DEBUG", "false").lower() == "true", - llama_stack=LlamaStackConfig( - base_url=os.getenv("LLAMA_STACK_BASE_URL", "http://localhost:8321"), - timeout=int(os.getenv("LLAMA_STACK_TIMEOUT", "30")), - model_name=os.getenv("INFERENCE_MODEL", "llama3.2:3b") - ), - jira=JiraConfig( - base_url=os.getenv("JIRA_BASE_URL"), - username=os.getenv("JIRA_USERNAME"), - api_token=os.getenv("JIRA_API_TOKEN") - ) - ) -``` - -## 🚨 Error Handling - -### Custom Exceptions - -```python -# exceptions.py -class RhoaiError(Exception): - """Base exception for RHOAI feature sizing.""" - pass - -class FeatureRefinementError(RhoaiError): - """Raised when feature refinement fails.""" - pass - -class EstimationError(RhoaiError): - """Raised when feature estimation fails.""" - pass - -class JiraIntegrationError(RhoaiError): - """Raised when JIRA operations fail.""" - pass - -class LlamaStackError(RhoaiError): - """Raised when Llama Stack communication fails.""" - pass -``` - -### Error Handling Patterns - -```python -import logging -from typing import Optional - -logger = logging.getLogger(__name__) - -def safe_api_call(func, *args, **kwargs) -> Optional[Dict[str, Any]]: - """Safely execute API calls with proper error handling.""" - try: - return func(*args, **kwargs) - except ConnectionError as e: - logger.error(f"Connection failed: {e}") - raise LlamaStackError(f"Failed to connect to Llama Stack: {e}") - except TimeoutError as e: - logger.error(f"Request timeout: {e}") - raise LlamaStackError(f"Request timed out: {e}") - except Exception as e: - logger.exception(f"Unexpected error in {func.__name__}") - raise RhoaiError(f"Unexpected error: {e}") - -def refine_feature_with_retry( - client: LlamaStackClient, - description: str, - max_retries: int = 3 -) -> Dict[str, Any]: - """Refine feature with automatic retry on failure.""" - for attempt in range(max_retries): - try: - return refine_feature(client, description) - except LlamaStackError as e: - if attempt == max_retries - 1: - raise - logger.warning(f"Attempt {attempt + 1} failed: {e}. Retrying...") - time.sleep(2 ** attempt) # Exponential backoff - - raise FeatureRefinementError("All retry attempts failed") -``` - -## 📊 Logging - -### Logging Configuration - -```python -# logging_config.py -import logging -import sys -from typing import Dict, Any - -def setup_logging(log_level: str = "INFO", log_file: Optional[str] = None) -> None: - """Configure application logging.""" - - log_format = ( - "%(asctime)s - %(name)s - %(levelname)s - " - "%(filename)s:%(lineno)d - %(message)s" - ) - - handlers = [logging.StreamHandler(sys.stdout)] - - if log_file: - handlers.append(logging.FileHandler(log_file)) - - logging.basicConfig( - level=getattr(logging, log_level.upper()), - format=log_format, - handlers=handlers - ) - - # Suppress verbose third-party logging - logging.getLogger("urllib3").setLevel(logging.WARNING) - logging.getLogger("requests").setLevel(logging.WARNING) - -# Usage in application code -logger = logging.getLogger(__name__) - -def process_feature(feature_data: Dict[str, Any]) -> Dict[str, Any]: - """Process a feature with proper logging.""" - feature_id = feature_data.get("id", "unknown") - - logger.info(f"Starting feature processing for {feature_id}") - - try: - result = perform_processing(feature_data) - logger.info(f"Successfully processed feature {feature_id}") - return result - except Exception as e: - logger.error(f"Failed to process feature {feature_id}: {e}") - raise -``` - -## 🔒 Security Practices - -### Secure Configuration - -```python -import os -from cryptography.fernet import Fernet - -class SecureConfig: - """Handle sensitive configuration securely.""" - - def __init__(self): - self.encryption_key = os.getenv("ENCRYPTION_KEY") - if self.encryption_key: - self.cipher = Fernet(self.encryption_key.encode()) - - def get_secure_value(self, key: str) -> Optional[str]: - """Get encrypted configuration value.""" - encrypted_value = os.getenv(f"ENCRYPTED_{key}") - if not encrypted_value or not self.cipher: - return os.getenv(key) - - try: - return self.cipher.decrypt(encrypted_value.encode()).decode() - except Exception: - logger.warning(f"Failed to decrypt {key}") - return None - -# Never log sensitive information -def safe_log_config(config: Dict[str, Any]) -> None: - """Log configuration with sensitive values masked.""" - safe_config = config.copy() - - sensitive_keys = ["password", "token", "secret", "key"] - - for key, value in safe_config.items(): - if any(sensitive in key.lower() for sensitive in sensitive_keys): - safe_config[key] = "***masked***" - - logger.info(f"Application configuration: {safe_config}") -``` - -## 🚀 Performance Guidelines - -### Efficient Code Patterns - -```python -# Use appropriate data structures -from collections import defaultdict, Counter -from typing import Set, Dict, List - -# Efficient feature grouping -def group_features_by_complexity(features: List[Dict[str, Any]]) -> Dict[str, List[Dict[str, Any]]]: - """Group features by complexity level efficiently.""" - grouped = defaultdict(list) - - for feature in features: - complexity = feature.get("complexity", "medium") - grouped[complexity].append(feature) - - return dict(grouped) - -# Batch processing for performance -async def process_features_batch( - features: List[Dict[str, Any]], - batch_size: int = 5 -) -> List[Dict[str, Any]]: - """Process features in batches for better performance.""" - results = [] - - for i in range(0, len(features), batch_size): - batch = features[i:i + batch_size] - - # Process batch concurrently - tasks = [process_single_feature(feature) for feature in batch] - batch_results = await asyncio.gather(*tasks, return_exceptions=True) - - # Handle any exceptions in batch - for result in batch_results: - if isinstance(result, Exception): - logger.error(f"Batch processing error: {result}") - else: - results.append(result) - - return results -``` - -## 📋 Code Review Checklist - -Before submitting code for review, ensure: - -### Functionality -- [ ] Code works as intended -- [ ] All tests pass -- [ ] Error cases are handled appropriately -- [ ] No obvious performance issues - -### Code Quality -- [ ] Follows coding standards (Black, isort, flake8, mypy) -- [ ] Functions have clear, single responsibilities -- [ ] Variable and function names are descriptive -- [ ] No commented-out code or debugging statements - -### Documentation -- [ ] Public functions have docstrings -- [ ] Complex logic is commented -- [ ] README and docs are updated if needed -- [ ] API changes are documented - -### Testing -- [ ] New functionality has tests -- [ ] Tests cover edge cases -- [ ] Tests are fast and reliable -- [ ] Integration tests pass with real services - -### Security -- [ ] No sensitive data in code or logs -- [ ] Input validation is present -- [ ] Dependencies are up to date -- [ ] No obvious security vulnerabilities - ---- - -## 🔗 Related Documentation - -- [Development Setup](./setup.md) - Setting up your development environment -- [Testing Guidelines](./testing.md) - Comprehensive testing practices -- [Contributing Guide](../../CONTRIBUTING.md) - How to contribute to the project - -*These standards are continuously refined. Please suggest improvements through issues or discussions.* \ No newline at end of file diff --git a/docs/development/testing.md b/docs/development/testing.md deleted file mode 100644 index acdf0c4f5..000000000 --- a/docs/development/testing.md +++ /dev/null @@ -1,925 +0,0 @@ -# Testing Guidelines - -This document outlines the testing strategy, practices, and tools for the RHOAI AI Feature Sizing project, which integrates with [Llama Stack](https://llama-stack.readthedocs.io/en/latest/). - -## 🧪 Testing Philosophy - -Our testing approach follows these principles: - -- **Test-Driven Development (TDD)**: Write tests before implementation when possible -- **Comprehensive Coverage**: Unit, integration, and end-to-end tests -- **Fast Feedback**: Quick test execution for rapid development cycles -- **Reliable Tests**: Deterministic and stable test suite -- **Real-World Scenarios**: Tests that reflect actual usage patterns - -## 📋 Testing Strategy - -### Test Pyramid - -``` - 🔺 End-to-End Tests (Few) - 🔺🔺 Integration Tests (Some) - 🔺🔺🔺 Unit Tests (Many) -``` - -**Unit Tests (70%)** -- Fast execution (< 1 second each) -- Isolated components -- Mock external dependencies -- High code coverage - -**Integration Tests (20%)** -- Component interactions -- Database operations -- External service integrations -- Medium execution time - -**End-to-End Tests (10%)** -- Complete user workflows -- Real service interactions -- Slower execution -- Critical path validation - -## 🛠️ Testing Tools and Framework - -### Core Testing Stack - -```toml -# pyproject.toml - Test dependencies -[project.optional-dependencies] -test = [ - "pytest>=7.0.0", - "pytest-cov>=4.0.0", - "pytest-asyncio>=0.21.0", - "pytest-mock>=3.10.0", - "pytest-xdist>=3.0.0", # Parallel test execution - "pytest-timeout>=2.1.0", # Test timeout handling - "pytest-randomly>=3.12.0", # Random test order - "factory-boy>=3.2.0", # Test data factories - "responses>=0.23.0", # HTTP mocking - "freezegun>=1.2.0", # Time mocking -] -``` - -### Test Configuration - -```ini -# pytest.ini -[tool:pytest] -minversion = 7.0 -addopts = - -ra - --strict-markers - --strict-config - --cov=src - --cov-branch - --cov-report=term-missing - --cov-report=html:htmlcov - --cov-report=xml - --cov-fail-under=80 -testpaths = tests -python_files = test_*.py -python_classes = Test* -python_functions = test_* -markers = - unit: Unit tests (fast, isolated) - integration: Integration tests (medium speed) - e2e: End-to-end tests (slow) - slow: Tests that take longer than 5 seconds - external: Tests requiring external services - llama_stack: Tests requiring Llama Stack - jira: Tests requiring JIRA integration -filterwarnings = - ignore::DeprecationWarning - ignore::PendingDeprecationWarning -``` - -## 📁 Test Structure - -### Directory Organization - -``` -tests/ -├── __init__.py -├── conftest.py # Shared fixtures and configuration -├── unit/ # Unit tests -│ ├── test_refine_feature.py -│ ├── test_estimate.py -│ ├── test_jira_integration.py -│ └── test_utils.py -├── integration/ # Integration tests -│ ├── test_llama_stack_integration.py -│ ├── test_jira_api_integration.py -│ └── test_database_operations.py -├── e2e/ # End-to-end tests -│ ├── test_complete_workflow.py -│ └── test_batch_processing.py -├── fixtures/ # Test data and fixtures -│ ├── sample_features.json -│ ├── mock_responses.json -│ └── test_data_factory.py -├── mocks/ # Mock services -│ ├── mock_llama_stack.py -│ └── mock_jira_server.py -└── utils/ # Test utilities - ├── assertions.py - └── helpers.py -``` - -### Test Fixtures and Configuration - -```python -# tests/conftest.py -import pytest -import asyncio -from unittest.mock import Mock, MagicMock -from pathlib import Path -from typing import Dict, Any - -from llama_stack_client import LlamaStackClient -from stages.refine_feature import FeatureRefiner -from tools.mcp_jira import JiraIntegration - -# Test data directory -TEST_DATA_DIR = Path(__file__).parent / "fixtures" - -@pytest.fixture(scope="session") -def event_loop(): - """Create an instance of the default event loop for the test session.""" - loop = asyncio.get_event_loop_policy().new_event_loop() - yield loop - loop.close() - -@pytest.fixture -def mock_llama_client(): - """Mock Llama Stack client for unit tests.""" - client = Mock(spec=LlamaStackClient) - return client - -@pytest.fixture -def sample_feature_data(): - """Sample feature data for testing.""" - return { - "id": "test-feature-001", - "title": "User Authentication System", - "description": "Implement comprehensive user authentication with OAuth2 support", - "acceptance_criteria": [ - "Users can register with email and password", - "Users can login with Google OAuth2", - "Session management with secure tokens" - ], - "technical_requirements": [ - "OAuth2 client configuration", - "JWT token management", - "Password hashing with bcrypt" - ] - } - -@pytest.fixture -def sample_estimation_result(): - """Sample estimation result for testing.""" - return { - "story_points": 8, - "confidence": 85, - "estimated_hours": {"min": 28, "max": 40, "expected": 32}, - "complexity_factors": { - "technical": "medium", - "business": "low", - "integration": "high" - }, - "risk_factors": [ - { - "factor": "OAuth provider API changes", - "impact": "medium", - "probability": "low" - } - ] - } - -@pytest.fixture -def feature_refiner(mock_llama_client): - """Feature refiner instance for testing.""" - return FeatureRefiner(mock_llama_client) - -@pytest.fixture -def temp_test_db(): - """Temporary test database.""" - # Setup test database - db_path = ":memory:" # SQLite in-memory database - # Initialize database schema - yield db_path - # Cleanup handled automatically with in-memory DB - -@pytest.fixture(scope="session") -def test_config(): - """Test configuration.""" - return { - "llama_stack": { - "base_url": "http://localhost:8322", # Test port - "timeout": 10, - "model_name": "test-model" - }, - "jira": { - "base_url": "http://localhost:8080", # Mock JIRA server - "username": "test@example.com", - "api_token": "test-token" - } - } -``` - -## 🧪 Unit Testing - -### Test Structure and Patterns - -```python -# tests/unit/test_refine_feature.py -import pytest -from unittest.mock import Mock, patch, MagicMock -from stages.refine_feature import refine_feature, FeatureRefiner - -class TestFeatureRefinement: - """Test cases for feature refinement functionality.""" - - def test_refine_feature_with_valid_input(self, mock_llama_client, sample_feature_data): - """Test successful feature refinement with valid input.""" - # Arrange - expected_response = { - "refined_description": "Enhanced feature description...", - "acceptance_criteria": ["Criterion 1", "Criterion 2"] - } - mock_llama_client.inference.completion.return_value = Mock( - content="Mocked AI response" - ) - - # Act - result = refine_feature(mock_llama_client, "Add user authentication") - - # Assert - assert isinstance(result, dict) - assert "refined_description" in result - mock_llama_client.inference.completion.assert_called_once() - - @pytest.mark.parametrize("invalid_input,expected_error", [ - ("", "Feature description cannot be empty"), - (" ", "Feature description cannot be empty"), - (None, "Feature description must be a string"), - ("a" * 10000, "Feature description too long"), - ]) - def test_refine_feature_input_validation( - self, - mock_llama_client, - invalid_input, - expected_error - ): - """Test input validation for feature refinement.""" - with pytest.raises(ValueError, match=expected_error): - refine_feature(mock_llama_client, invalid_input) - - def test_refine_feature_handles_llama_stack_error(self, mock_llama_client): - """Test error handling when Llama Stack fails.""" - # Arrange - mock_llama_client.inference.completion.side_effect = ConnectionError( - "Failed to connect to Llama Stack" - ) - - # Act & Assert - with pytest.raises(ConnectionError): - refine_feature(mock_llama_client, "Test feature") - - @patch('stages.refine_feature.load_prompt_template') - def test_refine_feature_uses_correct_prompt( - self, - mock_load_prompt, - mock_llama_client - ): - """Test that correct prompt template is used.""" - # Arrange - mock_load_prompt.return_value = "Test prompt template" - mock_llama_client.inference.completion.return_value = Mock(content="Response") - - # Act - refine_feature(mock_llama_client, "Test feature") - - # Assert - mock_load_prompt.assert_called_with("refine_feature.md") - -class TestFeatureRefinerClass: - """Test cases for FeatureRefiner class.""" - - def test_refiner_initialization(self, mock_llama_client): - """Test FeatureRefiner initialization.""" - refiner = FeatureRefiner(mock_llama_client) - assert refiner.client == mock_llama_client - - def test_refiner_batch_processing(self, feature_refiner): - """Test batch processing of multiple features.""" - # Arrange - features = ["Feature 1", "Feature 2", "Feature 3"] - feature_refiner.client.inference.completion.return_value = Mock( - content="Mocked response" - ) - - # Act - results = feature_refiner.refine_batch(features) - - # Assert - assert len(results) == len(features) - assert feature_refiner.client.inference.completion.call_count == len(features) -``` - -### Testing Async Code - -```python -# tests/unit/test_async_operations.py -import pytest -import asyncio -from unittest.mock import AsyncMock, Mock - -@pytest.mark.asyncio -async def test_async_feature_processing(): - """Test asynchronous feature processing.""" - # Arrange - mock_client = Mock() - mock_client.inference.completion = AsyncMock( - return_value=Mock(content="Async response") - ) - - # Act - result = await async_refine_feature(mock_client, "Test feature") - - # Assert - assert result is not None - mock_client.inference.completion.assert_called_once() - -@pytest.mark.asyncio -async def test_concurrent_batch_processing(): - """Test concurrent processing of feature batches.""" - # Arrange - features = [f"Feature {i}" for i in range(5)] - mock_client = Mock() - mock_client.inference.completion = AsyncMock( - return_value=Mock(content="Response") - ) - - # Act - results = await process_features_concurrently(mock_client, features) - - # Assert - assert len(results) == len(features) - assert mock_client.inference.completion.call_count == len(features) -``` - -## 🔗 Integration Testing - -### Llama Stack Integration - -```python -# tests/integration/test_llama_stack_integration.py -import pytest -import requests -from llama_stack_client import LlamaStackClient - -@pytest.mark.integration -@pytest.mark.llama_stack -class TestLlamaStackIntegration: - """Integration tests with Llama Stack service.""" - - @pytest.fixture(autouse=True) - def setup_llama_stack(self): - """Ensure Llama Stack is available for testing.""" - try: - response = requests.get("http://localhost:8321/health", timeout=5) - if response.status_code != 200: - pytest.skip("Llama Stack not available") - except requests.RequestException: - pytest.skip("Llama Stack not available") - - def test_llama_stack_connection(self): - """Test connection to Llama Stack service.""" - client = LlamaStackClient(base_url="http://localhost:8321") - - # Test basic connectivity - models = client.models.list() - assert len(models) > 0 - - def test_feature_refinement_integration(self): - """Test actual feature refinement with Llama Stack.""" - client = LlamaStackClient(base_url="http://localhost:8321") - - # Act - result = refine_feature(client, "Add user authentication system") - - # Assert - assert isinstance(result, dict) - assert "refined_description" in result - assert len(result["refined_description"]) > 0 - - @pytest.mark.slow - def test_large_feature_processing(self): - """Test processing of large feature descriptions.""" - client = LlamaStackClient(base_url="http://localhost:8321") - - large_description = "A" * 5000 # Large feature description - - # Act - result = refine_feature(client, large_description) - - # Assert - assert result is not None -``` - -### JIRA Integration Testing - -```python -# tests/integration/test_jira_integration.py -import pytest -import responses -from tools.mcp_jira import JiraIntegration, create_jira_ticket - -@pytest.mark.integration -@pytest.mark.jira -class TestJiraIntegration: - """Integration tests for JIRA API.""" - - @responses.activate - def test_jira_ticket_creation(self, sample_feature_data): - """Test JIRA ticket creation with mocked API.""" - # Arrange - responses.add( - responses.POST, - "http://localhost:8080/rest/api/2/issue", - json={"id": "10001", "key": "PROJ-123"}, - status=201 - ) - - jira = JiraIntegration( - base_url="http://localhost:8080", - username="test@example.com", - api_token="test-token" - ) - - # Act - ticket_id = jira.create_ticket(sample_feature_data) - - # Assert - assert ticket_id == "PROJ-123" - - @responses.activate - def test_jira_authentication_failure(self): - """Test handling of JIRA authentication failure.""" - # Arrange - responses.add( - responses.POST, - "http://localhost:8080/rest/api/2/issue", - status=401 - ) - - jira = JiraIntegration( - base_url="http://localhost:8080", - username="invalid", - api_token="invalid" - ) - - # Act & Assert - with pytest.raises(Exception, match="Authentication failed"): - jira.create_ticket({"title": "Test", "description": "Test"}) -``` - -## 🌐 End-to-End Testing - -### Complete Workflow Testing - -```python -# tests/e2e/test_complete_workflow.py -import pytest -from unittest.mock import patch -from main import main - -@pytest.mark.e2e -@pytest.mark.slow -class TestCompleteWorkflow: - """End-to-end tests for complete feature sizing workflow.""" - - @patch('sys.argv', ['main.py', '--feature', 'Add user authentication']) - def test_complete_feature_sizing_workflow(self, capfd): - """Test complete workflow from description to JIRA ticket.""" - # Act - main() - - # Assert - captured = capfd.readouterr() - assert "Feature refined successfully" in captured.out - assert "Estimation completed" in captured.out - assert "JIRA ticket created" in captured.out - - def test_batch_processing_workflow(self, tmp_path): - """Test batch processing of multiple features.""" - # Arrange - features_file = tmp_path / "features.txt" - features_file.write_text( - "Add user authentication\n" - "Implement payment gateway\n" - "Create admin dashboard\n" - ) - - # Act - with patch('sys.argv', ['main.py', '--batch-file', str(features_file)]): - main() - - # Assert - # Verify all features were processed - # Check output files were created - pass - - @pytest.mark.external - def test_real_llama_stack_workflow(self): - """Test workflow with real Llama Stack instance.""" - # This test requires actual Llama Stack service - pytest.skip("Requires real Llama Stack service") -``` - -## 🏭 Test Data Management - -### Test Data Factories - -```python -# tests/fixtures/test_data_factory.py -import factory -from typing import Dict, Any, List - -class FeatureDataFactory(factory.DictFactory): - """Factory for generating test feature data.""" - - id = factory.Sequence(lambda n: f"feature-{n:03d}") - title = factory.Faker('sentence', nb_words=4) - description = factory.Faker('text', max_nb_chars=500) - - @factory.LazyAttribute - def acceptance_criteria(self): - return [ - factory.Faker('sentence').generate(), - factory.Faker('sentence').generate(), - factory.Faker('sentence').generate(), - ] - - @factory.LazyAttribute - def technical_requirements(self): - return [ - factory.Faker('sentence').generate(), - factory.Faker('sentence').generate(), - ] - -class EstimationResultFactory(factory.DictFactory): - """Factory for generating test estimation results.""" - - story_points = factory.Faker('random_int', min=1, max=13) - confidence = factory.Faker('random_int', min=60, max=100) - - @factory.LazyAttribute - def estimated_hours(self): - base = self.story_points * 4 - return { - "min": base - 8, - "max": base + 8, - "expected": base - } - - @factory.SubFactory - def complexity_factors(self): - return { - "technical": factory.Faker('random_element', - elements=['low', 'medium', 'high']), - "business": factory.Faker('random_element', - elements=['low', 'medium', 'high']), - "integration": factory.Faker('random_element', - elements=['low', 'medium', 'high']) - } - -# Usage in tests -def test_with_generated_data(): - """Test using factory-generated data.""" - feature = FeatureDataFactory() - estimation = EstimationResultFactory(story_points=8) - - assert feature['id'].startswith('feature-') - assert estimation['story_points'] == 8 -``` - -### Mock Services - -```python -# tests/mocks/mock_llama_stack.py -import json -from http.server import HTTPServer, BaseHTTPRequestHandler -import threading -from typing import Dict, Any - -class MockLlamaStackHandler(BaseHTTPRequestHandler): - """Mock HTTP handler for Llama Stack API.""" - - def do_GET(self): - """Handle GET requests.""" - if self.path == '/health': - self.send_response(200) - self.send_header('Content-type', 'application/json') - self.end_headers() - self.wfile.write(json.dumps({"status": "healthy"}).encode()) - elif self.path == '/models': - self.send_response(200) - self.send_header('Content-type', 'application/json') - self.end_headers() - models = [ - { - "id": "test-model", - "name": "Test Model", - "type": "llm" - } - ] - self.wfile.write(json.dumps(models).encode()) - - def do_POST(self): - """Handle POST requests.""" - if self.path == '/inference/completion': - content_length = int(self.headers['Content-Length']) - post_data = self.rfile.read(content_length) - - # Mock AI response - response = { - "content": "This is a mock AI response for testing purposes." - } - - self.send_response(200) - self.send_header('Content-type', 'application/json') - self.end_headers() - self.wfile.write(json.dumps(response).encode()) - -class MockLlamaStackServer: - """Mock Llama Stack server for testing.""" - - def __init__(self, port: int = 8322): - self.port = port - self.server = None - self.thread = None - - def start(self): - """Start the mock server.""" - self.server = HTTPServer(('localhost', self.port), MockLlamaStackHandler) - self.thread = threading.Thread(target=self.server.serve_forever) - self.thread.daemon = True - self.thread.start() - - def stop(self): - """Stop the mock server.""" - if self.server: - self.server.shutdown() - self.server.server_close() - if self.thread: - self.thread.join() - -# Pytest fixture for mock server -@pytest.fixture -def mock_llama_stack_server(): - """Provide a mock Llama Stack server for testing.""" - server = MockLlamaStackServer() - server.start() - yield server - server.stop() -``` - -## 📊 Test Coverage and Quality - -### Coverage Configuration - -```toml -# pyproject.toml -[tool.coverage.run] -source = ["src"] -omit = [ - "*/tests/*", - "*/__pycache__/*", - "*/venv/*", - "*/migrations/*", - "*/settings/*", - "*/conftest.py", -] - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "if self.debug:", - "if settings.DEBUG", - "raise AssertionError", - "raise NotImplementedError", - "if 0:", - "if __name__ == .__main__.:", - "class .*\\bProtocol\\):", - "@(abc\\.)?abstractmethod", -] -show_missing = true -skip_covered = false - -[tool.coverage.html] -directory = "htmlcov" -``` - -### Quality Gates - -```yaml -# .github/workflows/test.yml -name: Test Suite -on: [push, pull_request] - -jobs: - test: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.12, 3.13] - - steps: - - uses: actions/checkout@v3 - - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - - name: Install dependencies - run: | - curl -LsSf https://astral.sh/uv/install.sh | sh - uv sync --all-extras - - - name: Lint code - run: | - uv run flake8 src tests - uv run black --check src tests - uv run isort --check-only src tests - uv run mypy src - - - name: Run tests - run: | - uv run pytest --cov=src --cov-report=xml --cov-fail-under=80 - - - name: Upload coverage to Codecov - uses: codecov/codecov-action@v3 - with: - file: ./coverage.xml -``` - -## 🚀 Performance Testing - -### Load Testing - -```python -# tests/performance/test_load.py -import pytest -import asyncio -import time -from concurrent.futures import ThreadPoolExecutor -from stages.refine_feature import refine_feature - -@pytest.mark.performance -class TestPerformance: - """Performance tests for feature sizing operations.""" - - def test_single_feature_performance(self, mock_llama_client): - """Test performance of single feature refinement.""" - start_time = time.time() - - result = refine_feature(mock_llama_client, "Test feature") - - end_time = time.time() - execution_time = end_time - start_time - - assert result is not None - assert execution_time < 5.0 # Should complete within 5 seconds - - def test_concurrent_processing_performance(self, mock_llama_client): - """Test performance under concurrent load.""" - features = [f"Feature {i}" for i in range(20)] - - start_time = time.time() - - with ThreadPoolExecutor(max_workers=5) as executor: - futures = [ - executor.submit(refine_feature, mock_llama_client, feature) - for feature in features - ] - results = [future.result() for future in futures] - - end_time = time.time() - total_time = end_time - start_time - - assert len(results) == len(features) - assert total_time < 30.0 # Should complete within 30 seconds - - @pytest.mark.slow - def test_memory_usage(self, mock_llama_client): - """Test memory usage with large datasets.""" - import psutil - import os - - process = psutil.Process(os.getpid()) - initial_memory = process.memory_info().rss - - # Process many features - features = [f"Large feature description {i}" * 100 for i in range(100)] - - for feature in features: - refine_feature(mock_llama_client, feature) - - final_memory = process.memory_info().rss - memory_increase = final_memory - initial_memory - - # Memory increase should be reasonable (less than 100MB) - assert memory_increase < 100 * 1024 * 1024 -``` - -## 🔍 Test Debugging and Troubleshooting - -### Debugging Test Failures - -```bash -# Run tests with verbose output -uv run pytest -v -s - -# Run specific test with debugging -uv run pytest -v -s tests/unit/test_refine_feature.py::test_specific_case - -# Run tests with pdb on failure -uv run pytest --pdb - -# Run tests with detailed output -uv run pytest --tb=long - -# Run only failed tests from last run -uv run pytest --lf - -# Run tests in parallel for speed -uv run pytest -n auto -``` - -### Test Environment Issues - -```python -# tests/utils/debugging.py -import os -import sys -import logging - -def debug_test_environment(): - """Print debug information about test environment.""" - print("=== Test Environment Debug Info ===") - print(f"Python version: {sys.version}") - print(f"Working directory: {os.getcwd()}") - print(f"Python path: {sys.path}") - print(f"Environment variables:") - for key, value in os.environ.items(): - if 'TEST' in key or 'PYTEST' in key: - print(f" {key}={value}") - -# Add to conftest.py for debugging -def pytest_configure(config): - """Configure pytest with debugging if needed.""" - if config.getoption("verbose") > 1: - debug_test_environment() -``` - -## 📋 Testing Checklist - -Before submitting code: - -### Unit Tests -- [ ] All new functions have unit tests -- [ ] Tests cover happy path and edge cases -- [ ] Tests use mocks for external dependencies -- [ ] Tests run in under 1 second each -- [ ] Code coverage is above 80% - -### Integration Tests -- [ ] Component interactions are tested -- [ ] External service integrations work -- [ ] Error handling is tested -- [ ] Tests clean up after themselves - -### Test Quality -- [ ] Tests have descriptive names -- [ ] Tests are independent and isolated -- [ ] Test data is realistic but minimal -- [ ] No flaky or intermittent test failures - -### Performance -- [ ] Performance-critical code has benchmarks -- [ ] Memory usage is reasonable -- [ ] No performance regressions - ---- - -## 🔗 Related Documentation - -- [Development Setup](./setup.md) - Setting up your development environment -- [Coding Standards](./standards.md) - Code quality and style guidelines -- [Contributing Guide](../../CONTRIBUTING.md) - How to contribute to the project - -*Testing is a collaborative effort. Please help improve our test suite by adding tests for new features and reporting any issues.* \ No newline at end of file diff --git a/docs/user-guide/faq.md b/docs/user-guide/faq.md deleted file mode 100644 index 9fcf85abe..000000000 --- a/docs/user-guide/faq.md +++ /dev/null @@ -1,439 +0,0 @@ -# Frequently Asked Questions (FAQ) - -This document answers common questions about the RHOAI AI Feature Sizing tool. - -## 🚀 Getting Started - -### Q: What is RHOAI AI Feature Sizing? - -**A:** RHOAI AI Feature Sizing is an AI-powered tool that helps development teams estimate the effort required for software features. It uses [Llama Stack](https://llama-stack.readthedocs.io/en/latest/) to: -- Refine vague feature descriptions into detailed specifications -- Estimate development effort using AI analysis -- Generate structured JIRA tickets for project management -- Provide confidence levels and risk assessments - -### Q: What are the system requirements? - -**A:** To run the tool, you need: -- **Python 3.12+** -- **4GB+ RAM** (8GB+ recommended for better model performance) -- **2GB+ disk space** for models and dependencies -- **Internet connection** for initial setup and model downloads -- **Optional**: GPU for faster AI inference - -### Q: How do I install the tool? - -**A:** Follow these steps: - -```bash -# Clone the repository -git clone -cd rhoai-ai-feature-sizing - -# Install dependencies with uv -uv sync -uv pip install -e . - -# Set up Ollama and Llama Stack -ollama run llama3.2:3b --keepalive 60m -INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template ollama --image-type venv --run -``` - -See the [Getting Started Guide](./getting-started.md) for detailed instructions. - -## 🤖 AI and Models - -### Q: Which AI models does the tool use? - -**A:** The tool supports various Llama models through Ollama: -- **Llama 3.2 3B** - Fast, good for development and testing -- **Llama 3.1 8B** - Balanced performance and quality -- **Llama 3.1 70B** - Highest quality but requires more resources - -You can configure the model using: -```bash -INFERENCE_MODEL=llama3.1:8b ollama run llama3.1:8b -``` - -### Q: Can I use other AI providers besides Ollama? - -**A:** Yes! Llama Stack supports multiple providers: -- **Ollama** - Local model serving (recommended for development) -- **Together AI** - Cloud-hosted models -- **Fireworks AI** - High-performance inference -- **NVIDIA TensorRT-LLM** - Optimized inference -- **Custom providers** - Implement your own - -See the [Llama Stack Providers documentation](https://llama-stack.readthedocs.io/en/latest/references/providers/index.html) for details. - -### Q: How accurate are the estimations? - -**A:** Estimation accuracy depends on several factors: -- **Input quality**: More detailed descriptions lead to better estimates -- **Model size**: Larger models generally provide more accurate estimates -- **Domain context**: The tool learns from patterns in software development -- **Team calibration**: Results improve when calibrated against team velocity - -Typical accuracy ranges: -- ±1-2 story points for well-defined features -- ±2-3 story points for complex or vague features -- 75-85% confidence for most estimates - -### Q: Can the tool learn from our team's historical data? - -**A:** Currently, the tool uses general software development patterns. Future versions will support: -- Team-specific calibration data -- Historical estimation accuracy tracking -- Custom complexity factors -- Project-specific estimation models - -## 🔧 Configuration and Usage - -### Q: How do I configure the tool for my team? - -**A:** Create a `config.json` file: - -```json -{ - "estimation": { - "default_confidence_threshold": 80, - "max_story_points": 13, - "complexity_factors": ["technical", "business", "integration"] - }, - "jira": { - "default_project": "YOUR_PROJECT", - "default_issue_type": "Story", - "auto_assign": false - } -} -``` - -Also set up environment variables in `.env`: -```bash -LLAMA_STACK_BASE_URL=http://localhost:8321 -JIRA_BASE_URL=https://your-domain.atlassian.net -JIRA_USERNAME=your-email@example.com -JIRA_API_TOKEN=your-api-token -``` - -### Q: What input formats are supported? - -**A:** The tool accepts: -- **Plain text descriptions**: "Add user authentication" -- **Structured feature specs**: JSON with acceptance criteria -- **Batch files**: Multiple features in text files -- **Interactive input**: Command-line prompts -- **API calls**: RESTful endpoints for integration - -Example structured input: -```json -{ - "title": "User Authentication", - "description": "Implement OAuth2 login", - "acceptance_criteria": [ - "Users can log in with Google", - "Session management" - ], - "constraints": ["Must integrate with existing user DB"] -} -``` - -### Q: How do I integrate with JIRA? - -**A:** Set up JIRA integration: - -1. **Get API credentials**: - - Go to Atlassian Account Settings - - Create an API token - - Note your JIRA base URL - -2. **Configure environment**: - ```bash - JIRA_BASE_URL=https://your-domain.atlassian.net - JIRA_USERNAME=your-email@example.com - JIRA_API_TOKEN=your-api-token - ``` - -3. **Test connection**: - ```bash - python -m tools.mcp_jira --test-connection - ``` - -4. **Generate tickets**: - ```bash - python -m stages.draft_jiras --feature-file "feature.json" - ``` - -### Q: Can I customize the AI prompts? - -**A:** Yes! Edit the prompt templates in the `prompts/` directory: - -- `refine_feature.md` - Feature refinement prompts -- `estimate_feature.md` - Estimation prompts (when implemented) -- `jira_template.md` - JIRA ticket formatting - -Example prompt customization: -```markdown -# Feature Refinement Prompt - -Please analyze this feature description and provide: -1. Detailed technical requirements -2. Acceptance criteria -3. Risk factors -4. Dependencies - -Feature: {feature_description} - -Focus on these aspects for our team: -- Security considerations -- Performance impact -- Integration complexity -``` - -## 🔍 Troubleshooting - -### Q: The Llama Stack server won't start. What should I do? - -**A:** Try these troubleshooting steps: - -1. **Check Ollama status**: - ```bash - ollama list - ollama ps - ``` - -2. **Verify model availability**: - ```bash - ollama pull llama3.2:3b - ollama run llama3.2:3b - ``` - -3. **Check port availability**: - ```bash - lsof -i :8321 - # Kill conflicting processes if needed - ``` - -4. **Review logs**: - ```bash - export LLAMA_STACK_LOG_FILE=debug.log - export LLAMA_STACK_LOGGING=all=debug - ``` - -### Q: I'm getting import errors. How do I fix them? - -**A:** Common solutions: - -1. **Reinstall dependencies**: - ```bash - uv sync --all-extras - uv pip install -e . - ``` - -2. **Check Python version**: - ```bash - python --version # Should be 3.12+ - ``` - -3. **Verify virtual environment**: - ```bash - source .venv/bin/activate - which python - ``` - -4. **Clear cache**: - ```bash - uv cache clean - pip cache purge - ``` - -### Q: The estimations seem inaccurate. How can I improve them? - -**A:** Tips for better estimations: - -1. **Provide detailed descriptions**: - - Include technical context - - Specify acceptance criteria - - Mention constraints and dependencies - -2. **Use larger models**: - ```bash - INFERENCE_MODEL=llama3.1:8b ollama run llama3.1:8b - ``` - -3. **Calibrate against known work**: - - Compare estimates with completed features - - Adjust complexity factors in configuration - -4. **Iterative refinement**: - - Run refinement multiple times - - Add context from initial estimates - -### Q: JIRA integration isn't working. What's wrong? - -**A:** Common JIRA issues: - -1. **Check credentials**: - ```bash - curl -u username:token https://your-domain.atlassian.net/rest/api/2/myself - ``` - -2. **Verify permissions**: - - API token needs project access - - User must have create issue permissions - -3. **Check project configuration**: - - Verify project key exists - - Confirm issue types are available - -4. **Test with simple API call**: - ```bash - curl -X GET \ - -H "Accept: application/json" \ - -u email:token \ - "https://your-domain.atlassian.net/rest/api/2/project" - ``` - -## 📊 Understanding Results - -### Q: What do the story point estimates mean? - -**A:** Our story point scale follows common agile practices: - -- **1-2 points**: Simple tasks, minimal complexity -- **3-5 points**: Standard features with known patterns -- **8 points**: Complex features requiring research or integration -- **13+ points**: Epics that should be broken down further - -The tool also provides: -- **Confidence percentage**: How certain the estimate is -- **Risk factors**: Potential complications -- **Complexity breakdown**: Technical, business, and integration factors - -### Q: How should I interpret confidence levels? - -**A:** Confidence levels indicate estimation reliability: - -- **90-100%**: High confidence, well-understood features -- **75-89%**: Good confidence, some unknowns exist -- **60-74%**: Medium confidence, significant unknowns -- **Below 60%**: Low confidence, needs more analysis - -**Recommendations by confidence level**: -- High: Proceed with estimate -- Medium: Consider additional research -- Low: Break down feature further or spike first - -### Q: What are complexity factors? - -**A:** The tool analyzes three complexity dimensions: - -1. **Technical Complexity**: - - New technologies or frameworks - - Performance requirements - - Code architecture changes - -2. **Business Complexity**: - - Unclear or changing requirements - - Multiple stakeholder approval - - Complex business logic - -3. **Integration Complexity**: - - External API dependencies - - Cross-team coordination - - Legacy system integration - -Each factor is rated as Low, Medium, or High and influences the final estimate. - -## 🚀 Advanced Usage - -### Q: Can I run the tool in production environments? - -**A:** Yes, with appropriate setup: - -1. **Use production-grade models**: - - Deploy Llama Stack on dedicated servers - - Consider cloud providers for scalability - - Implement load balancing for high availability - -2. **Security considerations**: - - Secure API endpoints with authentication - - Encrypt sensitive feature descriptions - - Implement audit logging - -3. **Performance optimization**: - - Use GPU acceleration - - Configure model caching - - Implement request queuing - -See [Deployment Documentation](../deployment/installation.md) for details. - -### Q: How do I integrate this with my existing tools? - -**A:** Integration options: - -1. **REST API**: Call endpoints programmatically -2. **Python library**: Import and use directly -3. **CLI wrapper**: Shell scripts around command-line interface -4. **Webhooks**: Trigger estimations from other systems -5. **CI/CD integration**: Automated estimation in deployment pipelines - -Example API integration: -```python -import requests - -response = requests.post( - "http://localhost:8000/api/estimate", - json={"feature": "Add user notifications"} -) -estimate = response.json() -``` - -### Q: Can I contribute to the project? - -**A:** Absolutely! We welcome contributions: - -1. **Read the [Contributing Guide](../../CONTRIBUTING.md)** -2. **Check open issues** for areas needing help -3. **Join discussions** for feature planning -4. **Submit pull requests** with improvements - -Common contribution areas: -- New estimation algorithms -- Additional tool integrations -- Documentation improvements -- Bug fixes and optimizations - -## 📞 Getting Help - -### Q: Where can I get more help? - -**A:** Support resources: - -- **📖 Documentation**: [docs/README.md](../README.md) -- **🐛 Bug Reports**: GitHub Issues -- **💡 Feature Requests**: GitHub Discussions -- **🤝 Community**: Project Discord/Slack (if available) -- **📧 Direct Contact**: Maintainer email (if provided) - -### Q: How do I report bugs or request features? - -**A:** Follow this process: - -1. **Search existing issues** to avoid duplicates -2. **For bugs**: Provide reproduction steps and environment details -3. **For features**: Describe the use case and expected behavior -4. **Include logs** and configuration details when relevant - -**Bug report template**: -``` -**Environment**: OS, Python version, model used -**Steps to reproduce**: 1. 2. 3. -**Expected behavior**: What should happen -**Actual behavior**: What actually happens -**Logs**: Relevant error messages -``` - ---- - -*Don't see your question? [Open a discussion](https://github.com/your-repo/discussions) or [submit an issue](https://github.com/your-repo/issues) and we'll help you out!* diff --git a/docs/user-guide/getting-started.md b/docs/user-guide/getting-started.md deleted file mode 100644 index 2ef289b50..000000000 --- a/docs/user-guide/getting-started.md +++ /dev/null @@ -1,344 +0,0 @@ -# Getting Started with RHOAI AI Feature Sizing - -Welcome! This guide will help you get up and running with the RHOAI AI Feature Sizing tool quickly. This tool uses [Llama Stack](https://llama-stack.readthedocs.io/en/latest/) to provide AI-powered feature estimation and JIRA integration. - -## 🚀 Quick Start - -### Prerequisites - -Before you begin, ensure you have: - -- **Python 3.12+** installed -- **Git** for version control -- **uv** package manager ([installation guide](https://docs.astral.sh/uv/getting-started/installation/)) -- **Ollama** for local AI models ([installation guide](https://ollama.com/download)) - -### 1. Installation - -```bash -# Clone the repository -git clone -cd rhoai-ai-feature-sizing - -# Install dependencies -uv sync -uv pip install -e . - -# Activate virtual environment -source .venv/bin/activate -``` - -### 2. Set Up Llama Stack - -Following the [Llama Stack Quickstart](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html#step-1-install-and-setup): - -```bash -# 1. Install uv (if not already installed) -curl -LsSf https://astral.sh/uv/install.sh | sh - -# 2. Run inference on a Llama model with Ollama -ollama run llama3.2:3b --keepalive 60m -``` - -### 3. Start Llama Stack Server - -```bash -# Run the Llama Stack server with Ollama provider -INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template ollama --image-type venv --run -``` - -This will start the Llama Stack server at `http://localhost:8321`. - -### 4. Configure Environment - -Create a `.env` file in the project root: - -```bash -# Llama Stack Configuration -LLAMA_STACK_BASE_URL=http://localhost:8321 -LLAMA_STACK_CLIENT_LOG=debug -LLAMA_STACK_PORT=8321 -LLAMA_STACK_CONFIG=ollama - -# Optional: JIRA Integration -JIRA_BASE_URL=https://your-domain.atlassian.net -JIRA_USERNAME=your-email@example.com -JIRA_API_TOKEN=your-api-token - -# Optional: Additional API Keys -TAVILY_SEARCH_API_KEY=your-key-here -BRAVE_SEARCH_API_KEY=your-key-here -``` - -### 5. Run Your First Feature Estimation - -```bash -# Run the main application -python main.py -``` - -## 🎯 Basic Usage - -### Command Line Interface - -The tool provides a command-line interface for feature sizing: - -```bash -# Basic feature refinement -python -m stages.refine_feature "Add user authentication to the mobile app" - -# Run complete estimation pipeline -python main.py --feature "Implement real-time chat functionality" - -# Generate JIRA drafts -python -m stages.draft_jiras --feature-id "FEAT-001" -``` - -### Python API Usage - -You can also use the tool programmatically: - -```python -from llama_stack_client import LlamaStackClient -from stages.refine_feature import refine_feature -from stages.estimate import estimate_feature - -# Initialize Llama Stack client -client = LlamaStackClient(base_url="http://localhost:8321") - -# Refine a feature description -feature_description = "Add user dashboard with analytics" -refined_feature = refine_feature(client, feature_description) - -# Get size estimate -estimate = estimate_feature(client, refined_feature) - -print(f"Estimated effort: {estimate['story_points']} story points") -print(f"Confidence: {estimate['confidence']}%") -``` - -## 🔧 Configuration - -### Llama Stack Configuration - -The application uses Llama Stack for AI processing. You can configure different aspects: - -```bash -# Use different models -INFERENCE_MODEL=llama3.1:8b uv run --with llama-stack llama stack build --template ollama --image-type venv --run - -# Configure logging levels -export LLAMA_STACK_LOGGING=server=debug;core=info - -# Set log file -export LLAMA_STACK_LOG_FILE=server.log -``` - -### Application Configuration - -Create a `config.json` file for application-specific settings: - -```json -{ - "estimation": { - "default_confidence_threshold": 80, - "max_story_points": 13, - "complexity_factors": ["technical", "business", "integration"] - }, - "jira": { - "default_project": "PROJ", - "default_issue_type": "Story", - "auto_assign": false - }, - "prompts": { - "refinement_template": "prompts/refine_feature.md", - "estimation_template": "prompts/estimate_feature.md" - } -} -``` - -## 📝 Example Workflows - -### Workflow 1: Basic Feature Estimation - -```bash -# Step 1: Start with a rough feature description -FEATURE="Add social login options (Google, Facebook, GitHub) to registration" - -# Step 2: Refine the feature -python -m stages.refine_feature "$FEATURE" - -# Step 3: Get estimation -python -m stages.estimate --refined-feature-file "output/refined_feature.json" - -# Step 4: Generate JIRA tickets -python -m stages.draft_jiras --estimation-file "output/estimation.json" -``` - -### Workflow 2: Batch Processing - -```bash -# Process multiple features from a file -python main.py --batch-file "features.txt" --output-dir "results/" -``` - -Where `features.txt` contains: -``` -Implement user profile management -Add email notification system -Create admin dashboard -Integrate payment gateway -``` - -### Workflow 3: Interactive Mode - -```bash -# Start interactive session -python main.py --interactive - -# Follow prompts to: -# 1. Describe your feature -# 2. Review refined description -# 3. Confirm estimation parameters -# 4. Generate deliverables -``` - -## 🔍 Understanding the Output - -### Feature Refinement Output - -```json -{ - "original_description": "Add social login", - "refined_description": "Implement OAuth2-based social login integration...", - "acceptance_criteria": [ - "Users can log in with Google OAuth2", - "Users can log in with Facebook OAuth2", - "Error handling for failed authentication", - "Redirect to appropriate page after login" - ], - "technical_requirements": [ - "OAuth2 client configuration", - "User account linking logic", - "Security considerations", - "Database schema updates" - ], - "dependencies": [ - "User management system", - "Database migration framework" - ] -} -``` - -### Estimation Output - -```json -{ - "story_points": 8, - "confidence": 85, - "complexity_factors": { - "technical": "medium", - "business": "low", - "integration": "high" - }, - "estimated_hours": 32, - "risk_factors": [ - "Third-party OAuth provider changes", - "User account merging complexity" - ], - "recommendations": [ - "Start with Google OAuth2 implementation", - "Plan for comprehensive testing of edge cases" - ] -} -``` - -## 🛠️ Troubleshooting - -### Common Issues - -**1. Llama Stack Server Not Starting** -```bash -# Check if Ollama is running -ollama list - -# Verify model is available -ollama run llama3.2:3b - -# Check port availability -lsof -i :8321 -``` - -**2. Model Not Found** -```bash -# Pull the required model -ollama pull llama3.2:3b - -# Or use a different model -INFERENCE_MODEL=llama3.1:8b ollama run llama3.1:8b -``` - -**3. Import Errors** -```bash -# Reinstall dependencies -uv sync --all-extras - -# Check Python path -python -c "import sys; print(sys.path)" -``` - -**4. JIRA Integration Issues** -```bash -# Test JIRA connectivity -curl -u username:token https://your-domain.atlassian.net/rest/api/2/myself - -# Verify API token permissions -``` - -### Performance Tips - -1. **Model Performance** - - Use GPU acceleration if available - - Consider smaller models for faster responses - - Cache model responses for repeated queries - -2. **Batch Processing** - - Process multiple features together - - Use async processing for large batches - - Monitor memory usage with large datasets - -3. **Network Optimization** - - Use local Ollama for development - - Configure connection pooling for production - - Implement retry logic for API calls - -## 📚 Next Steps - -Now that you're up and running: - -1. **Explore Advanced Features** - - [API Documentation](../api/endpoints.md) - Integrate with other systems - - [Architecture Overview](../architecture/overview.md) - Understand the system design - -2. **Customize for Your Team** - - [Development Setup](../development/setup.md) - Set up development environment - - [Configuration Guide](../deployment/configuration.md) - Advanced configuration options - -3. **Get Help** - - [FAQ](./faq.md) - Common questions and answers - - [Contributing Guide](../../CONTRIBUTING.md) - Contribute to the project - -## 🎉 Success! - -You're now ready to start using RHOAI AI Feature Sizing for your project estimation needs! The tool will help you: - -- 🔍 **Refine** vague feature descriptions into detailed specifications -- 📊 **Estimate** development effort with AI-powered analysis -- 📋 **Generate** structured JIRA tickets for project management -- 🔄 **Iterate** on estimations as requirements evolve - -For more advanced usage patterns and configuration options, continue reading the rest of our documentation. - ---- - -*Having issues? Check our [FAQ](./faq.md) or [open an issue](https://github.com/your-repo/issues) for help.* \ No newline at end of file diff --git a/llama_stack_setup.py b/llama_stack_setup.py deleted file mode 100644 index f0342f56b..000000000 --- a/llama_stack_setup.py +++ /dev/null @@ -1,23 +0,0 @@ -from llama_stack_client import BadRequestError, LlamaStackClient - -MCP_ATLASSIAN_URL = "http://localhost:9000/sse" -LLAMA_STACK_URL = "http://localhost:8321" - - -def get_llama_stack_client(): - client = LlamaStackClient(base_url=LLAMA_STACK_URL) - try: - tg = client.toolgroups.get(toolgroup_id="mcp::atlassian") - if tg: - print("Unregistering toolgroup mcp::atlassian") - client.toolgroups.unregister(toolgroup_id="mcp::atlassian") - except BadRequestError as e: - print("Toolgroup mcp::atlassian not found, skipping unregister.") - # Register the MCP Atlassian tool group only once - client.toolgroups.register( - toolgroup_id="mcp::atlassian", - provider_id="model-context-protocol", - mcp_endpoint={"uri": MCP_ATLASSIAN_URL}, - ) - print("Toolgroup mcp::atlassian registered") - return client diff --git a/prompts/refine_feature.md b/prompts/refine_feature.md deleted file mode 100644 index 53d3e0c3a..000000000 --- a/prompts/refine_feature.md +++ /dev/null @@ -1,13 +0,0 @@ -You are a senior engineer refining the following *Jira Feature* into an engineering-ready spec. - -## Feature (verbatim) -{{feature_text}} - -## Acceptance criteria -- Bullet the user-visible outcomes… - -## Non-goals -... - -## Open questions -... \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 6eb6605c0..97130e662 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,11 +1,26 @@ [project] name = "rhoai-ai-feature-sizing" version = "0.1.0" -description = "Add your description here" +description = "AI-powered feature sizing for Jira using LLMs and MCP." readme = "README.md" requires-python = ">=3.12" dependencies = [ "fire>=0.7.0", + "llama-stack>=0.2.10", "llama-stack-client>=0.2.10", "requests>=2.32.3", + "fastapi>=0.104.0", + "uvicorn>=0.24.0", + "python-multipart>=0.0.6", ] + +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[tool.setuptools] +package-dir = {"" = "src"} + +[tool.setuptools.packages.find] +where = ["src"] +include = ["rhoai_ai_feature_sizing*"] diff --git a/src/rhoai_ai_feature_sizing/llama_stack_setup.py b/src/rhoai_ai_feature_sizing/llama_stack_setup.py new file mode 100644 index 000000000..a4d17f144 --- /dev/null +++ b/src/rhoai_ai_feature_sizing/llama_stack_setup.py @@ -0,0 +1,37 @@ +import os +from llama_stack_client import LlamaStackClient + +LLAMA_STACK_URL = os.getenv("LLAMA_STACK_URL", "http://localhost:8321") +MCP_ATLASSIAN_URL = os.getenv("MCP_ATLASSIAN_URL", "http://localhost:9000/sse") + + +def get_llama_stack_client(): + """ + Get a client connected to the Llama Stack server. + + This connects to a running Llama Stack server that handles all provider + and toolgroup configuration via run.yaml. The setup is K8s-ready. + """ + print(f"Creating client for {LLAMA_STACK_URL}") + client = LlamaStackClient(base_url=LLAMA_STACK_URL) + try: + # First, try to unregister any existing toolgroup to clear old config + try: + client.toolgroups.unregister(toolgroup_id="mcp::atlassian") + print("Unregistered existing mcp::atlassian toolgroup") + except Exception as unregister_err: + print( + f"No existing toolgroup to unregister (this is fine): {unregister_err}" + ) + + # Register the MCP Atlassian tool group with correct endpoint + client.toolgroups.register( + toolgroup_id="mcp::atlassian", + provider_id="model-context-protocol", + mcp_endpoint={"uri": "http://jira-mcp:9000/sse"}, + ) + print("Toolgroup mcp::atlassian registered with correct endpoint") + + except Exception as e: + print(f"Error registering toolgroup mcp::atlassian: {e}") + return client diff --git a/main.py b/src/rhoai_ai_feature_sizing/main.py similarity index 72% rename from main.py rename to src/rhoai_ai_feature_sizing/main.py index ba06ea07c..fd7ab4c8b 100644 --- a/main.py +++ b/src/rhoai_ai_feature_sizing/main.py @@ -3,25 +3,68 @@ import logging import sys from pathlib import Path +import time # Import stage functions -from stages.refine_feature import fetch_jira_issue_with_agent, fill_template +from rhoai_ai_feature_sizing.stages.refine_feature import ( + generate_refinement_with_agent, +) # Setup logging def setup_logging(debug=False): - level = logging.DEBUG if debug else logging.WARNING - logging.basicConfig( - level=level, - format="%(levelname)s: %(message)s", - handlers=[logging.StreamHandler(sys.stderr)], + """Configure comprehensive logging following Llama Stack best practices.""" + level = logging.DEBUG if debug else logging.INFO + + # Create formatter with more detailed information + formatter = logging.Formatter( + "%(asctime)s - %(name)s - %(levelname)s - %(funcName)s:%(lineno)d - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + # Configure root logger + root_logger = logging.getLogger() + root_logger.setLevel(level) + + # Clear any existing handlers + for handler in root_logger.handlers[:]: + root_logger.removeHandler(handler) + + # Console handler + console_handler = logging.StreamHandler(sys.stderr) + console_handler.setLevel(level) + console_handler.setFormatter(formatter) + root_logger.addHandler(console_handler) + + # File handler for detailed logs + if debug: + file_handler = logging.FileHandler("rhoai_feature_sizing.log") + file_handler.setLevel(logging.DEBUG) + file_handler.setFormatter(formatter) + root_logger.addHandler(file_handler) + + # Set specific logger levels + logging.getLogger("llama_stack_client").setLevel( + logging.INFO if debug else logging.WARNING ) + logging.getLogger("httpx").setLevel(logging.WARNING) + + if debug: + logging.info( + "Debug logging enabled - detailed logs written to rhoai_feature_sizing.log" + ) # Stage implementations def run_refine_stage(issue_key: str, output_dir: Path, debug: bool = False): - """Run the refine feature stage.""" + """Run the refine feature stage with enhanced error handling and monitoring.""" logger = logging.getLogger("refine_feature") + start_time = time.time() + + # Validate inputs + if not issue_key or not issue_key.strip(): + logger.error("Issue key cannot be empty") + sys.exit(1) template_path = Path(__file__).parent / "prompts" / "refine_feature.md" @@ -30,20 +73,35 @@ def run_refine_stage(issue_key: str, output_dir: Path, debug: bool = False): sys.exit(1) logger.info(f"Reading template from {template_path}") - with open(template_path, "r") as f: - template = f.read() - - logger.info(f"Fetching Jira issue {issue_key}") - issue = fetch_jira_issue_with_agent(issue_key) + try: + with open(template_path, "r", encoding="utf-8") as f: + template = f.read() + except Exception as e: + logger.error(f"Failed to read template: {e}") + sys.exit(1) - filled = fill_template(template, issue) + logger.info(f"Generating refinement document for Jira issue {issue_key}") + try: + refinement_content = generate_refinement_with_agent(issue_key, template) + except Exception as e: + logger.error(f"Failed to generate refinement document: {e}") + if debug: + logger.exception("Full traceback:") + sys.exit(1) output_path = output_dir / f"refined_{issue_key}.md" logger.info(f"Writing refinement spec to {output_path}") - with open(output_path, "w") as f: - f.write(filled) + try: + with open(output_path, "w", encoding="utf-8") as f: + f.write(refinement_content) + except Exception as e: + logger.error(f"Failed to write output file: {e}") + sys.exit(1) + # Log performance metrics + duration = time.time() - start_time + logger.info(f"Refinement stage completed in {duration:.2f}s") print(f"✓ Refinement spec written to {output_path}") return output_path diff --git a/src/rhoai_ai_feature_sizing/prompts/refine_feature.md b/src/rhoai_ai_feature_sizing/prompts/refine_feature.md new file mode 100644 index 000000000..a5ca97645 --- /dev/null +++ b/src/rhoai_ai_feature_sizing/prompts/refine_feature.md @@ -0,0 +1,260 @@ +# Task: Create RHOAI Feature Refinement Document + +## Objective +Fetch Jira issue {{issue_key}} and create a comprehensive RHOAI feature refinement document following the provided template and examples. + +## Step-by-Step Process +1. **Fetch Complete Issue Details**: Use the Jira tools to get comprehensive information about {{issue_key}}: + - Summary and description + - Components and labels + - Priority and status + - Reporter and assignee information + - Any linked issues or epics + - Comments and attachments (if relevant) + +2. **Analyze and Transform**: Convert the Jira content into a structured refinement document + +3. **Generate Complete Document**: Create the full refinement document using the template structure below + +## Examples + +Here are examples of well-written RHOAI refinement documents: + +### Example 1: Data Science Pipeline Integration + +```markdown +# RHOAI Feature Refinement - Data Science Pipeline Integration + +| RHOAI Feature Refinement | | | | +| :---- | :---- | :---- | :---- | +| [RHOAI-456] Feature Summary: *Integrate Kubeflow Pipelines with RHOAI data science projects* Feature Owner/PM: *Sarah Chen* Delivery Owner: *Mike Rodriguez* RFE Council Reviewer: *Alex Thompson* | **Refinement Status** | Generated | | +| | **Slack channel or thread for discussions** | #forum-openshift-ai-pipelines | | +| | **Date created** | 2024-03-15 | | + +# **Feature Overview** + +Enable data scientists to create, manage, and execute ML pipelines directly within RHOAI workbenches. Users can design pipelines using a visual interface, version control pipeline definitions, and monitor execution across the OpenShift cluster. This bridges the gap between experimentation and production deployment. + +## **The Why** + +Current data scientists must manually orchestrate ML workflows, leading to inconsistent deployments and difficulty scaling models to production. This feature will reduce time-to-production by 60% and ensure reproducible ML workflows, making RHOAI competitive with Databricks and AWS SageMaker. + +## **High level requirements** + +1. Visual pipeline designer integrated into RHOAI dashboard +2. Pipeline versioning and Git integration +3. Automated model deployment from pipeline outputs +4. Resource scheduling and optimization for pipeline steps +5. Pipeline monitoring and alerting + +## **Non-functional requirements** + +* **Performance parameters**: Support 100+ concurrent pipeline executions +* **Security concerns**: Pipeline isolation, RBAC integration with OpenShift +* **Disconnected needs**: Offline pipeline execution capability +* **User expectations**: Sub-5 minute pipeline startup time + +## **Out of scope** + +- Custom pipeline step authoring (use existing containers only) +- Integration with external orchestration tools (Airflow, etc.) +- Real-time streaming pipelines + +## **Feature level Dependencies** + +- Kubeflow Pipelines upstream integration +- OpenShift Service Mesh for pipeline networking +- RHOAI Model Registry for artifact management + +### **Will this feature require bringing in new upstream projects or sub-projects into the product?** + +Yes - Kubeflow Pipelines v2.0 SDK and Tekton integration + +### **Will this feature require onboarding of a new container Image or Component?** + +Yes - Pipeline execution engine containers and SDK images + +### **Will this feature require user-facing documentation?** + +Yes - Comprehensive documentation including tutorials and API reference + +### **Will this feature require UX design?** + +Yes - Visual pipeline designer interface requires extensive UX work + +## **Acceptance Criteria** + +- Users can create pipelines with 5+ steps using visual interface +- Pipeline execution logs are accessible within RHOAI dashboard +- Failed pipelines provide clear error messages and retry mechanisms +- Pipeline artifacts integrate with RHOAI Model Registry +- Performance meets SLA: 95% of pipelines complete within expected timeframes + +## **High Level Delivery Plan** + +| Team | Sprint Start Availability | Work to Deliver | Dependencies | T-Shirt Size | Comments | +| ----- | ----- | ----- | ----- | ----- | ----- | +| **Platform (Required)** | Sprint 24.2 | Kubeflow integration, cluster config | Upstream approval | L | Platform impact assessment complete | +| **Dashboard Team** | Sprint 24.3 | Visual pipeline designer UI | UX designs | M | Requires new React components | +| **Backend Team** | Sprint 24.2 | Pipeline API, execution engine | Platform work | M | REST API and database schema | +``` + +### Example 2: Model Serving Optimization + +```markdown +# RHOAI Feature Refinement - Multi-Model Serving Optimization + +| RHOAI Feature Refinement | | | | +| :---- | :---- | :---- | :---- | +| [RHOAI-789] Feature Summary: *Optimize resource usage for serving multiple ML models* Feature Owner/PM: *David Kim* Delivery Owner: *Lisa Wang* RFE Council Reviewer: *TBD* | **Refinement Status** | Generated | | +| | **Slack channel or thread for discussions** | #forum-openshift-ai-serving | | +| | **Date created** | 2024-03-20 | | + +# **Feature Overview** + +Enable efficient resource sharing when serving multiple ML models by implementing dynamic batching, model caching, and intelligent resource allocation. Users can deploy multiple models on shared infrastructure with automatic scaling based on demand. + +## **The Why** + +Current model serving requires dedicated resources per model, leading to 70% resource waste and high infrastructure costs. This optimization will reduce serving costs by 50% while improving response times through intelligent batching and caching. + +## **High level requirements** + +1. Dynamic request batching across multiple models +2. Intelligent model caching with LRU eviction +3. Automatic resource scaling based on model demand +4. Request routing optimization +5. Cost and performance monitoring dashboard + +## **Non-functional requirements** + +* **Performance parameters**: <100ms latency increase with batching +* **Security concerns**: Model isolation, secure multi-tenancy +* **Disconnected needs**: Works with edge deployments +* **User expectations**: Zero-downtime model updates + +## **Out of scope** + +- Cross-cloud model serving +- Custom inference frameworks beyond supported ones +- Real-time model retraining + +## **Feature level Dependencies** + +- KServe optimization APIs +- Prometheus metrics integration +- OpenShift resource quotas + +### **Will this feature require bringing in new upstream projects or sub-projects into the product?** + +No - uses existing KServe and Istio components + +### **Will this feature require onboarding of a new container Image or Component?** + +Yes - Optimized serving runtime containers + +### **Will this feature require user-facing documentation?** + +Yes - Configuration guides and best practices documentation + +### **Will this feature require UX design?** + +Yes - Resource utilization dashboard requires UX design + +## **Acceptance Criteria** + +- Multiple models can share resources with <10% performance degradation +- Resource utilization improves by 40% compared to dedicated serving +- Dashboard shows real-time resource usage and cost metrics +- Model updates complete without request failures +- Automatic scaling responds to load changes within 30 seconds + +## **High Level Delivery Plan** + +| Team | Sprint Start Availability | Work to Deliver | Dependencies | T-Shirt Size | Comments | +| ----- | ----- | ----- | ----- | ----- | ----- | +| **Platform (Required)** | Sprint 24.1 | Resource optimization, KServe config | None | S | Minimal platform changes | +| **Serving Team** | Sprint 24.1 | Batching logic, caching implementation | Platform work | M | Core optimization algorithms | +| **Dashboard Team** | Sprint 24.2 | Monitoring dashboard | UX designs, backend APIs | S | Extend existing dashboards | +``` + +## Template Structure + +Now create a refinement document for {{issue_key}} following this exact structure: + +```markdown +# RHOAI Feature Refinement - [FEATURE NAME] + +| RHOAI Feature Refinement | | | | +| :---- | :---- | :---- | :---- | +| [{{issue_key}}] Feature Summary: *[Brief summary from Jira]* Feature Owner/PM: *TBD* Delivery Owner: *TBD* RFE Council Reviewer: *TBD* | **Refinement Status** | Generated | | +| | **Slack channel or thread for discussions** | #forum-openshift-ai-[feature-name] | | +| | **Date created** | [Current Date] | | + +# **Feature Overview** + +[Clear description based on Jira issue. Who benefits? What's the user narrative? What's the difference between today and future state?] + +## **The Why** + +[Business justification from Jira context. Why now? Customer impact? Competitive advantage?] + +## **High level requirements** + +[List functionality by business value priority. Only mandatory items for initial release.] + +## **Non-functional requirements** + +* **Performance parameters**: [Specific metrics and targets] +* **Security concerns**: [Security requirements and considerations] +* **Disconnected needs**: [Offline/air-gapped requirements] +* **User expectations**: [UX and performance expectations] + +## **Out of scope** + +[Explicitly list what's NOT included for clarity] + +## **Feature level Dependencies** + +[Internal and external dependencies from Jira analysis] + +### **Will this feature require bringing in new upstream projects or sub-projects into the product?** + +[Yes/No with specifics] + +### **Will this feature require onboarding of a new container Image or Component?** + +[Yes/No - note RHOAIENG tickets if needed] + +### **Will this feature require user-facing documentation?** + +[Yes/No - specify type] + +### **Will this feature require UX design?** + +[Yes/No - note UXD component if needed] + +## **Acceptance Criteria** + +[Specific, measurable success criteria] + +## **High Level Delivery Plan** + +[Based on components and scope from Jira] + +| Team | Sprint Start Availability | Work to Deliver | Dependencies | T-Shirt Size | Comments | +| ----- | ----- | ----- | ----- | ----- | ----- | +| **Platform (Required)** | TBD | TBD | TBD | TBD | Platform impact assessment needed | +| [Other teams based on Jira analysis] | TBD | TBD | TBD | TBD | TBD | +``` + +## Output Requirements + +- Return ONLY the complete markdown refinement document +- Replace ALL placeholder text with specific information from the Jira issue +- Use the examples as a quality standard +- If information is missing from Jira, mark as "TBD - requires stakeholder input" +- Ensure all sections are meaningful and actionable +- Follow the exact markdown formatting shown in examples + +Begin by fetching the Jira issue details for {{issue_key}}, then generate the complete refinement document following the template and examples above. \ No newline at end of file diff --git a/src/rhoai_ai_feature_sizing/prompts/validate_refinement.md b/src/rhoai_ai_feature_sizing/prompts/validate_refinement.md new file mode 100644 index 000000000..902b27321 --- /dev/null +++ b/src/rhoai_ai_feature_sizing/prompts/validate_refinement.md @@ -0,0 +1,117 @@ +# Task: Validate RHOAI Feature Refinement Document + +You are a senior technical reviewer evaluating RHOAI feature refinement documents for quality and completeness. + +## Assessment Criteria + +Evaluate the document against these standards: + +### 1. **Completeness** (25 points) +- All required sections present and filled with meaningful content +- No placeholder text or "TBD" without proper context +- Information depth appropriate for stakeholder decision-making + +### 2. **Clarity** (20 points) +- Clear, specific, actionable language +- Avoids vague terms like "improved", "better", "enhanced" without metrics +- Technical concepts explained appropriately for mixed audience + +### 3. **Business Value** (20 points) +- Articulates why this feature matters and who benefits +- Clear competitive advantage or customer pain point addressed +- Quantified business impact where possible + +### 4. **Technical Feasibility** (15 points) +- Realistic requirements and time estimates +- Dependencies clearly identified +- T-shirt sizing appears reasonable for scope + +### 5. **Scope Definition** (10 points) +- Clear boundaries of what's included/excluded +- "Out of scope" section properly filled +- Requirements prioritized by business value + +### 6. **Acceptance Criteria** (10 points) +- Specific, measurable success criteria +- Testable outcomes defined +- Clear definition of "done" + +## Scoring Guide + +- **0.9-1.0**: Exceptional - Ready for immediate stakeholder review +- **0.8-0.89**: Good - Minor improvements needed, acceptable for review +- **0.7-0.79**: Fair - Significant improvements needed before review +- **0.6-0.69**: Poor - Major rework required +- **Below 0.6**: Unacceptable - Start over + +**Passing threshold: 0.8+** + +## Response Format + +Return your assessment as JSON with this exact structure: + +```json +{ + "overall_score": 0.85, + "passed": true, + "section_scores": { + "completeness": 0.9, + "clarity": 0.8, + "business_value": 0.9, + "technical_feasibility": 0.8, + "scope_definition": 0.7, + "acceptance_criteria": 0.8 + }, + "issues": [ + { + "section": "Feature Overview", + "severity": "minor", + "issue": "Could be more specific about user personas", + "suggestion": "Add specific examples of data scientists who would use this feature" + }, + { + "section": "Acceptance Criteria", + "severity": "major", + "issue": "Criteria not measurable", + "suggestion": "Replace 'users should be satisfied' with 'survey scores >4.0/5.0'" + } + ], + "strengths": [ + "Clear business justification with quantified benefits", + "Realistic t-shirt sizing and timeline estimates", + "Well-defined scope boundaries" + ], + "summary": "High quality document with clear value proposition. Minor improvements needed in acceptance criteria specificity." +} +``` + +## Severity Levels + +- **critical**: Document cannot proceed without fixing this issue +- **major**: Significantly impacts document quality, should be addressed +- **minor**: Small improvement that would enhance clarity +- **suggestion**: Optional enhancement for future consideration + +## Quality Standards Examples + +### ❌ Poor Examples: +- "This will improve user experience" (vague) +- "Performance will be better" (unmeasurable) +- "Timeline: TBD" (incomplete) +- "Users should be happy" (untestable acceptance criteria) + +### ✅ Good Examples: +- "Reduces data scientist model deployment time from 2 days to 30 minutes" (specific, measurable) +- "Response time <100ms for 95% of requests under normal load" (quantified performance) +- "Sprint 24.2 - Dashboard team can start UI work" (specific timeline) +- "Users can create pipelines with 5+ steps using visual interface" (testable criteria) + +## Focus Areas + +Prioritize feedback on: +1. **Actionable suggestions** - provide specific improvements +2. **Business impact clarity** - ensure value proposition is compelling +3. **Measurable outcomes** - push for quantified success criteria +4. **Realistic scope** - flag overly ambitious or unclear requirements + +Assess the provided document thoroughly and return only the JSON assessment. \ No newline at end of file diff --git a/stages/draft_jiras.py b/src/rhoai_ai_feature_sizing/stages/draft_jiras.py similarity index 100% rename from stages/draft_jiras.py rename to src/rhoai_ai_feature_sizing/stages/draft_jiras.py diff --git a/stages/estimate.py b/src/rhoai_ai_feature_sizing/stages/estimate.py similarity index 100% rename from stages/estimate.py rename to src/rhoai_ai_feature_sizing/stages/estimate.py diff --git a/src/rhoai_ai_feature_sizing/stages/refine_feature.py b/src/rhoai_ai_feature_sizing/stages/refine_feature.py new file mode 100644 index 000000000..298ed92a6 --- /dev/null +++ b/src/rhoai_ai_feature_sizing/stages/refine_feature.py @@ -0,0 +1,269 @@ +import logging +import os +import time +from pathlib import Path +from llama_stack_client import Agent +from rhoai_ai_feature_sizing.llama_stack_setup import get_llama_stack_client + + +INSTRUCTIONS = "You are a senior product manager expert at creating RHOAI feature refinement documents. Follow the provided template and examples precisely." + + +def _load_validation_template() -> str: + """Load the validation prompt template from markdown file.""" + template_path = Path(__file__).parent.parent / "prompts" / "validate_refinement.md" + + if not template_path.exists(): + raise FileNotFoundError(f"Validation template not found: {template_path}") + + with open(template_path, "r", encoding="utf-8") as f: + return f.read() + + +def generate_refinement_with_agent(issue_key: str, template: str) -> str: + """ + Generate a comprehensive refinement document using the Llama Stack agent. + Uses LLM-based validation with iteration for quality improvement. + """ + logger = logging.getLogger("refine_feature") + INFERENCE_MODEL = os.getenv("INFERENCE_MODEL") + + if not issue_key or not issue_key.strip(): + raise ValueError("Issue key cannot be empty") + + if not template or not template.strip(): + raise ValueError("Template cannot be empty") + + if not INFERENCE_MODEL: + raise ValueError("INFERENCE_MODEL environment variable must be set") + + logger.info(f"Generating refinement document for Jira issue {issue_key}...") + + try: + client = get_llama_stack_client() + + # Load validation template + validation_template = _load_validation_template() + + # Create generation agent + generation_agent = Agent( + client, + model=INFERENCE_MODEL, + instructions=INSTRUCTIONS, + tools=["mcp::atlassian"], + ) + + # Create validation agent + validation_agent = Agent( + client, + model=INFERENCE_MODEL, + instructions=validation_template, + tools=[], + ) + + # Generate and iterate on the document + best_content = None + best_score = 0.0 + max_iterations = 3 + + for iteration in range(max_iterations): + start_time = time.time() + + session_id = generation_agent.create_session( + f"refine-{issue_key}-iter{iteration}-{int(time.time())}" + ) + logger.debug( + f"Created generation session: {session_id} (iteration {iteration + 1})" + ) + + # Create prompt based on iteration + if iteration == 0: + # First attempt: use template directly + prompt = template.replace("{{issue_key}}", issue_key) + else: + # Subsequent attempts: include validation feedback + prompt = _create_improvement_prompt( + issue_key, template, validation_result + ) + + # Generate content + response = generation_agent.create_turn( + messages=[ + { + "role": "user", + "content": prompt, + } + ], + session_id=session_id, + stream=False, + ) + + content = response.output_message.content + duration = time.time() - start_time + + logger.info( + f"Generated content in {duration:.2f}s (iteration {iteration + 1}, length: {len(content)} chars)" + ) + + # Validate with LLM + validation_result = _validate_with_llm(validation_agent, content, issue_key) + + logger.info( + f"Validation score: {validation_result.get('overall_score', 0):.2f}" + ) + + # Track best result + current_score = validation_result.get("overall_score", 0) + if current_score > best_score: + best_score = current_score + best_content = content + + # Check if we have a passing result + if validation_result.get("passed", False) and current_score >= 0.8: + logger.info( + f"✅ Document passed validation on iteration {iteration + 1} with score {current_score:.2f}" + ) + return content + + # Log issues for next iteration + issues = validation_result.get("issues", []) + if issues: + logger.info( + f"📋 Found {len(issues)} issues to address in next iteration" + ) + for issue in issues[:3]: # Log top 3 issues + logger.info( + f" - {issue.get('section', 'General')}: {issue.get('issue', 'N/A')}" + ) + + # If we didn't get a passing result, return the best attempt + logger.warning( + f"⚠️ No iteration achieved passing score. Returning best attempt (score: {best_score:.2f})" + ) + return best_content or content + + except Exception as e: + logger.error(f"Unexpected error: {e}") + raise RuntimeError(f"Failed to generate refinement document: {e}") from e + + +def _validate_with_llm(validation_agent: Agent, content: str, issue_key: str) -> dict: + """Use LLM to validate the generated content quality.""" + logger = logging.getLogger("refine_feature") + + try: + validation_session = validation_agent.create_session( + f"validate-{issue_key}-{int(time.time())}" + ) + + validation_prompt = f"""Please evaluate this RHOAI feature refinement document: + +--- +{content} +--- + +Provide your assessment as JSON following the specified format.""" + + response = validation_agent.create_turn( + messages=[{"role": "user", "content": validation_prompt}], + session_id=validation_session, + stream=False, + ) + + validation_content = response.output_message.content + + # Extract JSON from response + result = _extract_validation_json(validation_content) + logger.debug(f"Validation result: {result}") + + return result + + except Exception as e: + logger.error(f"LLM validation failed: {e}") + # Fallback to basic validation + return _basic_fallback_validation(content) + + +def _extract_validation_json(content: str) -> dict: + """Extract JSON validation result from LLM response.""" + import json + import re + + # Try to find JSON in the response + json_pattern = r"\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}" + matches = re.findall(json_pattern, content, re.DOTALL) + + for match in matches: + try: + return json.loads(match) + except json.JSONDecodeError: + continue + + # Fallback if no valid JSON found + return { + "overall_score": 0.5, + "passed": False, + "issues": [ + {"section": "General", "issue": "Could not parse validation response"} + ], + "summary": "Validation parsing failed", + } + + +def _create_improvement_prompt( + issue_key: str, template: str, validation_result: dict +) -> str: + """Create an improvement prompt based on validation feedback.""" + issues = validation_result.get("issues", []) + + feedback_text = "\n".join( + [ + f"- {issue.get('section', 'General')}: {issue.get('issue', 'N/A')}" + + (f" Suggestion: {issue['suggestion']}" if issue.get("suggestion") else "") + for issue in issues[:5] # Top 5 issues + ] + ) + + return f"""Based on the feedback below, please improve the RHOAI feature refinement document for issue {issue_key}. + +PREVIOUS ISSUES TO ADDRESS: +{feedback_text} + +STRENGTHS TO MAINTAIN: +{', '.join(validation_result.get('strengths', []))} + +{template.replace("{{issue_key}}", issue_key)} + +Focus on addressing the specific issues mentioned while maintaining the document's strengths. Ensure all content is specific, actionable, and follows the examples provided.""" + + +def _basic_fallback_validation(content: str) -> dict: + """Basic fallback validation if LLM validation fails.""" + issues = [] + score = 0.7 # Default reasonable score + + required_sections = [ + "# RHOAI Feature Refinement", + "## **Feature Overview**", + "## **The Why**", + "## **Acceptance Criteria**", + ] + + missing_sections = [ + section for section in required_sections if section not in content + ] + if missing_sections: + issues.extend( + [ + {"section": section, "issue": "Section missing"} + for section in missing_sections + ] + ) + score -= 0.1 * len(missing_sections) + + return { + "overall_score": max(0, score), + "passed": score >= 0.8, + "issues": issues, + "summary": "Basic validation (LLM validation unavailable)", + } diff --git a/stages/refine_feature.py b/stages/refine_feature.py deleted file mode 100644 index ce7ca86ac..000000000 --- a/stages/refine_feature.py +++ /dev/null @@ -1,71 +0,0 @@ -import json -import logging -from pathlib import Path -from llama_stack_client import Agent -from llama_stack_setup import get_llama_stack_client - -MODEL = "llama3.2:3b" -INSTRUCTIONS = "You are a helpful assistant. Use the available tools to fetch Jira issue details and fill out the refinement spec." - - -def extract_first_json(text): - """Extract the first valid JSON object from a string.""" - import re - from json import JSONDecodeError - - logger = logging.getLogger("refine_feature") - matches = re.finditer(r"\{.*?\}", text, re.DOTALL) - for match in matches: - try: - logger.debug(f"Trying to parse JSON: {match.group(0)}") - return json.loads(match.group(0)) - except JSONDecodeError as e: - logger.debug(f"Failed to parse JSON: {e}") - continue - logger.error(f"Could not extract valid JSON from agent output: {text}") - raise ValueError(f"Could not extract valid JSON from agent output: {text}") - - -def fetch_jira_issue_with_agent(issue_key: str) -> dict: - """Fetch Jira issue details using the Llama Stack agent with MCP Atlassian tools.""" - logger = logging.getLogger("refine_feature") - logger.info(f"Fetching Jira issue {issue_key} with agent...") - - client = get_llama_stack_client() - agent = Agent( - client, - model=MODEL, - instructions=INSTRUCTIONS, - tools=["mcp::atlassian"], - ) - session_id = agent.create_session("refine-feature-session") - logger.debug(f"Created agent session: {session_id}") - - response = agent.create_turn( - messages=[ - { - "role": "user", - "content": f"Fetch the Jira issue with key {issue_key} and return its summary and description as JSON.", - } - ], - session_id=session_id, - stream=False, - ) - - content = response.output_message.content - logger.info(f"Agent response: {content}") - issue = extract_first_json(content) - logger.info(f"Extracted issue: {issue}") - return issue - - -def fill_template(template: str, issue: dict) -> str: - """Fill the refinement template with issue details.""" - logger = logging.getLogger("refine_feature") - - summary = issue.get("summary", "") - description = issue.get("description", "") - feature_text = f"**{summary}**\n\n{description}" - - logger.debug(f"Filling template with feature_text: {feature_text}") - 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Update command-line interface to support soft and hard modes for Jira ticket generation. Improve logging and validation processes for generated ticket structures. --- README.md | 52 +- src/rhoai_ai_feature_sizing/main.py | 101 +++- .../prompts/draft_jiras.md | 452 ++++++++++++++++++ .../prompts/validate_jira_draft.md | 143 ++++++ .../stages/draft_jiras.py | 378 +++++++++++++++ 5 files changed, 1107 insertions(+), 19 deletions(-) create mode 100644 src/rhoai_ai_feature_sizing/prompts/draft_jiras.md create mode 100644 src/rhoai_ai_feature_sizing/prompts/validate_jira_draft.md diff --git a/README.md b/README.md index dcd637dd1..4d92bd8b2 100644 --- a/README.md +++ b/README.md @@ -66,18 +66,41 @@ uv run python -m rhoai_ai_feature_sizing.main stage refine PROJ-123 # 2. Create epics from refined spec (not yet implemented) uv run python -m rhoai_ai_feature_sizing.main stage epics refined_PROJ-123.md -# 3. Draft Jira tickets from epics (not yet implemented) -uv run python -m rhoai_ai_feature_sizing.main stage jiras epics_PROJ-123.md +# 3. Draft Jira tickets from refined spec (soft mode - no actual tickets created) +uv run python -m rhoai_ai_feature_sizing.main stage jiras refined_PROJ-123.md + +# 3a. Draft Jira tickets in hard mode (creates actual Jira tickets) +uv run python -m rhoai_ai_feature_sizing.main stage jiras refined_PROJ-123.md --hard-mode # 4. Create estimates from tickets (not yet implemented) uv run python -m rhoai_ai_feature_sizing.main stage estimate jiras_PROJ-123.md ``` +### Common Workflows + +```bash +# Workflow 1: Analyze and plan feature implementation (soft mode) +uv run python -m rhoai_ai_feature_sizing.main stage refine PROJ-123 +uv run python -m rhoai_ai_feature_sizing.main stage jiras refined_PROJ-123.md +# Review jiras_PROJ-123.md for epic/story breakdown and estimates + +# Workflow 2: Complete feature setup with actual Jira tickets +uv run python -m rhoai_ai_feature_sizing.main stage refine PROJ-123 +uv run python -m rhoai_ai_feature_sizing.main stage jiras refined_PROJ-123.md --hard-mode +# Actual epics and stories will be created in your Jira instance + +# Workflow 3: Run full pipeline (currently uses soft mode by default) +uv run python -m rhoai_ai_feature_sizing.main run PROJ-123 +``` + ### Full Pipeline ```bash -# Run complete workflow (only refine stage currently works) +# Run complete workflow (refine + jiras stages work) uv run python -m rhoai_ai_feature_sizing.main run PROJ-123 + +# Run with hard mode for actual Jira ticket creation +uv run python -m rhoai_ai_feature_sizing.main run PROJ-123 --hard-mode ``` ### API Server Mode @@ -116,10 +139,24 @@ OUTPUT_DIR=./outputs **Refine Stage** - Convert Jira issues to detailed specs: ```bash # Fetch a Jira issue and refine it into a detailed spec -uv run python -m rhoai_ai_feature_sizing.cli refine PROJ-123 +uv run python -m rhoai_ai_feature_sizing.main stage refine PROJ-123 # Output: refined_PROJ-123.md ``` +**Draft Jiras Stage** - Break down features into implementable tickets: +```bash +# Generate Jira tickets structure from refined spec (soft mode - no actual tickets) +uv run python -m rhoai_ai_feature_sizing.main stage jiras refined_PROJ-123.md +# Output: jiras_PROJ-123.md + +# Create actual Jira tickets (hard mode) +uv run python -m rhoai_ai_feature_sizing.main stage jiras refined_PROJ-123.md --hard-mode +``` + +**Soft Mode vs Hard Mode:** +- **Soft Mode** (default): Generates a structured markdown document with detailed ticket definitions including titles, descriptions, dependencies, story points, and parent-child relationships. No actual Jira tickets are created. +- **Hard Mode**: Uses the MCP Atlassian integration to create actual Jira tickets in your configured Jira instance based on the generated structure. + **API Server** - RESTful API with job management: ```bash # Start the API server @@ -143,8 +180,7 @@ uv run python -m rhoai_ai_feature_sizing.cli refine PROJ-123 --wait ### 🚧 Planned Features (Not Yet Implemented) The following stages are defined but not implemented: -- **Epics Stage**: Break refined specs into epics -- **Jiras Stage**: Create individual Jira tickets from epics +- **Epics Stage**: Break refined specs into epics - **Estimate Stage**: Add story point estimates to tickets ### 🔍 Architecture Details @@ -162,8 +198,10 @@ Jira Issue PROJ-123 Jira details (JSON) ↓ (via LLM + template) refined_PROJ-123.md + ↓ (via LLM + template) +jiras_PROJ-123.md (soft mode: structure only / hard mode: actual tickets) ↓ (planned stages) -epics_PROJ-123.md → jiras_PROJ-123.md → estimates_PROJ-123.md +epics_PROJ-123.md → estimates_PROJ-123.md ``` ## 🎯 Architecture Overview diff --git a/src/rhoai_ai_feature_sizing/main.py b/src/rhoai_ai_feature_sizing/main.py index fd7ab4c8b..4ae77f319 100644 --- a/src/rhoai_ai_feature_sizing/main.py +++ b/src/rhoai_ai_feature_sizing/main.py @@ -9,6 +9,9 @@ from rhoai_ai_feature_sizing.stages.refine_feature import ( generate_refinement_with_agent, ) +from rhoai_ai_feature_sizing.stages.draft_jiras import ( + draft_jiras_from_file, +) # Setup logging @@ -119,16 +122,53 @@ def run_epics_stage(input_file: Path, output_dir: Path, debug: bool = False): return output_path -def run_jiras_stage(input_file: Path, output_dir: Path, debug: bool = False): - """Run the draft jiras stage.""" +def run_jiras_stage( + input_file: Path, output_dir: Path, debug: bool = False, soft_mode: bool = True +): + """Run the draft jiras stage with enhanced error handling and monitoring.""" logger = logging.getLogger("jiras") - logger.info(f"Creating jiras from {input_file}") + start_time = time.time() + + # Validate inputs + if not input_file.exists(): + logger.error(f"Input file not found: {input_file}") + sys.exit(1) + + logger.info( + f"Generating Jira tickets structure from {input_file} (soft_mode: {soft_mode})" + ) + + try: + jira_content = draft_jiras_from_file(input_file, soft_mode=soft_mode) + except Exception as e: + logger.error(f"Failed to generate Jira tickets structure: {e}") + if debug: + logger.exception("Full traceback:") + sys.exit(1) + + # Determine output filename based on input type + if input_file.stem.startswith("epics_"): + issue_key = input_file.stem.replace("epics_", "") + elif input_file.stem.startswith("refined_"): + issue_key = input_file.stem.replace("refined_", "") + else: + issue_key = input_file.stem - # TODO: Implement jiras creation logic - output_path = output_dir / f"jiras_{input_file.stem.replace('epics_', '')}.md" + output_path = output_dir / f"jiras_{issue_key}.md" + logger.info(f"Writing Jira tickets structure to {output_path}") - logger.warning("Jiras stage not yet implemented") - print(f"✗ Jiras stage not yet implemented. Would output to {output_path}") + try: + with open(output_path, "w", encoding="utf-8") as f: + f.write(jira_content) + except Exception as e: + logger.error(f"Failed to write output file: {e}") + sys.exit(1) + + # Log performance metrics + duration = time.time() - start_time + mode_text = "soft mode" if soft_mode else "hard mode" + logger.info(f"Jira tickets stage completed in {duration:.2f}s ({mode_text})") + print(f"✓ Jira tickets structure written to {output_path} ({mode_text})") return output_path @@ -145,7 +185,9 @@ def run_estimate_stage(input_file: Path, output_dir: Path, debug: bool = False): return output_path -def run_full_pipeline(issue_key: str, output_dir: Path, debug: bool = False): +def run_full_pipeline( + issue_key: str, output_dir: Path, debug: bool = False, soft_mode: bool = True +): """Run the complete pipeline.""" logger = logging.getLogger("pipeline") logger.info(f"Running full pipeline for {issue_key}") @@ -160,9 +202,16 @@ def run_full_pipeline(issue_key: str, output_dir: Path, debug: bool = False): print("📋 Stage 2: Creating epics...") epics_output = run_epics_stage(refined_output, output_dir, debug) - # Stage 3: Jiras + # Stage 3: Jiras (use refined output if epics stage isn't implemented) print("🎫 Stage 3: Drafting jiras...") - jiras_output = run_jiras_stage(epics_output, output_dir, debug) + # For now, use refined output since epics stage isn't implemented yet + if not epics_output.exists(): + logger.info("Epics stage not implemented, using refined output for jiras stage") + jiras_input = refined_output + else: + jiras_input = epics_output + + jiras_output = run_jiras_stage(jiras_input, output_dir, debug, soft_mode) # Stage 4: Estimate print("📊 Stage 4: Creating estimates...") @@ -221,6 +270,17 @@ def main(): jiras_parser.add_argument( "input_file", type=Path, help="Input file from epics stage" ) + jiras_parser.add_argument( + "--soft-mode", + action="store_true", + default=True, + help="Generate ticket structure without creating actual Jira tickets (default: True)", + ) + jiras_parser.add_argument( + "--hard-mode", + action="store_true", + help="Create actual Jira tickets (overrides --soft-mode)", + ) # Stage: estimate estimate_parser = stage_subparsers.add_parser( @@ -233,6 +293,17 @@ def main(): # Run subcommand (full pipeline) run_parser = subparsers.add_parser("run", help="Run the complete pipeline") run_parser.add_argument("issue_key", help="Jira issue key (e.g., PROJ-123)") + run_parser.add_argument( + "--soft-mode", + action="store_true", + default=True, + help="Generate ticket structure without creating actual Jira tickets (default: True)", + ) + run_parser.add_argument( + "--hard-mode", + action="store_true", + help="Create actual Jira tickets (overrides --soft-mode)", + ) # Parse arguments args = parser.parse_args() @@ -260,7 +331,11 @@ def main(): f"Error: Input file not found: {args.input_file}", file=sys.stderr ) sys.exit(1) - run_jiras_stage(args.input_file, args.output_dir, args.debug) + # Determine soft_mode: hard_mode flag overrides soft_mode + soft_mode = ( + not args.hard_mode if hasattr(args, "hard_mode") else args.soft_mode + ) + run_jiras_stage(args.input_file, args.output_dir, args.debug, soft_mode) elif args.stage == "estimate": if not args.input_file.exists(): print( @@ -271,7 +346,9 @@ def main(): else: stage_parser.print_help() elif args.command == "run": - run_full_pipeline(args.issue_key, args.output_dir, args.debug) + # Determine soft_mode: hard_mode flag overrides soft_mode + soft_mode = not args.hard_mode if hasattr(args, "hard_mode") else args.soft_mode + run_full_pipeline(args.issue_key, args.output_dir, args.debug, soft_mode) else: parser.print_help() diff --git a/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md b/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md new file mode 100644 index 000000000..c63430bce --- /dev/null +++ b/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md @@ -0,0 +1,452 @@ +# Task: Create Jira Tickets Structure from RHOAI Feature Documentation + +## Objective +Analyze the provided RHOAI feature document and create a comprehensive structure of Jira tickets that would be needed to implement the feature. This includes epics, stories, tasks, and their relationships. + +## Step-by-Step Process +1. **Analyze Input Document**: Understand the feature scope, requirements, and delivery plan +2. **Identify Epic Structure**: Break down the feature into logical epics based on teams and components +3. **Create Detailed Tickets**: Generate specific implementable tickets for each epic +4. **Define Relationships**: Establish parent-child and dependency relationships between tickets + +## Soft Mode Instructions +Generate ticket structures in **soft mode** - do NOT create actual Jira tickets. Instead, define the complete ticket structure with: +- Ticket title and detailed description +- Epic/Story/Task/Bug classifications +- Parent-child relationships +- Cross-ticket dependencies +- Component assignments +- Story point estimates +- Priority levels + +## Examples + +### Example 1: Data Science Pipeline Integration + +```markdown +# Jira Tickets Structure - Data Science Pipeline Integration + +## Epic Structure + +### Epic 1: Pipeline Platform Integration +**Epic Title**: Platform Integration for Kubeflow Pipelines +**Epic Key**: [Generated as RHOAI-PIPE-001] +**Description**: Integrate Kubeflow Pipelines v2.0 with RHOAI platform infrastructure +**Component**: Platform +**Epic Owner**: Platform Team +**Story Points**: 21 +**Priority**: High + +#### Child Stories: + +##### Story 1.1: Kubeflow SDK Integration +**Title**: Integrate Kubeflow Pipelines v2.0 SDK +**Type**: Story +**Parent**: Epic 1 +**Description**: +Integrate Kubeflow Pipelines v2.0 SDK into RHOAI platform to enable pipeline execution. + +**Acceptance Criteria:** +- Kubeflow Pipelines v2.0 SDK installed and configured +- Pipeline execution engine can submit and monitor pipeline runs +- Integration tests pass for basic pipeline operations +- Platform can handle pipeline resource allocation + +**Components**: Platform, SDK Integration +**Story Points**: 8 +**Priority**: High +**Dependencies**: None + +##### Story 1.2: Pipeline Resource Management +**Title**: Implement pipeline resource scheduling and quotas +**Type**: Story +**Parent**: Epic 1 +**Description**: +Create resource management system for pipeline executions with quotas and scheduling. + +**Acceptance Criteria:** +- Resource quotas enforced per namespace/project +- Pipeline scheduling prevents resource conflicts +- Failed pipelines release resources properly +- Monitoring of resource usage available + +**Components**: Platform, Resource Management +**Story Points**: 13 +**Priority**: High +**Dependencies**: Story 1.1 + +##### Task 1.2.1: Pipeline Resource Quota Implementation +**Title**: Implement pipeline-specific resource quotas +**Type**: Task +**Parent**: Story 1.2 +**Description**: Create quota enforcement for CPU/Memory/GPU resources per pipeline execution +**Story Points**: 5 +**Dependencies**: Story 1.1 + +##### Task 1.2.2: Pipeline Scheduling Logic +**Title**: Develop pipeline execution scheduling +**Type**: Task +**Parent**: Story 1.2 +**Description**: Implement intelligent scheduling to prevent resource conflicts between concurrent pipelines +**Story Points**: 8 +**Dependencies**: Task 1.2.1 + +### Epic 2: Dashboard Pipeline UI +**Epic Title**: Visual Pipeline Designer Dashboard Integration +**Epic Key**: [Generated as RHOAI-PIPE-002] +**Description**: Create visual pipeline designer interface within RHOAI dashboard +**Component**: Dashboard, UI/UX +**Epic Owner**: Dashboard Team +**Story Points**: 34 +**Priority**: High +**Dependencies**: Epic 1 (Platform Integration) + +#### Child Stories: + +##### Story 2.1: Pipeline Designer Canvas +**Title**: Create visual pipeline designer interface +**Type**: Story +**Parent**: Epic 2 +**Description**: +Build drag-and-drop visual interface for creating ML pipelines with components, connections, and configuration. + +**Acceptance Criteria:** +- Users can drag pipeline components from palette +- Components can be connected with visual connectors +- Component properties can be configured via forms +- Pipeline can be saved and loaded +- Visual validation of pipeline structure + +**Components**: Dashboard, Frontend, React +**Story Points**: 21 +**Priority**: High +**Dependencies**: Epic 1 completion + +##### Story 2.2: Pipeline Execution Monitoring +**Title**: Pipeline run monitoring and logs dashboard +**Type**: Story +**Parent**: Epic 2 +**Description**: +Create dashboard views for monitoring pipeline execution status, logs, and results. + +**Acceptance Criteria:** +- Real-time pipeline execution status display +- Detailed logs accessible for each pipeline step +- Pipeline run history and artifacts viewable +- Error states clearly displayed with actionable information + +**Components**: Dashboard, Backend API +**Story Points**: 13 +**Priority**: Medium +**Dependencies**: Story 2.1, Epic 1 + +### Epic 3: Pipeline API Backend +**Epic Title**: Pipeline Management REST API +**Epic Key**: [Generated as RHOAI-PIPE-003] +**Description**: Backend API services for pipeline CRUD operations and execution management +**Component**: Backend, API +**Epic Owner**: Backend Team +**Story Points**: 21 +**Priority**: High +**Dependencies**: Epic 1 (Platform Integration) + +#### Child Stories: + +##### Story 3.1: Pipeline CRUD API +**Title**: REST API for pipeline management +**Type**: Story +**Parent**: Epic 3 +**Description**: +Create REST API endpoints for creating, reading, updating, and deleting pipeline definitions. + +**Acceptance Criteria:** +- API endpoints for pipeline CRUD operations +- Pipeline validation before saving +- Versioning support for pipeline definitions +- Authentication and authorization integration +- OpenAPI documentation generated + +**Components**: Backend, API, Database +**Story Points**: 13 +**Priority**: High +**Dependencies**: Epic 1 completion + +##### Story 3.2: Pipeline Execution API +**Title**: API for pipeline execution and monitoring +**Type**: Story +**Parent**: Epic 3 +**Description**: +API endpoints for triggering pipeline execution, monitoring status, and retrieving results. + +**Acceptance Criteria:** +- Trigger pipeline execution via API +- Query execution status and progress +- Retrieve execution logs and artifacts +- Cancel/retry pipeline executions +- Webhook support for execution events + +**Components**: Backend, API, Event System +**Story Points**: 8 +**Priority**: High +**Dependencies**: Story 3.1 + +## Cross-Epic Dependencies + +### Critical Path Dependencies: +1. **Epic 1 → Epic 2**: Dashboard requires platform integration +2. **Epic 1 → Epic 3**: Backend API requires platform foundation +3. **Story 2.2 → Story 3.2**: Monitoring UI requires execution API + +### Component Integration Points: +- **Story 1.1 ↔ Story 3.1**: SDK integration must align with API design +- **Story 2.1 ↔ Story 3.1**: UI components need API schema alignment +- **Story 2.2 ↔ Story 3.2**: Monitoring UI needs execution event integration + +## Implementation Phases + +### Phase 1 (Sprint 24.2): Foundation +- Epic 1: Pipeline Platform Integration +- Story 3.1: Pipeline CRUD API (basic endpoints) + +### Phase 2 (Sprint 24.3): Core Features +- Story 2.1: Pipeline Designer Canvas +- Story 3.2: Pipeline Execution API + +### Phase 3 (Sprint 24.4): Monitoring & Polish +- Story 2.2: Pipeline Execution Monitoring +- Integration testing and bug fixes + +## Resource Allocation + +### Platform Team (Epic 1): +- 2 senior engineers +- Duration: 6 weeks +- Story Points: 21 + +### Dashboard Team (Epic 2): +- 1 frontend engineer, 1 UX designer +- Duration: 8 weeks +- Story Points: 34 + +### Backend Team (Epic 3): +- 2 backend engineers +- Duration: 6 weeks +- Story Points: 21 + +**Total Effort**: 76 story points across 3 teams +``` + +### Example 2: Model Serving Optimization + +```markdown +# Jira Tickets Structure - Multi-Model Serving Optimization + +## Epic Structure + +### Epic 1: Serving Runtime Optimization +**Epic Title**: Multi-Model Serving Runtime Optimization +**Epic Key**: [Generated as RHOAI-SERVE-001] +**Description**: Implement dynamic batching, caching, and resource optimization for model serving +**Component**: Serving, Runtime +**Epic Owner**: Serving Team +**Story Points**: 26 +**Priority**: High + +#### Child Stories: + +##### Story 1.1: Dynamic Request Batching +**Title**: Implement dynamic request batching for multiple models +**Type**: Story +**Parent**: Epic 1 +**Description**: +Create intelligent request batching system that groups requests for optimal throughput while maintaining latency requirements. + +**Acceptance Criteria:** +- Requests batched based on model type and availability +- Configurable batch size and timeout parameters +- Latency increase <100ms compared to individual requests +- Throughput improvement >40% for concurrent requests + +**Components**: Serving, Batching Engine +**Story Points**: 13 +**Priority**: High +**Dependencies**: None + +##### Story 1.2: Model Caching System +**Title**: Intelligent model caching with LRU eviction +**Type**: Story +**Parent**: Epic 1 +**Description**: +Implement smart caching system for model artifacts with automatic eviction based on usage patterns. + +**Acceptance Criteria:** +- LRU cache for model artifacts and metadata +- Configurable cache size limits per node +- Cache hit ratio >80% for frequently used models +- Automatic cache warming for predicted models + +**Components**: Serving, Caching, Storage +**Story Points**: 13 +**Priority**: Medium +**Dependencies**: Story 1.1 + +### Epic 2: Resource Optimization Dashboard +**Epic Title**: Serving Resource Monitoring Dashboard +**Epic Key**: [Generated as RHOAI-SERVE-002] +**Description**: Dashboard for monitoring and optimizing serving resource utilization +**Component**: Dashboard, Monitoring +**Epic Owner**: Dashboard Team +**Story Points**: 18 +**Priority**: Medium +**Dependencies**: Epic 1 + +#### Child Stories: + +##### Story 2.1: Resource Utilization Metrics +**Title**: Serving resource utilization metrics collection +**Type**: Story +**Parent**: Epic 2 +**Description**: +Collect and expose metrics for serving resource usage, costs, and optimization opportunities. + +**Acceptance Criteria:** +- Metrics for CPU/GPU/Memory utilization per model +- Cost calculation based on resource usage +- Performance metrics (latency, throughput) +- Resource waste identification algorithms + +**Components**: Monitoring, Metrics, Backend +**Story Points**: 8 +**Priority**: Medium +**Dependencies**: Epic 1 completion + +##### Story 2.2: Optimization Dashboard UI +**Title**: Resource optimization dashboard interface +**Type**: Story +**Parent**: Epic 2 +**Description**: +Create dashboard interface showing resource utilization, cost optimization recommendations, and performance trends. + +**Acceptance Criteria:** +- Real-time resource utilization visualization +- Cost breakdown by model and time period +- Optimization recommendations display +- Historical trend analysis charts + +**Components**: Dashboard, Frontend, Charts +**Story Points**: 10 +**Priority**: Low +**Dependencies**: Story 2.1 + +## Cross-Epic Dependencies + +### Integration Points: +- **Story 1.1 → Story 2.1**: Batching metrics needed for monitoring +- **Story 1.2 → Story 2.1**: Cache metrics required for optimization dashboard + +## Implementation Timeline + +### Phase 1 (Sprint 24.1): Core Optimization +- Story 1.1: Dynamic Request Batching + +### Phase 2 (Sprint 24.2): Advanced Features +- Story 1.2: Model Caching System +- Story 2.1: Resource Utilization Metrics + +### Phase 3 (Sprint 24.3): Dashboard +- Story 2.2: Optimization Dashboard UI + +## Resource Requirements + +### Serving Team (Epic 1): +- 2 senior engineers +- Duration: 4 weeks +- Story Points: 26 + +### Dashboard Team (Epic 2): +- 1 frontend engineer +- Duration: 4 weeks +- Story Points: 18 + +**Total Effort**: 44 story points across 2 teams +``` + +## Template Structure + +Analyze the provided document and create a Jira tickets structure following this format: + +```markdown +# Jira Tickets Structure - [FEATURE NAME] + +## Epic Structure + +[For each major epic/component identified:] + +### Epic N: [Epic Name] +**Epic Title**: [Clear epic title] +**Epic Key**: [Generated as PREFIX-XXX-NNN] +**Description**: [Epic description and scope] +**Component**: [Primary component/team] +**Epic Owner**: [Team responsible] +**Story Points**: [Total epic points] +**Priority**: [High/Medium/Low] +**Dependencies**: [Other epics this depends on] + +#### Child Stories: + +[For each story under the epic:] + +##### Story N.N: [Story Name] +**Title**: [Implementable story title] +**Type**: [Story/Task/Bug] +**Parent**: [Epic reference] +**Description**: [Detailed story description] + +**Acceptance Criteria:** +[Specific, testable acceptance criteria] + +**Components**: [Relevant components] +**Story Points**: [Fibonacci estimate] +**Priority**: [High/Medium/Low] +**Dependencies**: [Other tickets this depends on] + +[Include sub-tasks if story is complex:] + +##### Task N.N.N: [Task Name] +**Title**: [Specific task title] +**Type**: Task +**Parent**: [Story reference] +**Description**: [Task details] +**Story Points**: [Points] +**Dependencies**: [Task dependencies] + +## Cross-Epic Dependencies + +### Critical Path Dependencies: +[List blocking dependencies between epics] + +### Component Integration Points: +[List integration requirements between stories] + +## Implementation Phases + +[Suggested phased rollout plan] + +## Resource Allocation + +[Team assignments and effort estimates] +``` + +## Output Requirements + +- Analyze the input document thoroughly +- Break down into logical epics based on teams and technical components +- Create detailed, implementable stories with clear acceptance criteria +- Define realistic story point estimates using Fibonacci sequence (1,2,3,5,8,13,21...) +- Establish clear parent-child and dependency relationships +- Include suggested implementation phases +- Ensure all stories are specific enough to be implemented +- Use "soft mode" - do NOT create actual Jira tickets, only define the structure +- Return ONLY the complete markdown structure + +Begin by analyzing the provided RHOAI feature document and generating the complete Jira tickets structure. \ No newline at end of file diff --git a/src/rhoai_ai_feature_sizing/prompts/validate_jira_draft.md b/src/rhoai_ai_feature_sizing/prompts/validate_jira_draft.md new file mode 100644 index 000000000..068ea3cfe --- /dev/null +++ b/src/rhoai_ai_feature_sizing/prompts/validate_jira_draft.md @@ -0,0 +1,143 @@ +# Task: Validate Jira Tickets Structure + +You are a senior Agile coach and engineering manager evaluating Jira tickets structures for implementation readiness and team efficiency. + +## Assessment Criteria + +Evaluate the ticket structure against these standards: + +### 1. **Epic Structure** (25 points) +- Epics are logically organized by team/component boundaries +- Epic scope is appropriate (not too big, not too granular) +- Clear epic ownership and responsibility assignments +- Epic descriptions capture business value and technical scope + +### 2. **Story Quality** (25 points) +- Stories are implementable within 1-2 sprints +- Acceptance criteria are specific, measurable, and testable +- Stories follow INVEST principles (Independent, Negotiable, Valuable, Estimable, Small, Testable) +- Clear description of what needs to be built + +### 3. **Dependency Management** (20 points) +- Dependencies between stories and epics are clearly identified +- Critical path is apparent and realistic +- No circular dependencies +- Integration points between teams are explicit + +### 4. **Estimation Accuracy** (15 points) +- Story points follow Fibonacci sequence appropriately +- Estimates seem realistic for story scope +- Epic totals align with component complexity +- T-shirt sizing matches actual work breakdown + +### 5. **Implementation Feasibility** (10 points) +- Stories can be implemented by assigned teams +- Technical dependencies are realistic +- Resource allocation is reasonable +- Timeline phases make sense + +### 6. **Completeness** (5 points) +- All major components from original feature are covered +- No obvious gaps in implementation +- Sufficient detail for teams to start work + +## Scoring Guide + +- **0.9-1.0**: Exceptional - Ready for sprint planning immediately +- **0.8-0.89**: Good - Minor adjustments needed before implementation +- **0.7-0.79**: Fair - Significant improvements needed in story details or dependencies +- **0.6-0.69**: Poor - Major restructuring required +- **Below 0.6**: Unacceptable - Fundamental issues with epic/story breakdown + +**Passing threshold: 0.8+** + +## Response Format + +Return your assessment as JSON with this exact structure: + +```json +{ + "overall_score": 0.85, + "passed": true, + "section_scores": { + "epic_structure": 0.9, + "story_quality": 0.8, + "dependency_management": 0.9, + "estimation_accuracy": 0.8, + "implementation_feasibility": 0.7, + "completeness": 0.9 + }, + "issues": [ + { + "section": "Epic 2", + "severity": "minor", + "issue": "Dashboard epic could be split into UX and API integration components", + "suggestion": "Consider separate epics for frontend UI work and backend API integration" + }, + { + "section": "Story 1.2", + "severity": "major", + "issue": "Acceptance criteria not measurable", + "suggestion": "Replace 'system should work well' with specific performance metrics like '95% of requests complete in <200ms'" + } + ], + "strengths": [ + "Clear epic ownership by appropriate teams", + "Realistic story point estimates using Fibonacci sequence", + "Well-defined cross-team dependencies and integration points" + ], + "summary": "Well-structured ticket breakdown with clear team ownership. Minor improvements needed in acceptance criteria specificity." +} +``` + +## Severity Levels + +- **critical**: Blocking issue that prevents implementation +- **major**: Significantly impacts team efficiency or delivery timeline +- **minor**: Small improvement that would enhance clarity +- **suggestion**: Optional enhancement for future consideration + +## Quality Standards Examples + +### ❌ Poor Examples: +- Epic: "Improve user experience" (too vague) +- Story: "Make the system faster" (not measurable) +- Acceptance Criteria: "Users should be happy" (not testable) +- Dependencies: "Everything depends on everything" (not specific) +- Estimates: "This story is 7 points" (not Fibonacci) + +### ✅ Good Examples: +- Epic: "Visual Pipeline Designer Dashboard Integration" (specific scope) +- Story: "Create drag-and-drop interface for pipeline components" (implementable) +- Acceptance Criteria: "Users can drag 5+ component types from palette to canvas" (measurable) +- Dependencies: "Story 2.1 depends on Epic 1 completion for API schema" (specific) +- Estimates: "8 story points" (Fibonacci sequence) + +## Focus Areas + +Prioritize feedback on: +1. **Story implementability** - can a team actually build this in 1-2 sprints? +2. **Dependency clarity** - are integration points between teams explicit? +3. **Acceptance criteria quality** - are they testable and specific? +4. **Epic scope management** - are epics appropriately sized for teams? +5. **Estimation realism** - do story points match complexity? + +## Red Flags to Identify + +- Stories that span multiple teams +- Epics with >50 story points +- Vague acceptance criteria using words like "better", "improved", "easier" +- Missing dependencies between obviously related work +- Stories that can't be demoed independently +- Technical debt or infrastructure work without clear user value + +## Validation Questions + +For each epic/story, ask: +- Can this be implemented by the assigned team alone? +- Are the acceptance criteria specific enough to write tests? +- Would a stakeholder understand the business value? +- Can this be completed in 1-2 sprints? +- Are dependencies realistic and well-defined? + +Assess the provided Jira tickets structure thoroughly and return only the JSON assessment. \ No newline at end of file diff --git a/src/rhoai_ai_feature_sizing/stages/draft_jiras.py b/src/rhoai_ai_feature_sizing/stages/draft_jiras.py index e69de29bb..53d80a3b8 100644 --- a/src/rhoai_ai_feature_sizing/stages/draft_jiras.py +++ b/src/rhoai_ai_feature_sizing/stages/draft_jiras.py @@ -0,0 +1,378 @@ +import logging +import os +import time +from pathlib import Path +from llama_stack_client import Agent +from rhoai_ai_feature_sizing.llama_stack_setup import get_llama_stack_client + + +INSTRUCTIONS = "You are a senior Agile coach and technical lead expert at breaking down RHOAI features into implementable Jira tickets. Follow the provided template and examples precisely." + + +def _load_validation_template() -> str: + """Load the validation prompt template from markdown file.""" + template_path = Path(__file__).parent.parent / "prompts" / "validate_jira_draft.md" + + if not template_path.exists(): + raise FileNotFoundError(f"Validation template not found: {template_path}") + + with open(template_path, "r", encoding="utf-8") as f: + return f.read() + + +def _load_draft_template() -> str: + """Load the draft jiras prompt template from markdown file.""" + template_path = Path(__file__).parent.parent / "prompts" / "draft_jiras.md" + + if not template_path.exists(): + raise FileNotFoundError(f"Draft template not found: {template_path}") + + with open(template_path, "r", encoding="utf-8") as f: + return f.read() + + +def generate_jira_structure_with_agent( + input_content: str, soft_mode: bool = True +) -> str: + """ + Generate a comprehensive Jira tickets structure using the Llama Stack agent. + Uses LLM-based validation with iteration for quality improvement. + + Args: + input_content: The content from refined or epics document + soft_mode: If True, only generate ticket structure without creating actual Jira tickets + """ + logger = logging.getLogger("draft_jiras") + INFERENCE_MODEL = os.getenv("INFERENCE_MODEL") + + if not input_content or not input_content.strip(): + raise ValueError("Input content cannot be empty") + + if not INFERENCE_MODEL: + raise ValueError("INFERENCE_MODEL environment variable must be set") + + logger.info(f"Generating Jira tickets structure (soft_mode: {soft_mode})...") + + try: + client = get_llama_stack_client() + + # Load templates + draft_template = _load_draft_template() + validation_template = _load_validation_template() + + # Create generation agent + generation_agent = Agent( + client, + model=INFERENCE_MODEL, + instructions=INSTRUCTIONS, + tools=( + ["mcp::atlassian"] if not soft_mode else [] + ), # Only use Jira tools if not in soft mode + ) + + # Create validation agent + validation_agent = Agent( + client, + model=INFERENCE_MODEL, + instructions=validation_template, + tools=[], + ) + + # Generate and iterate on the document + best_content = None + best_score = 0.0 + max_iterations = 3 + + for iteration in range(max_iterations): + start_time = time.time() + + session_id = generation_agent.create_session( + f"draft-jiras-iter{iteration}-{int(time.time())}" + ) + logger.debug( + f"Created generation session: {session_id} (iteration {iteration + 1})" + ) + + # Create prompt based on iteration + if iteration == 0: + # First attempt: use template with input content + prompt = _create_initial_prompt( + draft_template, input_content, soft_mode + ) + else: + # Subsequent attempts: include validation feedback + prompt = _create_improvement_prompt( + draft_template, input_content, validation_result, soft_mode + ) + + # Generate content + response = generation_agent.create_turn( + messages=[ + { + "role": "user", + "content": prompt, + } + ], + session_id=session_id, + stream=False, + ) + + content = response.output_message.content + duration = time.time() - start_time + + logger.info( + f"Generated tickets structure in {duration:.2f}s (iteration {iteration + 1}, length: {len(content)} chars)" + ) + + # Validate with LLM + validation_result = _validate_with_llm(validation_agent, content) + + logger.info( + f"Validation score: {validation_result.get('overall_score', 0):.2f}" + ) + + # Track best result + current_score = validation_result.get("overall_score", 0) + if current_score > best_score: + best_score = current_score + best_content = content + + # Check if we have a passing result + if validation_result.get("passed", False) and current_score >= 0.8: + logger.info( + f"✅ Tickets structure passed validation on iteration {iteration + 1} with score {current_score:.2f}" + ) + return content + + # Log issues for next iteration + issues = validation_result.get("issues", []) + if issues: + logger.info( + f"📋 Found {len(issues)} issues to address in next iteration" + ) + for issue in issues[:3]: # Log top 3 issues + logger.info( + f" - {issue.get('section', 'General')}: {issue.get('issue', 'N/A')}" + ) + + # If we didn't get a passing result, return the best attempt + logger.warning( + f"⚠️ No iteration achieved passing score. Returning best attempt (score: {best_score:.2f})" + ) + return best_content or content + + except Exception as e: + logger.error(f"Unexpected error: {e}") + raise RuntimeError(f"Failed to generate Jira tickets structure: {e}") from e + + +def _create_initial_prompt(template: str, input_content: str, soft_mode: bool) -> str: + """Create the initial prompt for generating Jira tickets structure.""" + mode_instruction = ( + "**IMPORTANT: Use SOFT MODE - Do NOT create actual Jira tickets. Only generate the ticket structure definition.**" + if soft_mode + else "**Note: After generating the structure, create actual Jira tickets using the MCP tools.**" + ) + + return f"""{mode_instruction} + +{template} + +## Input Document + +Here is the RHOAI feature document to analyze: + +--- +{input_content} +--- + +Analyze this document thoroughly and generate the complete Jira tickets structure following the template and examples above.""" + + +def _create_improvement_prompt( + template: str, input_content: str, validation_result: dict, soft_mode: bool +) -> str: + """Create an improvement prompt based on validation feedback.""" + issues = validation_result.get("issues", []) + + feedback_text = "\n".join( + [ + f"- {issue.get('section', 'General')}: {issue.get('issue', 'N/A')}" + + (f" Suggestion: {issue['suggestion']}" if issue.get("suggestion") else "") + for issue in issues[:5] # Top 5 issues + ] + ) + + mode_instruction = ( + "**IMPORTANT: Use SOFT MODE - Do NOT create actual Jira tickets. Only generate the ticket structure definition.**" + if soft_mode + else "**Note: After generating the structure, create actual Jira tickets using the MCP tools.**" + ) + + return f"""Based on the feedback below, please improve the Jira tickets structure. + +{mode_instruction} + +PREVIOUS ISSUES TO ADDRESS: +{feedback_text} + +STRENGTHS TO MAINTAIN: +{', '.join(validation_result.get('strengths', []))} + +{template} + +## Input Document + +Here is the RHOAI feature document to analyze: + +--- +{input_content} +--- + +Focus on addressing the specific issues mentioned while maintaining the structure's strengths. Ensure all tickets are implementable, well-estimated, and have clear dependencies.""" + + +def _validate_with_llm(validation_agent: Agent, content: str) -> dict: + """Use LLM to validate the generated Jira tickets structure quality.""" + logger = logging.getLogger("draft_jiras") + + try: + validation_session = validation_agent.create_session( + f"validate-jiras-{int(time.time())}" + ) + + validation_prompt = f"""Please evaluate this Jira tickets structure: + +--- +{content} +--- + +Provide your assessment as JSON following the specified format.""" + + response = validation_agent.create_turn( + messages=[{"role": "user", "content": validation_prompt}], + session_id=validation_session, + stream=False, + ) + + validation_content = response.output_message.content + + # Extract JSON from response + result = _extract_validation_json(validation_content) + logger.debug(f"Validation result: {result}") + + return result + + except Exception as e: + logger.error(f"LLM validation failed: {e}") + # Fallback to basic validation + return _basic_fallback_validation(content) + + +def _extract_validation_json(content: str) -> dict: + """Extract JSON validation result from LLM response.""" + import json + import re + + # Try to find JSON in the response + json_pattern = r"\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}" + matches = re.findall(json_pattern, content, re.DOTALL) + + for match in matches: + try: + return json.loads(match) + except json.JSONDecodeError: + continue + + # Fallback if no valid JSON found + return { + "overall_score": 0.5, + "passed": False, + "issues": [ + {"section": "General", "issue": "Could not parse validation response"} + ], + "summary": "Validation parsing failed", + } + + +def _basic_fallback_validation(content: str) -> dict: + """Basic fallback validation if LLM validation fails.""" + issues = [] + score = 0.7 # Default reasonable score + + required_sections = [ + "# Jira Tickets Structure", + "## Epic Structure", + "### Epic", + "#### Child Stories:", + ] + + missing_sections = [ + section for section in required_sections if section not in content + ] + if missing_sections: + issues.extend( + [ + {"section": section, "issue": "Section missing"} + for section in missing_sections + ] + ) + score -= 0.1 * len(missing_sections) + + # Check for story points in Fibonacci sequence + if any( + invalid_point in content + for invalid_point in [ + "4 points", + "6 points", + "7 points", + "9 points", + "10 points", + "11 points", + "12 points", + ] + ): + issues.append( + { + "section": "Estimation", + "issue": "Non-Fibonacci story points found", + "suggestion": "Use Fibonacci sequence: 1, 2, 3, 5, 8, 13, 21", + } + ) + score -= 0.1 + + return { + "overall_score": max(0, score), + "passed": score >= 0.8, + "issues": issues, + "summary": "Basic validation (LLM validation unavailable)", + } + + +def draft_jiras_from_file(input_file: Path, soft_mode: bool = True) -> str: + """ + Generate Jira tickets structure from an input file. + + Args: + input_file: Path to the input file (refined or epics document) + soft_mode: If True, only generate ticket structure without creating actual Jira tickets + + Returns: + Generated Jira tickets structure content + """ + logger = logging.getLogger("draft_jiras") + + if not input_file.exists(): + raise FileNotFoundError(f"Input file not found: {input_file}") + + logger.info(f"Reading input from {input_file}") + try: + with open(input_file, "r", encoding="utf-8") as f: + input_content = f.read() + except Exception as e: + raise RuntimeError(f"Failed to read input file: {e}") from e + + if not input_content.strip(): + raise ValueError("Input file is empty") + + return generate_jira_structure_with_agent(input_content, soft_mode) From dc5542a1abeb1874d444f9cc69e91cc65a02d9e0 Mon Sep 17 00:00:00 2001 From: Gage Krumbach Date: Wed, 9 Jul 2025 08:25:19 -0500 Subject: [PATCH 003/240] Add deployment guides for LLAMA Stack on OpenShift, including examples for various external API providers. Introduce a deployment script for easier configuration and setup. Update README to reflect new deployment options and link to detailed guides. --- CUSTOM-API-EXAMPLES.md | 268 ++++++++++++++++++++++++++++++++ OPENSHIFT-DEPLOYMENT.md | 312 ++++++++++++++++++++++++++++++++++++++ README.md | 12 ++ deploy-to-openshift.sh | 143 +++++++++++++++++ openshift-deployment.yaml | 191 +++++++++++++++++++++++ 5 files changed, 926 insertions(+) create mode 100644 CUSTOM-API-EXAMPLES.md create mode 100644 OPENSHIFT-DEPLOYMENT.md create mode 100755 deploy-to-openshift.sh create mode 100644 openshift-deployment.yaml diff --git a/CUSTOM-API-EXAMPLES.md b/CUSTOM-API-EXAMPLES.md new file mode 100644 index 000000000..300e93c61 --- /dev/null +++ b/CUSTOM-API-EXAMPLES.md @@ -0,0 +1,268 @@ +# Custom API Examples for LLAMA Stack + +This guide provides specific examples for connecting common hosted LLM APIs to LLAMA Stack on OpenShift. + +## 🎯 Quick Reference + +Most modern LLM APIs are **OpenAI-compatible**, meaning they can use the OpenAI provider with a custom `base_url`. Here are common patterns: + +### API Compatibility Test + +First, test if your API is OpenAI-compatible: + +```bash +./test-api-compatibility.sh "https://your-api-endpoint.com" +``` + +## 📋 Common API Examples + +### 1. Red Hat AI Services (RHOAI) Models + +For APIs like your Mistral endpoint: +``` +https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443 +``` + +**Configuration:** +- **API Base URL**: `https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443` +- **API Key**: Usually not required for internal RHOAI services, or provided separately +- **Model Name**: `mistral-small` (for LLAMA Stack) +- **Model ID**: Often same as model name or `auto` + +**Deploy Command:** +```bash +./deploy-to-openshift.sh custom +``` + +**Example Values During Deployment:** +``` +API base URL: https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443 +API key: [leave empty if not required] +Model name for LLAMA Stack: mistral-small +Actual model ID: mistral-small +``` + +### 2. Google Gemini via API + +For company-hosted Gemini models: + +**Configuration:** +- **API Base URL**: `https://your-gemini-proxy.company.com/v1` +- **API Key**: Your Gemini API key +- **Model Name**: `gemini-pro` (for LLAMA Stack) +- **Model ID**: `gemini-1.5-pro` (actual Gemini model ID) + +### 3. Anthropic Claude via API Gateway + +**Configuration:** +- **API Base URL**: `https://your-claude-gateway.company.com/v1` +- **API Key**: Your Claude API key +- **Model Name**: `claude-3-sonnet` (for LLAMA Stack) +- **Model ID**: `claude-3-sonnet-20240229` + +### 4. Self-Hosted vLLM/TGI + +For your own hosted inference servers: + +**Configuration:** +- **API Base URL**: `https://your-vllm-server.company.com/v1` +- **API Key**: [usually not required] +- **Model Name**: `llama2-7b-chat` (for LLAMA Stack) +- **Model ID**: `meta-llama/Llama-2-7b-chat-hf` + +### 5. Hugging Face Inference Endpoints + +**Configuration:** +- **API Base URL**: `https://your-endpoint.endpoints.huggingface.cloud/v1` +- **API Key**: Your HF token +- **Model Name**: `custom-model` (for LLAMA Stack) +- **Model ID**: The model deployed on your endpoint + +## 🔧 Manual Configuration + +If you prefer manual configuration over the script: + +### 1. Create Secrets + +```bash +# For APIs with authentication +oc create secret generic llama-stack-secrets-custom \ + --from-literal=CUSTOM_API_BASE_URL="https://your-api.com" \ + --from-literal=CUSTOM_API_KEY="your-api-key" \ + --from-literal=CUSTOM_MODEL_NAME="your-model" \ + --from-literal=CUSTOM_MODEL_ID="actual-model-id" \ + -n llama-stack + +# For APIs without authentication (remove API_KEY) +oc create secret generic llama-stack-secrets-custom \ + --from-literal=CUSTOM_API_BASE_URL="https://your-api.com" \ + --from-literal=CUSTOM_MODEL_NAME="your-model" \ + --from-literal=CUSTOM_MODEL_ID="actual-model-id" \ + -n llama-stack +``` + +### 2. Deploy Configuration + +```bash +oc apply -f openshift-deployment-custom-api.yaml +``` + +## 🛠️ Advanced Configuration + +### Custom Headers + +Some APIs require custom headers. Edit the ConfigMap: + +```yaml +providers: + inference: + - provider_id: custom-api + provider_type: remote::openai + config: + api_key: ${env.CUSTOM_API_KEY} + base_url: ${env.CUSTOM_API_BASE_URL} + extra_headers: + X-Custom-Header: ${env.CUSTOM_HEADER_VALUE} + Authorization: "Bearer ${env.CUSTOM_API_KEY}" +``` + +### Multiple Models + +To support multiple models from the same API: + +```yaml +models: + - metadata: {} + model_id: model-1 + provider_id: custom-api + provider_model_id: actual-model-1-id + model_type: llm + - metadata: {} + model_id: model-2 + provider_id: custom-api + provider_model_id: actual-model-2-id + model_type: llm +``` + +### Custom Timeouts + +For slower APIs, adjust timeouts: + +```yaml +providers: + inference: + - provider_id: custom-api + provider_type: remote::openai + config: + api_key: ${env.CUSTOM_API_KEY} + base_url: ${env.CUSTOM_API_BASE_URL} + timeout: 120 # 2 minutes + max_retries: 3 +``` + +## 🔍 Troubleshooting + +### Common Issues + +1. **404 Not Found** + - Check if API URL includes `/v1` suffix + - Try with and without `/v1` + - Verify the full endpoint path + +2. **Authentication Errors** + - Verify API key format + - Check if key should be in headers vs query params + - Some APIs use different auth schemes + +3. **Model Not Found** + - List available models: `curl https://your-api.com/v1/models` + - Use exact model ID from the API + - Some APIs use different model naming + +4. **Timeout Errors** + - Increase timeout values + - Check network connectivity + - Monitor API response times + +### Debug Mode + +To enable debug logging, add to the deployment: + +```yaml +env: +- name: LLAMA_STACK_LOG_LEVEL + value: "DEBUG" +``` + +### Testing Your Configuration + +Once deployed, test with: + +```bash +# Get the route URL +ROUTE_URL=$(oc get route llama-stack-route-custom -n llama-stack -o jsonpath='{.spec.host}') + +# Test health +curl http://$ROUTE_URL/v1/health + +# Test models list +curl http://$ROUTE_URL/v1/models + +# Test inference +curl -X POST http://$ROUTE_URL/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "your-model-name", + "messages": [{"role": "user", "content": "Hello"}], + "max_tokens": 50 + }' +``` + +## 📚 API Format Requirements + +For an API to work with LLAMA Stack's OpenAI provider, it should support: + +### Required Endpoints + +1. **Models**: `GET /v1/models` + ```json + { + "data": [ + {"id": "model-name", "object": "model"} + ] + } + ``` + +2. **Chat Completions**: `POST /v1/chat/completions` + ```json + { + "model": "model-name", + "messages": [{"role": "user", "content": "Hello"}] + } + ``` + +### Response Format + +Chat completions should return: +```json +{ + "choices": [ + { + "message": { + "role": "assistant", + "content": "Response text" + }, + "finish_reason": "stop" + } + ] +} +``` + +## 🤝 Getting Help + +- **API not working?** Use the compatibility test script first +- **Authentication issues?** Check API documentation for auth format +- **Model errors?** Verify model names with the provider's model list +- **Performance issues?** Monitor logs and adjust timeouts + +Most hosted LLM services that claim "OpenAI compatibility" should work with minimal configuration! \ No newline at end of file diff --git a/OPENSHIFT-DEPLOYMENT.md b/OPENSHIFT-DEPLOYMENT.md new file mode 100644 index 000000000..7901ad63e --- /dev/null +++ b/OPENSHIFT-DEPLOYMENT.md @@ -0,0 +1,312 @@ +# Deploying LLAMA Stack to OpenShift with External Model Providers + +This guide shows you how to deploy LLAMA Stack to OpenShift using external model providers instead of hosting your own GPU-based models. + +## 🎯 Overview + +Instead of using Ollama locally, this deployment connects to external model providers like: +- **OpenAI** (GPT-4o, GPT-4o-mini) +- **Azure OpenAI** (Your deployed models) +- **External vLLM services** (Hosted elsewhere) +- **HuggingFace TGI endpoints** (Hosted services) +- **Custom APIs** (Mistral, Gemini, Claude, RHOAI models, etc.) + +## 🚀 Quick Start + +### Prerequisites + +1. OpenShift cluster access with `oc` CLI configured +2. Appropriate permissions to create namespaces, deployments, secrets, and routes +3. API credentials for your chosen provider: + - **OpenAI**: API key from https://platform.openai.com/api-keys + - **Azure OpenAI**: API key, endpoint, and deployment names + - **vLLM**: URL of your hosted vLLM service + +### 1. Clone and Prepare + +```bash +git clone +cd rhoai-ai-feature-sizing + +# Make deployment script executable +chmod +x deploy-to-openshift.sh +``` + +### 2. Deploy to OpenShift + +Run the deployment script: + +```bash +./deploy-to-openshift.sh +``` + +The script will prompt you for: +- **API URL** (e.g., OpenAI, Mistral, Gemini, Azure OpenAI, etc.) +- **API Key** (if required) +- **Model Name** (for LLAMA Stack) +- **Model ID** (actual model used by the API) +- **Jira credentials** + +The script will: +- Create the `llama-stack` namespace +- Prompt you for API details and credentials +- Deploy LLAMA Stack configured for your API +- Deploy the Jira MCP service +- Create routes for external access + +### 3. Update Your Environment + +After deployment, update your local `.env` file: + +```bash +cp env.openshift.example .env +# Edit .env with the URL provided by the deployment script +``` + +### 4. Test Your Deployment + +```bash +# Test the health endpoint +curl http://your-llama-stack-route-url/v1/health + +# Test with your existing CLI +uv run python -m rhoai_ai_feature_sizing.main stage refine PROJ-123 +``` + +## 🛠️ Manual Deployment + +If you prefer manual deployment or need to customize the configuration: + +### 1. Create Namespace +```bash +oc create namespace llama-stack +``` + +### 2. Create Secrets + +```bash +# Create API secrets +oc create secret generic llama-stack-secrets \ + --from-literal=CUSTOM_API_BASE_URL="https://your-api.com/v1" \ + --from-literal=CUSTOM_API_KEY="your-api-key" \ + --from-literal=CUSTOM_MODEL_NAME="your-model" \ + --from-literal=CUSTOM_MODEL_ID="actual-model-id" \ + -n llama-stack + +# Create Jira secrets +oc create secret generic jira-secrets \ + --from-literal=JIRA_URL="https://your-company.atlassian.net" \ + --from-literal=JIRA_API_TOKEN="your-token" \ + --from-literal=JIRA_USERNAME="your-username" \ + -n llama-stack +``` + +### 3. Deploy Configuration + +```bash +oc apply -f openshift-deployment.yaml +``` + +## 📊 Supported APIs + +LLAMA Stack works with **any OpenAI-compatible API endpoint**. Examples include: + +### Popular API Providers +- **OpenAI**: `https://api.openai.com/v1` (GPT-4o, GPT-4o-mini) +- **Azure OpenAI**: `https://your-resource.openai.azure.com/` (Your deployed models) +- **Mistral**: `https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443` +- **Anthropic Claude**: Via compatible proxy/gateway +- **Google Gemini**: Via compatible proxy/gateway +- **RHOAI Models**: Red Hat AI Services hosted models +- **Self-hosted vLLM/TGI**: Your own inference servers +- **HuggingFace Endpoints**: Hosted inference endpoints + +### Requirements +- **OpenAI-compatible API** with `/v1/chat/completions` endpoint +- **Optional authentication** (API key or no auth) +- **Model availability** via `/v1/models` endpoint (recommended) + +See [Custom API Examples](CUSTOM-API-EXAMPLES.md) for specific configuration examples. + +## 🔧 Configuration Details + +### LLAMA Stack Configuration + +The configuration is defined in `run.yaml` within the ConfigMap. Key sections: + +- **Providers**: Defines which inference provider to use +- **Models**: Maps model IDs to provider-specific model names +- **APIs**: Enabled APIs (agents, inference, safety, telemetry) + +### Environment Variables + +The deployment uses these environment variables for any API: + +**API Configuration:** +- `CUSTOM_API_BASE_URL`: Base URL of your API endpoint +- `CUSTOM_API_KEY`: API key (if required) +- `CUSTOM_MODEL_NAME`: Model name for LLAMA Stack +- `CUSTOM_MODEL_ID`: Actual model ID used by the API + +**Examples:** +- **OpenAI**: `https://api.openai.com/v1`, `your-api-key`, `gpt-4o-mini`, `gpt-4o-mini` +- **Mistral**: `https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443`, `[none]`, `mistral-small`, `mistral-small` +- **Azure OpenAI**: `https://your-resource.openai.azure.com/`, `your-api-key`, `gpt-4o-mini`, `your-deployment-name` + +## 🚦 Monitoring and Troubleshooting + +### Check Deployment Status + +```bash +# Check pod status +oc get pods -n llama-stack + +# Check logs +oc logs deployment/llama-stack -n llama-stack +oc logs deployment/jira-mcp -n llama-stack + +# Check routes +oc get routes -n llama-stack +``` + +### Common Issues + +1. **Pods not starting**: Check logs for credential or configuration issues +2. **Route not accessible**: Verify route creation and DNS resolution +3. **Model not found**: Verify model names match your provider's available models +4. **API errors**: Check API keys and network connectivity + +### Port Forward for Local Testing + +If routes aren't working, use port-forward: + +```bash +# LLAMA Stack +oc port-forward svc/llama-stack-service 8321:8321 -n llama-stack + +# Jira MCP +oc port-forward svc/jira-mcp-service 9000:9000 -n llama-stack +``` + +## 🔄 Switching Providers + +To switch to a different provider: + +1. Delete the current deployment: + ```bash + oc delete all -l app=llama-stack -n llama-stack + oc delete secrets llama-stack-secrets* -n llama-stack + ``` + +2. Deploy with new provider: + ```bash + ./deploy-to-openshift.sh + ``` + +## 📈 Scaling and Performance + +### Resource Requests and Limits + +The deployments include resource specifications: +- **LLAMA Stack**: 512Mi-1Gi memory, 250m-500m CPU +- **Jira MCP**: 256Mi-512Mi memory, 100m-200m CPU + +Adjust these based on your usage patterns and cluster resources. + +### High Availability + +For production deployments: + +1. Increase replica count: + ```yaml + spec: + replicas: 3 # Instead of 1 + ``` + +2. Add pod disruption budgets +3. Configure anti-affinity rules +4. Use persistent volumes for SQLite stores + +## 🔐 Security Considerations + +1. **Secrets Management**: All credentials are stored in OpenShift secrets +2. **Network Policies**: Consider implementing network policies to restrict traffic +3. **RBAC**: Use appropriate service accounts and RBAC rules +4. **TLS**: Enable TLS for routes in production +5. **Image Security**: Use trusted image registries and scan for vulnerabilities + +## 🎛️ Advanced Configuration + +### Custom Models + +To add custom models, edit the ConfigMap: + +```yaml +models: + - metadata: {} + model_id: custom-model + provider_id: your-provider + provider_model_id: provider-specific-name + model_type: llm +``` + +### Authentication + +To enable authentication on LLAMA Stack, add to the server configuration: + +```yaml +server: + port: 8321 + auth: + provider_config: + type: "oauth2_token" + jwks: + uri: "https://your-auth-provider/jwks" +``` + +### Persistent Storage + +For production, use persistent volumes: + +```yaml +volumeMounts: +- name: storage + mountPath: /data +volumes: +- name: storage + persistentVolumeClaim: + claimName: llama-stack-pvc +``` + +## 📚 Next Steps + +1. **Test your deployment** with the existing CLI commands +2. **Monitor performance** and adjust resources as needed +3. **Set up monitoring** with Prometheus/Grafana if available +4. **Configure backups** for any persistent data +5. **Review security** settings for your environment + +## 🤝 External Provider Setup Guides + +### Setting up OpenAI +1. Go to https://platform.openai.com/api-keys +2. Create a new API key +3. Note the key for use in deployment + +### Setting up Azure OpenAI +1. Create an Azure OpenAI resource +2. Deploy models (GPT-4o, GPT-4o-mini) +3. Get the endpoint URL and API key +4. Note the deployment names + +### Setting up External vLLM +Refer to external guides like: +- [OpenShift AI vLLM examples](https://github.com/rh-aiservices-bu/llm-on-openshift) +- [Community vLLM deployments](https://github.com/rcarrat-AI/hftgi-llms) + +## 🐛 Getting Help + +- Check the [LLAMA Stack documentation](https://llama-stack.readthedocs.io/) +- Review OpenShift logs and events +- Open issues in the project repository +- Consult OpenShift documentation for cluster-specific issues \ No newline at end of file diff --git a/README.md b/README.md index 4d92bd8b2..ffaddba22 100644 --- a/README.md +++ b/README.md @@ -50,6 +50,18 @@ docker-compose ps ## 🚀 Running the CLI +### Deployment Options + +**Local Development (Current):** +- Uses Docker Compose with Ollama +- Requires local GPU or CPU inference +- See main instructions below + +**OpenShift Production (New):** +- Uses any OpenAI-compatible API (OpenAI, Mistral, Gemini, Azure OpenAI, vLLM, etc.) +- No local GPU required +- See [OpenShift Deployment Guide](OPENSHIFT-DEPLOYMENT.md) + ### Basic Usage ```bash diff --git a/deploy-to-openshift.sh b/deploy-to-openshift.sh new file mode 100755 index 000000000..a04cf8cfc --- /dev/null +++ b/deploy-to-openshift.sh @@ -0,0 +1,143 @@ +#!/bin/bash + +# Deploy LLAMA Stack to OpenShift with External API Provider +# Usage: ./deploy-to-openshift.sh + +set -e + +NAMESPACE="llama-stack" + +echo "🚀 Deploying LLAMA Stack to OpenShift..." + +# Function to encode base64 +encode_base64() { + echo -n "$1" | base64 -w 0 +} + +# Function to prompt for input +prompt_input() { + local prompt="$1" + local var_name="$2" + local secret=${3:-false} + + echo -n "$prompt: " + if [ "$secret" = true ]; then + read -s value + echo + else + read value + fi + + if [ -z "$value" ]; then + echo "❌ Error: $var_name cannot be empty" + exit 1 + fi + + eval "$var_name='$value'" +} + +# Create namespace +echo "📋 Creating namespace '$NAMESPACE'..." +oc create namespace $NAMESPACE --dry-run=client -o yaml | oc apply -f - + +echo "🔧 Configuring API provider..." + +echo "" +echo "📡 API Configuration:" +prompt_input "Enter your API base URL (e.g., https://api.openai.com/v1, https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443)" API_BASE_URL +prompt_input "Enter your API key (leave empty if not required)" API_KEY +prompt_input "Enter the model name for LLAMA Stack (e.g., gpt-4o-mini, mistral-small)" MODEL_NAME +prompt_input "Enter the actual model ID used by the API (often same as above, or 'auto')" MODEL_ID + +echo "" +echo "📋 Jira Configuration:" +prompt_input "Enter your Jira URL (e.g., https://your-company.atlassian.net)" JIRA_URL +prompt_input "Enter your Jira API Token" JIRA_API_TOKEN true +prompt_input "Enter your Jira Username" JIRA_USERNAME + +# Create secrets with base64 encoded values +echo "🔐 Creating secrets..." +cat </dev/null || echo "Route not available") + +if [ "$ROUTE_URL" != "Route not available" ]; then + echo "🌐 LLAMA Stack URL: http://$ROUTE_URL" + echo "🔧 Update your .env file with:" + echo "LLAMA_STACK_URL=http://$ROUTE_URL" + echo "MCP_ATLASSIAN_URL=http://jira-mcp-service.$NAMESPACE.svc.cluster.local:9000/sse" + echo "" + echo "📝 Your API configuration:" + echo " • API URL: $API_BASE_URL" + echo " • Model: $MODEL_NAME" + echo " • Has API Key: $([ -n "$API_KEY" ] && echo "Yes" || echo "No")" +else + echo "⚠️ Route not created. Access via port-forward:" + echo "oc port-forward svc/llama-stack-service 8321:8321 -n $NAMESPACE" +fi + +echo "" +echo "🧪 Test your deployment:" +echo "curl http://$ROUTE_URL/v1/health" +echo "" +echo "📚 Next steps:" +echo "1. Update your application's LLAMA_STACK_URL environment variable" +echo "2. Test the API endpoints" +echo "3. Run your existing CLI commands against the new endpoint" +echo "" +echo "🎯 Example API configurations that work with this setup:" +echo " • OpenAI: https://api.openai.com/v1 + gpt-4o-mini" +echo " • Mistral: https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443 + mistral-small" +echo " • Azure OpenAI: https://your-resource.openai.azure.com/ + your-deployment-name" +echo " • Any other OpenAI-compatible API" \ No newline at end of file diff --git a/openshift-deployment.yaml b/openshift-deployment.yaml new file mode 100644 index 000000000..95d4a8114 --- /dev/null +++ b/openshift-deployment.yaml @@ -0,0 +1,191 @@ +apiVersion: v1 +kind: Secret +metadata: + name: llama-stack-secrets + namespace: llama-stack +type: Opaque +data: + VLLM_API_TOKEN: "" + VLLM_URL: "" + VLLM_INFERENCE_MODEL: "" + SQLITE_STORE_DIR: "" + FILES_STORAGE_DIR: "" + MILVUS_DB_PATH: "" + MILVUS_KVSTORE_DB_PATH: "" + OTEL_SERVICE_NAME: "" + TELEMETRY_SINKS: "" + +--- +apiVersion: v1 +kind: ConfigMap +metadata: + name: llama-stack-config + namespace: llama-stack +data: + run.yaml: | + version: 2 + image_name: llamastack-rhoai-ai-feature-sizing + container_image: llamastack-rhoai-ai-feature-sizing + apis: + - inference + - safety + - agents + - vector_io + - tool_runtime + - telemetry + - files + providers: + inference: + - provider_id: vllm + provider_type: remote::vllm + config: + url: ${env.VLLM_URL} + api_token: ${env.VLLM_API_TOKEN:=fake} + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: + excluded_categories: [] + agents: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + persistence_store: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing}/responses_store.db + vector_io: + - provider_id: milvus + provider_type: inline::milvus + config: + db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing/milvus.db} + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing}/${env.MILVUS_KVSTORE_DB_PATH:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing/milvus_registry.db} + tool_runtime: + - provider_id: model-context-protocol + provider_type: remote::model-context-protocol + config: {} + telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: "${env.OTEL_SERVICE_NAME:=\u200B}" + sinks: ${env.TELEMETRY_SINKS:=console,sqlite} + sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing}/trace_store.db + files: + - provider_id: localfs + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing}/files_metadata.db + metadata_store: null + inference_store: null + models: + - metadata: {} + model_id: ${env.VLLM_INFERENCE_MODEL} + provider_id: vllm + model_type: llm + shields: [] + vector_dbs: [] + datasets: [] + scoring_fns: [] + benchmarks: [] + tool_groups: [] + logging: null + server: + port: 8321 + tls_certfile: null + tls_keyfile: null + tls_cafile: null + auth: null + host: null + quota: null +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: llama-stack + namespace: llama-stack +spec: + replicas: 1 + selector: + matchLabels: + app: llama-stack + template: + metadata: + labels: + app: llama-stack + spec: + containers: + - name: llama-stack + image: llamastack/distribution-starter:latest + ports: + - containerPort: 8321 + env: + - name: VLLM_URL + valueFrom: + secretKeyRef: + name: llama-stack-secrets + key: VLLM_URL + - name: VLLM_API_TOKEN + valueFrom: + secretKeyRef: + name: llama-stack-secrets + key: CUSTOM_API_KEY + optional: true + - name: VLLM_INFERENCE_MODEL + valueFrom: + secretKeyRef: + name: llama-stack-secrets + key: VLLM_INFERENCE_MODEL + - name: SQLITE_STORE_DIR + value: "/tmp" + - name: MILVUS_DB_PATH + value: "/tmp/milvus.db" + - name: MILVUS_KVSTORE_DB_PATH + value: "/tmp/milvus_registry.db" + - name: OTEL_SERVICE_NAME + value: "llama-stack" + - name: TELEMETRY_SINKS + value: "console,sqlite" + - name: FILES_STORAGE_DIR + value: "/tmp/files" + resources: + requests: + memory: "512Mi" + cpu: "250m" + limits: + memory: "1Gi" + cpu: "500m" +--- +apiVersion: v1 +kind: Service +metadata: + name: llama-stack-service + namespace: llama-stack +spec: + selector: + app: llama-stack + ports: + - port: 8321 + targetPort: 8321 + type: ClusterIP +--- +apiVersion: route.openshift.io/v1 +kind: Route +metadata: + name: llama-stack-route + namespace: llama-stack +spec: + to: + kind: Service + name: llama-stack-service + port: + targetPort: 8321 \ No newline at end of file From c049ff3a627fc3f97b1f4662c40f3f1918ca558c Mon Sep 17 00:00:00 2001 From: emarion1 <59747212+emarion1@users.noreply.github.com> Date: Wed, 9 Jul 2025 11:09:55 -0500 Subject: [PATCH 004/240] Update draft_jiras.md Added additional guidance to Soft Mode instructions including sizing guidelines and cross-ticket dependency guidelines --- .../prompts/draft_jiras.md | 23 +++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md b/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md index c63430bce..d11847d94 100644 --- a/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md +++ b/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md @@ -13,11 +13,26 @@ Analyze the provided RHOAI feature document and create a comprehensive structure Generate ticket structures in **soft mode** - do NOT create actual Jira tickets. Instead, define the complete ticket structure with: - Ticket title and detailed description - Epic/Story/Task/Bug classifications -- Parent-child relationships -- Cross-ticket dependencies +- Acceptance Criteria using standard Acceptance Criteria Format: "Given [scenario], when [action performed],Then [expected state]" +- Parent-child relationships, including assigning appropriate Feature Jira Issues as Parent issue +- Cross-ticket dependencies, using following dependency types: + * Blocks / is blocked byOutward Description: Blocks – The source issue prevents the destination issue from starting or completing. +Inward Description: Is blocked by – The destination issue cannot proceed until the source issue is resolved. Use Case: Commonly used for process dependencies where one task must be completed before another can start (e.g., a "Finish-to-Start" dependency). + * Relates to / relates toOutward Description: Relates to – The source issue has a general relationship with the destination issue. Inward Description: Relates to – The destination issue is related to the source issue. Use Case: Used for loose associations where issues are connected but not necessarily dependent (e.g., for context or shared relevance). This link type may not always indicate a strict dependency, so use it cautiously for dependency management. - Component assignments -- Story point estimates +- Story point estimates using ticket sizing guidelines: + * decompose into child user stories or tasks ensuring that each is appropriately sized for completion within a three-week sprint. + * Story points are in Fibonacci sequence: 0, 1, 2, 3, 5, 8 and 13. + * Story points do not exactly reflect the time the ticket will take, but reflect more the complexity of the ticket and overall effort required. If the ticket is 8 or 13, consider dividing it into smaller tickets. +- For Feature estimation, the points of all child tickets shall be added up to provide a Feature story point estimate which will be combined with implementation plan timeline. - Priority levels +- upstream open source applicability +- tech debt identification +- detailed test steps +- required documentation + +## Ticket sequencing +Sequence the tickets in chronological order within appicable epic accounting for cross-ticket dependencies ## Examples @@ -449,4 +464,4 @@ Analyze the provided document and create a Jira tickets structure following this - Use "soft mode" - do NOT create actual Jira tickets, only define the structure - Return ONLY the complete markdown structure -Begin by analyzing the provided RHOAI feature document and generating the complete Jira tickets structure. \ No newline at end of file +Begin by analyzing the provided RHOAI feature document and generating the complete Jira tickets structure. From 579c5d5663ceeec169d4f7b955f5abc02a1166f8 Mon Sep 17 00:00:00 2001 From: Gage Krumbach Date: Wed, 9 Jul 2025 14:31:20 -0500 Subject: [PATCH 005/240] Update environment configuration and deployment scripts for Jira MCP integration. Add MCP Atlassian URL to .env.example, enhance deploy-to-openshift.sh with new secrets, and modify openshift-deployment.yaml to include Jira MCP service and deployment. Refactor llama_stack_setup.py to utilize environment variables for configuration. Improve logging for toolgroup registration process. --- .env.example | 14 ++- deploy-to-openshift.sh | 12 +++ openshift-deployment.yaml | 101 ++++++++++++++---- .../llama_stack_setup.py | 43 ++++---- 4 files changed, 125 insertions(+), 45 deletions(-) diff --git a/.env.example b/.env.example index 3a40d4b5c..af9e4bfc6 100644 --- a/.env.example +++ b/.env.example @@ -16,11 +16,19 @@ JIRA_API_TOKEN= JIRA_USERNAME=username@redhat.com # ============================================================================= -# LLAMA STACK OLLAMA CONFIGURATION +# MCP Config +# ============================================================================= +# Atlassian mcp url +MCP_ATLASSIAN_URL=http://jira-mcp:9000/sse + +# ============================================================================= +# LLAMA STACK CONFIGURATION # ============================================================================= # Ollama model to use (must be pulled/available in your Ollama instance) INFERENCE_MODEL=llama3.2:3b +LLAMA_STACK_URL=http://localhost:8321 + # Ollama server URL (running locally or on another host) OLLAMA_URL=http://host.docker.internal:11434 @@ -33,10 +41,6 @@ LOG_LEVEL=INFO # Enable debug mode for the Python application DEBUG=false -# ============================================================================= -# DOCKER CONFIGURATION -# ============================================================================= - # ============================================================================= # APPLICATION SETTINGS # ============================================================================= diff --git a/deploy-to-openshift.sh b/deploy-to-openshift.sh index a04cf8cfc..523dfee21 100755 --- a/deploy-to-openshift.sh +++ b/deploy-to-openshift.sh @@ -65,6 +65,8 @@ metadata: namespace: $NAMESPACE type: Opaque data: + VLLM_URL: $(encode_base64 "$API_BASE_URL") + VLLM_INFERENCE_MODEL: $(encode_base64 "$MODEL_ID") CUSTOM_API_BASE_URL: $(encode_base64 "$API_BASE_URL") CUSTOM_MODEL_NAME: $(encode_base64 "$MODEL_NAME") CUSTOM_MODEL_ID: $(encode_base64 "$MODEL_ID") @@ -75,6 +77,7 @@ if [ -n "$API_KEY" ]; then echo "🔑 Adding API key..." oc patch secret llama-stack-secrets -n $NAMESPACE --patch="$(cat < Date: Fri, 11 Jul 2025 08:12:04 -0500 Subject: [PATCH 006/240] Implement deployment configuration for rhoai-ai-feature-sizing-api, including Kubernetes deployment, service, and route definitions in openshift-deployment.yaml. Update pyproject.toml and uv.lock to include new dependencies: sqlalchemy, psycopg2-binary, and pydantic. Modify llama_stack_setup.py to use CONFIGURE_TOOLGROUPS environment variable for toolgroup configuration. --- API.md | 419 +++++++++++++++ openshift-deployment.yaml | 86 +++ pyproject.toml | 3 + src/rhoai_ai_feature_sizing/api/__init__.py | 1 + src/rhoai_ai_feature_sizing/api/main.py | 356 +++++++++++++ src/rhoai_ai_feature_sizing/api/models.py | 230 ++++++++ src/rhoai_ai_feature_sizing/api/schemas.py | 129 +++++ src/rhoai_ai_feature_sizing/api/services.py | 500 ++++++++++++++++++ .../llama_stack_setup.py | 4 +- src/rhoai_ai_feature_sizing/run_api.py | 56 ++ test_api.py | 274 ++++++++++ uv.lock | 99 ++++ 12 files changed, 2155 insertions(+), 2 deletions(-) create mode 100644 API.md create mode 100644 src/rhoai_ai_feature_sizing/api/__init__.py create mode 100644 src/rhoai_ai_feature_sizing/api/main.py create mode 100644 src/rhoai_ai_feature_sizing/api/models.py create mode 100644 src/rhoai_ai_feature_sizing/api/schemas.py create mode 100644 src/rhoai_ai_feature_sizing/api/services.py create mode 100644 src/rhoai_ai_feature_sizing/run_api.py create mode 100644 test_api.py diff --git a/API.md b/API.md new file mode 100644 index 000000000..b3c5a270d --- /dev/null +++ b/API.md @@ -0,0 +1,419 @@ +# RHOAI AI Feature Sizing API + +This FastAPI-based service provides a web API for the RHOAI AI Feature Sizing tool, allowing you to process Jira issues through multiple AI-powered stages with session management and progress tracking. + +## Features + +- **Session Management**: Create and track long-running processing sessions +- **Progress Tracking**: Real-time progress updates via polling endpoints +- **Chat History**: All agent communications are stored as messages +- **Output Storage**: Generated markdown files are stored in the database +- **MCP Usage Tracking**: Monitor MCP tool usage for analytics +- **Health Checks**: Monitor service and dependency status + +## Quick Start + +### Local Development + +1. **Install dependencies**: + ```bash + # Using uv (recommended) + uv pip install -e . + + # Or sync from lock file + uv sync + ``` + +2. **Set environment variables**: + + **Option A: Use your deployed cluster (recommended)** + ```bash + # Get your cluster's Llama Stack URL + CLUSTER_LLAMA_STACK=$(oc get route llama-stack-route -n llama-stack -o jsonpath='{.spec.host}') + + # Create simple .env file + cat > .env << EOF + LLAMA_STACK_URL=https://$CLUSTER_LLAMA_STACK + PORT=8000 + LOG_LEVEL=INFO + SQLITE_DB_PATH=./data/rhoai_sessions.db + EOF + ``` + + **Option B: Full local setup** + ```bash + # RHOAI AI Feature Sizing API - Environment Configuration + + # Llama Stack service URL (where your Llama Stack is running) + LLAMA_STACK_URL=http://localhost:8321 + + # VLLM inference service URL (your actual LLM endpoint) + VLLM_URL=https://your-vllm-endpoint.example.com/v1 + + # VLLM API token (if required by your endpoint) + VLLM_API_TOKEN=your-api-token-here + + # Model name for inference (e.g., "llama-3.1-70b-instruct", "mistral-7b") + VLLM_INFERENCE_MODEL=llama-3.1-70b-instruct + INFERENCE_MODEL=llama-3.1-70b-instruct + + # API server configuration + HOST=0.0.0.0 + PORT=8000 + LOG_LEVEL=INFO + + # Database (SQLite for development) + SQLITE_DB_PATH=./data/rhoai_sessions.db + + # Optional - Jira/MCP Configuration (for hard mode) + # JIRA_URL=https://your-company.atlassian.net + # JIRA_USERNAME=your-email@company.com + # JIRA_API_TOKEN=your-jira-api-token + # MCP_ATLASSIAN_URL=http://localhost:9000/sse + ``` + + **Simple setup using your deployed cluster:** + ```bash + # Just point to your OpenShift Llama Stack - that's it! + + # Get your cluster's Llama Stack URL + LLAMA_STACK_URL=https://$(oc get route llama-stack-route -n llama-stack -o jsonpath='{.spec.host}') + + # API config + PORT=8000 + LOG_LEVEL=INFO + + # Database (local SQLite for development) + SQLITE_DB_PATH=./data/rhoai_sessions.db + ``` + + **Alternative endpoint examples (if not using cluster):** + ```bash + # Local Ollama + LLAMA_STACK_URL=http://localhost:8321 + VLLM_URL=http://localhost:11434/v1 + VLLM_INFERENCE_MODEL=llama3.1:8b + + # OpenAI + LLAMA_STACK_URL=http://localhost:8321 + VLLM_URL=https://api.openai.com/v1 + VLLM_API_TOKEN=sk-your-openai-api-key + VLLM_INFERENCE_MODEL=gpt-4o-mini + ``` + +3. **Run the API server**: + ```bash + # Method 1: Using uv with .env file (recommended) + uv run --env-file .env python -m rhoai_ai_feature_sizing.run_api + + # Method 2: Using uv with auto-reload for development + uv run --env-file .env uvicorn rhoai_ai_feature_sizing.api.main:app --reload --host 0.0.0.0 --port 8000 + + # Method 3: Export variables and run + export $(cat .env | xargs) && python -m rhoai_ai_feature_sizing.run_api + + # Method 4: Direct uvicorn + uv run uvicorn rhoai_ai_feature_sizing.api.main:app --host 0.0.0.0 --port 8000 + ``` + +4. **Access the API**: + - API: http://localhost:8000 + - Interactive docs: http://localhost:8000/docs + - OpenAPI spec: http://localhost:8000/openapi.json + +### OpenShift Deployment + +The API is included in the OpenShift deployment. After deploying: + +```bash +# Deploy all services +./deploy-to-openshift.sh + +# Get the API route URL +oc get route rhoai-ai-feature-sizing-api-route -n llama-stack -o jsonpath='{.spec.host}' +``` + +## 🔧 Development Tips + +### **Quick Development Workflow (using your cluster)** + +```bash +# 1. Install dependencies with uv +uv pip install -e . + +# 2. Get your cluster's Llama Stack URL +CLUSTER_LLAMA_STACK=$(oc get route llama-stack-route -n llama-stack -o jsonpath='{.spec.host}') +echo "Llama Stack URL: https://$CLUSTER_LLAMA_STACK" + +# 3. Create simple .env file +cat > .env << EOF +LLAMA_STACK_URL=https://$CLUSTER_LLAMA_STACK +PORT=8000 +LOG_LEVEL=INFO +SQLITE_DB_PATH=./data/rhoai_sessions.db +EOF + +# 4. Run with auto-reload for development +uv run --env-file .env uvicorn rhoai_ai_feature_sizing.api.main:app --reload --host 0.0.0.0 --port 8000 + +# 5. Test the API +python test_api.py --url http://localhost:8000 --jira-key YOUR-JIRA-KEY --wait +``` + +### **Alternative Run Methods** + +```bash +# Production-like (no auto-reload) +uv run --env-file .env python -m rhoai_ai_feature_sizing.run_api + +# Without .env file (export manually) +export LLAMA_STACK_URL=http://localhost:8321 +export VLLM_URL=your-endpoint +export VLLM_INFERENCE_MODEL=your-model +uv run python -m rhoai_ai_feature_sizing.run_api + +# Debug mode with verbose logging +LOG_LEVEL=DEBUG uv run --env-file .env python -m rhoai_ai_feature_sizing.run_api +``` + +### **Testing Commands** + +```bash +# Quick health check +curl http://localhost:8000/healthz + +# Full API test with real Jira issue +python test_api.py --url http://localhost:8000 --jira-key RHOAI-1234 --wait + +# Just test basic endpoints (no processing) +python test_api.py --url http://localhost:8000 --jira-key TEST-123 +``` + +### **Database Management** + +```bash +# SQLite database location (default) +ls -la /tmp/rhoai_sessions.db + +# Or custom location from .env +ls -la ./data/rhoai_sessions.db + +# View database schema (if you have sqlite3 installed) +sqlite3 /tmp/rhoai_sessions.db ".schema" +``` + +## API Endpoints + +### Health Check + +```http +GET /healthz +``` + +Returns the health status of the API, database, and Llama Stack connections. + +### Session Management + +#### Create Session + +```http +POST /sessions +Content-Type: application/json + +{ + "jira_key": "PROJ-123", + "soft_mode": true +} +``` + +Creates a new processing session and starts background processing. + +#### List Sessions + +```http +GET /sessions?page=1&page_size=20 +``` + +Get paginated list of all sessions. + +#### Get Session Details + +```http +GET /sessions/{session_id} +``` + +Get complete session information including messages, outputs, and MCP usage. + +#### Get Session Progress + +```http +GET /sessions/{session_id}/progress +``` + +Get current progress status for polling. Returns: +- Current status (pending, running, completed, failed) +- Current stage (refine, epics, jiras, estimate) +- Progress percentage (0-100) +- Latest message +- Error information (if failed) + +### Messages (Chat History) + +```http +GET /sessions/{session_id}/messages?limit=50&stage=refine +``` + +Get chat messages for a session, optionally filtered by stage. + +### Outputs (Generated Files) + +```http +GET /sessions/{session_id}/outputs +GET /sessions/{session_id}/outputs/{stage} +``` + +Get generated markdown outputs, either all outputs or for a specific stage. + +### MCP Usage Analytics + +```http +GET /sessions/{session_id}/mcp-usage +``` + +Get MCP tool usage statistics for analytics. + +## Processing Stages + +The API processes Jira issues through four stages: + +1. **Refine** (25% progress): Generate detailed feature specification +2. **Epics** (50% progress): Create epic breakdown (placeholder) +3. **Jiras** (75% progress): Draft Jira ticket structure +4. **Estimate** (90% progress): Generate estimates (placeholder) + +## Soft Mode vs Hard Mode + +- **Soft Mode** (default): Generate ticket structure without creating actual Jira tickets +- **Hard Mode**: Create actual Jira tickets using MCP tools + +## Usage Examples + +### JavaScript/TypeScript + +```javascript +// Create a session +const response = await fetch('/sessions', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + jira_key: 'PROJ-123', + soft_mode: true + }) +}); +const session = await response.json(); + +// Poll for progress +const pollProgress = async (sessionId) => { + const response = await fetch(`/sessions/${sessionId}/progress`); + const progress = await response.json(); + console.log(`Progress: ${progress.progress_percentage}%`); + + if (progress.status === 'running') { + setTimeout(() => pollProgress(sessionId), 2000); + } +}; + +pollProgress(session.id); + +// Get messages for chat display +const messagesResponse = await fetch(`/sessions/${session.id}/messages`); +const messages = await messagesResponse.json(); + +// Get outputs for markdown display +const outputsResponse = await fetch(`/sessions/${session.id}/outputs`); +const outputs = await outputsResponse.json(); +``` + +### Python + +```python +import requests +import time + +# Create session +response = requests.post('/sessions', json={ + 'jira_key': 'PROJ-123', + 'soft_mode': True +}) +session = response.json() + +# Poll for completion +while True: + progress = requests.get(f'/sessions/{session["id"]}/progress').json() + print(f"Progress: {progress['progress_percentage']}%") + + if progress['status'] in ['completed', 'failed']: + break + + time.sleep(2) + +# Get results +outputs = requests.get(f'/sessions/{session["id"]}/outputs').json() +for output in outputs: + print(f"Stage {output['stage']}: {output['filename']}") + print(output['content'][:200] + "...") +``` + +### cURL + +```bash +# Create session +curl -X POST http://localhost:8000/sessions \ + -H "Content-Type: application/json" \ + -d '{"jira_key": "PROJ-123", "soft_mode": true}' + +# Check progress +curl http://localhost:8000/sessions/{session_id}/progress + +# Get messages +curl http://localhost:8000/sessions/{session_id}/messages + +# Get outputs +curl http://localhost:8000/sessions/{session_id}/outputs +``` + +## Database + +The API uses SQLAlchemy with support for: +- **Development**: SQLite (default) +- **Production**: PostgreSQL (via DATABASE_URL environment variable) + +### Environment Variables + +- `DATABASE_URL`: PostgreSQL connection string (optional, defaults to SQLite) +- `SQLITE_DB_PATH`: SQLite database file path (default: `/tmp/rhoai_sessions.db`) +- `LLAMA_STACK_URL`: Llama Stack service URL +- `VLLM_URL`: VLLM inference service URL +- `VLLM_INFERENCE_MODEL`: Model name for inference +- `INFERENCE_MODEL`: Alias for VLLM_INFERENCE_MODEL +- `PORT`: API server port (default: 8000) +- `HOST`: API server host (default: 0.0.0.0) +- `LOG_LEVEL`: Logging level (default: INFO) + +## Error Handling + +The API provides comprehensive error handling: +- 404: Session not found +- 500: Internal server errors with detailed messages +- 503: Service not initialized + +All errors include descriptive messages to help with debugging. + +## Future Enhancements + +- WebSocket support for real-time updates +- Session resumption and stage selection +- Advanced filtering and search +- Metrics and monitoring endpoints +- Authentication and authorization +- File upload/download endpoints \ No newline at end of file diff --git a/openshift-deployment.yaml b/openshift-deployment.yaml index b9f1e4820..7975df61c 100644 --- a/openshift-deployment.yaml +++ b/openshift-deployment.yaml @@ -237,6 +237,92 @@ spec: targetPort: 9000 type: ClusterIP --- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: rhoai-ai-feature-sizing-api + namespace: llama-stack +spec: + replicas: 1 + selector: + matchLabels: + app: rhoai-ai-feature-sizing-api + template: + metadata: + labels: + app: rhoai-ai-feature-sizing-api + spec: + containers: + - name: rhoai-ai-feature-sizing-api + image: llamastack/distribution-starter:latest + command: ["python", "-m", "rhoai_ai_feature_sizing.run_api"] + ports: + - containerPort: 8000 + env: + - name: PORT + value: "8000" + - name: HOST + value: "0.0.0.0" + - name: DATABASE_URL + value: "sqlite:///tmp/rhoai_sessions.db" + - name: LLAMA_STACK_URL + value: "http://llama-stack-service.llama-stack.svc.cluster.local:8321" + - name: VLLM_URL + valueFrom: + secretKeyRef: + name: llama-stack-secrets + key: VLLM_URL + - name: VLLM_API_TOKEN + valueFrom: + secretKeyRef: + name: llama-stack-secrets + key: VLLM_API_TOKEN + optional: true + - name: VLLM_INFERENCE_MODEL + valueFrom: + secretKeyRef: + name: llama-stack-secrets + key: VLLM_INFERENCE_MODEL + - name: INFERENCE_MODEL + valueFrom: + secretKeyRef: + name: llama-stack-secrets + key: VLLM_INFERENCE_MODEL + - name: LOG_LEVEL + value: "INFO" + resources: + requests: + memory: "512Mi" + cpu: "250m" + limits: + memory: "1Gi" + cpu: "500m" +--- +apiVersion: v1 +kind: Service +metadata: + name: rhoai-ai-feature-sizing-api-service + namespace: llama-stack +spec: + selector: + app: rhoai-ai-feature-sizing-api + ports: + - port: 8000 + targetPort: 8000 + type: ClusterIP +--- +apiVersion: route.openshift.io/v1 +kind: Route +metadata: + name: rhoai-ai-feature-sizing-api-route + namespace: llama-stack +spec: + to: + kind: Service + name: rhoai-ai-feature-sizing-api-service + port: + targetPort: 8000 +--- apiVersion: route.openshift.io/v1 kind: Route metadata: diff --git a/pyproject.toml b/pyproject.toml index 97130e662..3c0cf2bf3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,9 @@ dependencies = [ "fastapi>=0.104.0", "uvicorn>=0.24.0", "python-multipart>=0.0.6", + "sqlalchemy>=2.0.0", + "psycopg2-binary>=2.9.0", + "pydantic>=2.0.0", ] [build-system] diff --git a/src/rhoai_ai_feature_sizing/api/__init__.py b/src/rhoai_ai_feature_sizing/api/__init__.py new file mode 100644 index 000000000..6b2c44481 --- /dev/null +++ b/src/rhoai_ai_feature_sizing/api/__init__.py @@ -0,0 +1 @@ +# API package for RHOAI AI Feature Sizing diff --git a/src/rhoai_ai_feature_sizing/api/main.py b/src/rhoai_ai_feature_sizing/api/main.py new file mode 100644 index 000000000..2102faf37 --- /dev/null +++ b/src/rhoai_ai_feature_sizing/api/main.py @@ -0,0 +1,356 @@ +"""FastAPI application for RHOAI AI Feature Sizing.""" + +import logging +import uuid +import asyncio +from datetime import datetime +from typing import List, Optional + +from fastapi import FastAPI, HTTPException, BackgroundTasks, Query, Depends +from fastapi.middleware.cors import CORSMiddleware +from contextlib import asynccontextmanager + +from .models import init_database, SessionStatus, Stage +from .schemas import ( + CreateSessionRequest, + SessionResponse, + SessionDetailResponse, + MessageResponse, + OutputResponse, + MCPUsageResponse, + HealthResponse, + ProgressResponse, + SessionListResponse, +) +from .services import SessionService +from ..llama_stack_setup import get_llama_stack_client + + +# Global service instance +session_service = None + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Application lifespan manager.""" + global session_service + + # Startup + logging.info("Initializing database...") + init_database() + + logging.info("Creating session service...") + session_service = SessionService() + + logging.info("API startup complete") + + yield + + # Shutdown + logging.info("API shutdown complete") + + +# Create FastAPI app +app = FastAPI( + title="RHOAI AI Feature Sizing API", + description="API for transforming Jira features into detailed specs, epics, and estimates using AI agents", + version="1.0.0", + lifespan=lifespan, +) + +# Add CORS middleware +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # Configure appropriately for production + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +def get_session_service() -> SessionService: + """Dependency to get session service.""" + if session_service is None: + raise HTTPException(status_code=503, detail="Service not initialized") + return session_service + + +@app.get("/healthz", response_model=HealthResponse) +async def health_check(): + """Health check endpoint.""" + # Test database connection + try: + service = get_session_service() + service.get_sessions(page=1, page_size=1) + database_status = "connected" + except Exception as e: + database_status = f"error: {str(e)}" + + # Test Llama Stack connection + try: + client = get_llama_stack_client() + # Simple test - this might need adjustment based on your client + llama_stack_status = "connected" + except Exception as e: + llama_stack_status = f"error: {str(e)}" + + return HealthResponse(database=database_status, llama_stack=llama_stack_status) + + +@app.post("/sessions", response_model=SessionResponse) +async def create_session( + request: CreateSessionRequest, + background_tasks: BackgroundTasks, + service: SessionService = Depends(get_session_service), +): + """Create a new processing session and start processing.""" + try: + # Create session + session_id = service.create_session(request.jira_key, request.soft_mode) + + # Start processing in background + background_tasks.add_task(service.process_session, session_id) + + # Return session info + session = service.get_session(session_id) + return SessionResponse.model_validate(session) + + except Exception as e: + logging.error(f"Error creating session: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.get("/sessions", response_model=SessionListResponse) +async def list_sessions( + page: int = Query(1, ge=1, description="Page number"), + page_size: int = Query(20, ge=1, le=100, description="Items per page"), + service: SessionService = Depends(get_session_service), +): + """Get paginated list of sessions.""" + try: + sessions, total = service.get_sessions(page, page_size) + return SessionListResponse( + sessions=[SessionResponse.model_validate(s) for s in sessions], + total=total, + page=page, + page_size=page_size, + ) + except Exception as e: + logging.error(f"Error listing sessions: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.get("/sessions/{session_id}", response_model=SessionDetailResponse) +async def get_session( + session_id: uuid.UUID, service: SessionService = Depends(get_session_service) +): + """Get detailed session information.""" + try: + session = service.get_session(session_id) + if not session: + raise HTTPException(status_code=404, detail="Session not found") + + # Get related data + messages = service.get_session_messages(session_id) + outputs = service.get_session_outputs(session_id) + mcp_usages = service.get_session_mcp_usage(session_id) + + return SessionDetailResponse( + **SessionResponse.model_validate(session).model_dump(), + messages=[MessageResponse.model_validate(m) for m in messages], + outputs=[OutputResponse.model_validate(o) for o in outputs], + mcp_usages=[MCPUsageResponse.model_validate(u) for u in mcp_usages], + ) + except HTTPException: + raise + except Exception as e: + logging.error(f"Error getting session {session_id}: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.get("/sessions/{session_id}/progress", response_model=ProgressResponse) +async def get_session_progress( + session_id: uuid.UUID, service: SessionService = Depends(get_session_service) +): + """Get session progress for polling.""" + try: + session = service.get_session(session_id) + if not session: + raise HTTPException(status_code=404, detail="Session not found") + + # Calculate progress percentage based on stage + progress_map = { + None: 0, + Stage.REFINE: 25, + Stage.EPICS: 50, + Stage.JIRAS: 75, + Stage.ESTIMATE: 90, + } + + progress_percentage = progress_map.get(session.current_stage, 0) + if session.status == SessionStatus.COMPLETED: + progress_percentage = 100 + elif session.status == SessionStatus.FAILED: + progress_percentage = 0 + + # Get latest message + messages = service.get_session_messages(session_id, limit=1) + latest_message = messages[-1].content if messages else None + + return ProgressResponse( + session_id=session_id, + status=session.status, + current_stage=session.current_stage, + progress_percentage=progress_percentage, + latest_message=latest_message, + error_message=session.error_message, + started_at=session.started_at, + ) + except HTTPException: + raise + except Exception as e: + logging.error(f"Error getting progress for session {session_id}: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.get("/sessions/{session_id}/messages", response_model=List[MessageResponse]) +async def get_session_messages( + session_id: uuid.UUID, + limit: int = Query(50, ge=1, le=200, description="Maximum number of messages"), + stage: Optional[Stage] = Query(None, description="Filter by stage"), + service: SessionService = Depends(get_session_service), +): + """Get session messages (chat history).""" + try: + session = service.get_session(session_id) + if not session: + raise HTTPException(status_code=404, detail="Session not found") + + messages = service.get_session_messages(session_id, limit) + + # Filter by stage if requested + if stage: + messages = [m for m in messages if m.stage == stage] + + return [MessageResponse.model_validate(m) for m in messages] + except HTTPException: + raise + except Exception as e: + logging.error(f"Error getting messages for session {session_id}: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.get("/sessions/{session_id}/outputs", response_model=List[OutputResponse]) +async def get_session_outputs( + session_id: uuid.UUID, + stage: Optional[Stage] = Query(None, description="Filter by stage"), + service: SessionService = Depends(get_session_service), +): + """Get session outputs (markdown files).""" + try: + session = service.get_session(session_id) + if not session: + raise HTTPException(status_code=404, detail="Session not found") + + outputs = service.get_session_outputs(session_id) + + # Filter by stage if requested + if stage: + outputs = [o for o in outputs if o.stage == stage] + + return [OutputResponse.model_validate(o) for o in outputs] + except HTTPException: + raise + except Exception as e: + logging.error(f"Error getting outputs for session {session_id}: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.get("/sessions/{session_id}/outputs/{stage}", response_model=OutputResponse) +async def get_session_output_by_stage( + session_id: uuid.UUID, + stage: Stage, + service: SessionService = Depends(get_session_service), +): + """Get specific output by stage.""" + try: + session = service.get_session(session_id) + if not session: + raise HTTPException(status_code=404, detail="Session not found") + + outputs = service.get_session_outputs(session_id) + stage_output = next((o for o in outputs if o.stage == stage), None) + + if not stage_output: + raise HTTPException( + status_code=404, detail=f"No output found for stage {stage}" + ) + + return OutputResponse.model_validate(stage_output) + except HTTPException: + raise + except Exception as e: + logging.error( + f"Error getting output for session {session_id}, stage {stage}: {e}" + ) + raise HTTPException(status_code=500, detail=str(e)) + + +@app.get("/sessions/{session_id}/mcp-usage", response_model=List[MCPUsageResponse]) +async def get_session_mcp_usage( + session_id: uuid.UUID, service: SessionService = Depends(get_session_service) +): + """Get MCP usage analytics for a session.""" + try: + session = service.get_session(session_id) + if not session: + raise HTTPException(status_code=404, detail="Session not found") + + usages = service.get_session_mcp_usage(session_id) + return [MCPUsageResponse.model_validate(u) for u in usages] + except HTTPException: + raise + except Exception as e: + logging.error(f"Error getting MCP usage for session {session_id}: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.delete("/sessions/{session_id}") +async def delete_session( + session_id: uuid.UUID, service: SessionService = Depends(get_session_service) +): + """Delete a session and all related data.""" + try: + session = service.get_session(session_id) + if not session: + raise HTTPException(status_code=404, detail="Session not found") + + # TODO: Implement session deletion + # For now, just update status to cancelled + service.update_session_status(session_id, SessionStatus.CANCELLED) + + return {"message": "Session cancelled"} + except HTTPException: + raise + except Exception as e: + logging.error(f"Error deleting session {session_id}: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +if __name__ == "__main__": + import uvicorn + import os + + # Setup logging + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + ) + + # Run the application + port = int(os.getenv("PORT", 8000)) + host = os.getenv("HOST", "0.0.0.0") + + uvicorn.run( + "rhoai_ai_feature_sizing.api.main:app", host=host, port=port, reload=True + ) diff --git a/src/rhoai_ai_feature_sizing/api/models.py b/src/rhoai_ai_feature_sizing/api/models.py new file mode 100644 index 000000000..0c8c8ae43 --- /dev/null +++ b/src/rhoai_ai_feature_sizing/api/models.py @@ -0,0 +1,230 @@ +"""Database models for the API.""" + +import uuid +from datetime import datetime +from enum import Enum +from typing import Optional + +from sqlalchemy import ( + Boolean, + Column, + DateTime, + Enum as SQLEnum, + ForeignKey, + Integer, + String, + Text, + create_engine, +) +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.orm import relationship, sessionmaker +from sqlalchemy.dialects.postgresql import UUID +from sqlalchemy.types import TypeDecorator, CHAR +import os + +Base = declarative_base() + + +class GUID(TypeDecorator): + """Platform-independent GUID type. + Uses PostgreSQL's UUID type, otherwise uses CHAR(32), storing as stringified hex values. + """ + + impl = CHAR + cache_ok = True + + def load_dialect_impl(self, dialect): + if dialect.name == "postgresql": + return dialect.type_descriptor(UUID()) + else: + return dialect.type_descriptor(CHAR(32)) + + def process_bind_param(self, value, dialect): + if value is None: + return value + elif dialect.name == "postgresql": + return str(value) + else: + if not isinstance(value, uuid.UUID): + return "%.32x" % uuid.UUID(value).int + else: + return "%.32x" % value.int + + def process_result_value(self, value, dialect): + if value is None: + return value + else: + if not isinstance(value, uuid.UUID): + return uuid.UUID(value) + else: + return value + + +class SessionStatus(str, Enum): + """Status of a processing session.""" + + PENDING = "pending" + RUNNING = "running" + COMPLETED = "completed" + FAILED = "failed" + CANCELLED = "cancelled" + + +class Stage(str, Enum): + """Processing stages.""" + + REFINE = "refine" + EPICS = "epics" + JIRAS = "jiras" + ESTIMATE = "estimate" + + +class MessageRole(str, Enum): + """Message roles in chat history.""" + + SYSTEM = "system" + USER = "user" + ASSISTANT = "assistant" + AGENT = "agent" + + +class Session(Base): + """Main session tracking table.""" + + __tablename__ = "sessions" + + id = Column(GUID(), primary_key=True, default=uuid.uuid4) + jira_key = Column(String(50), nullable=False, index=True) + status = Column( + SQLEnum(SessionStatus), default=SessionStatus.PENDING, nullable=False + ) + current_stage = Column(SQLEnum(Stage), nullable=True) + soft_mode = Column(Boolean, default=True, nullable=False) + created_at = Column(DateTime, default=datetime.utcnow, nullable=False) + updated_at = Column( + DateTime, default=datetime.utcnow, onupdate=datetime.utcnow, nullable=False + ) + started_at = Column(DateTime, nullable=True) + completed_at = Column(DateTime, nullable=True) + error_message = Column(Text, nullable=True) + + # Relationships + messages = relationship( + "Message", back_populates="session", cascade="all, delete-orphan" + ) + outputs = relationship( + "Output", back_populates="session", cascade="all, delete-orphan" + ) + mcp_usages = relationship( + "MCPUsage", back_populates="session", cascade="all, delete-orphan" + ) + + def __repr__(self): + return ( + f"" + ) + + +class Message(Base): + """Chat messages and agent communications.""" + + __tablename__ = "messages" + + id = Column(GUID(), primary_key=True, default=uuid.uuid4) + session_id = Column(GUID(), ForeignKey("sessions.id"), nullable=False, index=True) + role = Column(SQLEnum(MessageRole), nullable=False) + content = Column(Text, nullable=False) + stage = Column(SQLEnum(Stage), nullable=True) + timestamp = Column(DateTime, default=datetime.utcnow, nullable=False) + message_metadata = Column(Text, nullable=True) # JSON string for additional data + + # Relationships + session = relationship("Session", back_populates="messages") + + def __repr__(self): + return f"" + + +class Output(Base): + """Generated markdown outputs from each stage.""" + + __tablename__ = "outputs" + + id = Column(GUID(), primary_key=True, default=uuid.uuid4) + session_id = Column(GUID(), ForeignKey("sessions.id"), nullable=False, index=True) + stage = Column(SQLEnum(Stage), nullable=False) + filename = Column(String(255), nullable=False) + content = Column(Text, nullable=False) + created_at = Column(DateTime, default=datetime.utcnow, nullable=False) + + # Relationships + session = relationship("Session", back_populates="outputs") + + def __repr__(self): + return f"" + + +class MCPUsage(Base): + """Track MCP tool usage for analytics.""" + + __tablename__ = "mcp_usages" + + id = Column(GUID(), primary_key=True, default=uuid.uuid4) + session_id = Column(GUID(), ForeignKey("sessions.id"), nullable=False, index=True) + tool_name = Column(String(100), nullable=False) + request_data = Column(Text, nullable=True) # JSON string + response_data = Column(Text, nullable=True) # JSON string + timestamp = Column(DateTime, default=datetime.utcnow, nullable=False) + duration_ms = Column(Integer, nullable=True) + success = Column(Boolean, default=True, nullable=False) + error_message = Column(Text, nullable=True) + + # Relationships + session = relationship("Session", back_populates="mcp_usages") + + def __repr__(self): + return f"" + + +# Database setup +def get_database_url(): + """Get database URL from environment variables.""" + # For OpenShift/production, use PostgreSQL + db_url = os.getenv("DATABASE_URL") + if db_url: + return db_url + + # For development, use SQLite + db_path = os.getenv("SQLITE_DB_PATH", "/tmp/rhoai_sessions.db") + return f"sqlite:///{db_path}" + + +def create_engine_instance(): + """Create SQLAlchemy engine instance.""" + database_url = get_database_url() + + if database_url.startswith("sqlite"): + engine = create_engine( + database_url, + connect_args={"check_same_thread": False}, + echo=os.getenv("DEBUG", "").lower() == "true", + ) + else: + engine = create_engine( + database_url, echo=os.getenv("DEBUG", "").lower() == "true" + ) + + return engine + + +def create_session_factory(): + """Create SQLAlchemy session factory.""" + engine = create_engine_instance() + return sessionmaker(autocommit=False, autoflush=False, bind=engine) + + +def init_database(): + """Initialize database tables.""" + engine = create_engine_instance() + Base.metadata.create_all(bind=engine) + return engine diff --git a/src/rhoai_ai_feature_sizing/api/schemas.py b/src/rhoai_ai_feature_sizing/api/schemas.py new file mode 100644 index 000000000..a4db298bd --- /dev/null +++ b/src/rhoai_ai_feature_sizing/api/schemas.py @@ -0,0 +1,129 @@ +"""Pydantic models for API request and response schemas.""" + +import uuid +from datetime import datetime +from typing import List, Optional, Dict, Any + +from pydantic import BaseModel, Field +from .models import SessionStatus, Stage, MessageRole + + +# Request schemas +class CreateSessionRequest(BaseModel): + """Request to create a new processing session.""" + + jira_key: str = Field(..., description="Jira issue key (e.g., PROJ-123)") + soft_mode: bool = Field( + True, + description="If True, only generate structure without creating actual Jira tickets", + ) + + +# Response schemas +class MessageResponse(BaseModel): + """Chat message response.""" + + id: uuid.UUID + role: MessageRole + content: str + stage: Optional[Stage] + timestamp: datetime + message_metadata: Optional[Dict[str, Any]] = None + + class Config: + from_attributes = True + + +class OutputResponse(BaseModel): + """Output file response.""" + + id: uuid.UUID + stage: Stage + filename: str + content: str + created_at: datetime + + class Config: + from_attributes = True + + +class MCPUsageResponse(BaseModel): + """MCP usage tracking response.""" + + id: uuid.UUID + tool_name: str + timestamp: datetime + duration_ms: Optional[int] + success: bool + error_message: Optional[str] + + class Config: + from_attributes = True + + +class SessionResponse(BaseModel): + """Session status response.""" + + id: uuid.UUID + jira_key: str + status: SessionStatus + current_stage: Optional[Stage] + soft_mode: bool + created_at: datetime + updated_at: datetime + started_at: Optional[datetime] + completed_at: Optional[datetime] + error_message: Optional[str] + + class Config: + from_attributes = True + + +class SessionDetailResponse(SessionResponse): + """Detailed session response with related data.""" + + messages: List[MessageResponse] = [] + outputs: List[OutputResponse] = [] + mcp_usages: List[MCPUsageResponse] = [] + + +class HealthResponse(BaseModel): + """Health check response.""" + + status: str = "healthy" + timestamp: datetime = Field(default_factory=datetime.utcnow) + version: str = "1.0.0" + database: str = "connected" + llama_stack: str = "unknown" + + +class ProgressResponse(BaseModel): + """Progress update response.""" + + session_id: uuid.UUID + status: SessionStatus + current_stage: Optional[Stage] + progress_percentage: int = Field(ge=0, le=100) + latest_message: Optional[str] = None + error_message: Optional[str] = None + started_at: Optional[datetime] = None + estimated_completion: Optional[datetime] = None + + +class SessionListResponse(BaseModel): + """List of sessions response.""" + + sessions: List[SessionResponse] + total: int + page: int + page_size: int + + +class StageProgressUpdate(BaseModel): + """Internal model for stage progress updates.""" + + stage: Stage + status: str + message: str + progress_percentage: int = 0 + metadata: Optional[Dict[str, Any]] = None diff --git a/src/rhoai_ai_feature_sizing/api/services.py b/src/rhoai_ai_feature_sizing/api/services.py new file mode 100644 index 000000000..8e5e34483 --- /dev/null +++ b/src/rhoai_ai_feature_sizing/api/services.py @@ -0,0 +1,500 @@ +"""Service layer for processing sessions and managing stages.""" + +import json +import logging +import time +import uuid +from datetime import datetime +from pathlib import Path +from typing import Optional, Dict, Any, List +from contextlib import contextmanager +import tempfile +import os + +from sqlalchemy.orm import Session as DBSession + +from .models import ( + Session, + Message, + Output, + MCPUsage, + SessionStatus, + Stage, + MessageRole, + create_session_factory, +) +from .schemas import StageProgressUpdate +from ..stages.refine_feature import generate_refinement_with_agent +from ..stages.draft_jiras import draft_jiras_from_file + + +class SessionService: + """Service for managing processing sessions.""" + + def __init__(self): + self.session_factory = create_session_factory() + self.logger = logging.getLogger("SessionService") + + @contextmanager + def get_db_session(self): + """Context manager for database sessions.""" + db_session = self.session_factory() + try: + yield db_session + db_session.commit() + except Exception: + db_session.rollback() + raise + finally: + db_session.close() + + def create_session(self, jira_key: str, soft_mode: bool = True) -> uuid.UUID: + """Create a new processing session.""" + with self.get_db_session() as db_session: + session = Session( + jira_key=jira_key, soft_mode=soft_mode, status=SessionStatus.PENDING + ) + db_session.add(session) + db_session.flush() + + # Add initial message + initial_message = Message( + session_id=session.id, + role=MessageRole.SYSTEM, + content=f"Session created for Jira issue {jira_key} ({'soft mode' if soft_mode else 'hard mode'})", + stage=None, + ) + db_session.add(initial_message) + + self.logger.info(f"Created session {session.id} for {jira_key}") + return session.id + + def get_session(self, session_id: uuid.UUID) -> Optional[Session]: + """Get session by ID.""" + with self.get_db_session() as db_session: + session = db_session.query(Session).filter(Session.id == session_id).first() + if session: + # Load all attributes while session is active to avoid DetachedInstanceError + _ = ( + session.id, + session.jira_key, + session.status, + session.current_stage, + session.soft_mode, + session.created_at, + session.updated_at, + session.started_at, + session.completed_at, + session.error_message, + ) + db_session.expunge(session) + return session + + def get_sessions( + self, page: int = 1, page_size: int = 20 + ) -> tuple[List[Session], int]: + """Get paginated list of sessions.""" + with self.get_db_session() as db_session: + offset = (page - 1) * page_size + sessions = ( + db_session.query(Session) + .order_by(Session.created_at.desc()) + .offset(offset) + .limit(page_size) + .all() + ) + total = db_session.query(Session).count() + + # Load all attributes for each session to avoid DetachedInstanceError + for session in sessions: + _ = ( + session.id, + session.jira_key, + session.status, + session.current_stage, + session.soft_mode, + session.created_at, + session.updated_at, + session.started_at, + session.completed_at, + session.error_message, + ) + db_session.expunge(session) + + return sessions, total + + def get_session_messages( + self, session_id: uuid.UUID, limit: int = 50 + ) -> List[Message]: + """Get messages for a session.""" + with self.get_db_session() as db_session: + messages = ( + db_session.query(Message) + .filter(Message.session_id == session_id) + .order_by(Message.timestamp.asc()) + .limit(limit) + .all() + ) + + # Load all attributes for each message to avoid DetachedInstanceError + for message in messages: + _ = ( + message.id, + message.session_id, + message.role, + message.content, + message.stage, + message.timestamp, + message.message_metadata, + ) + db_session.expunge(message) + + return messages + + def get_session_outputs(self, session_id: uuid.UUID) -> List[Output]: + """Get outputs for a session.""" + with self.get_db_session() as db_session: + outputs = ( + db_session.query(Output) + .filter(Output.session_id == session_id) + .order_by(Output.created_at.asc()) + .all() + ) + + # Load all attributes for each output to avoid DetachedInstanceError + for output in outputs: + _ = ( + output.id, + output.session_id, + output.stage, + output.filename, + output.content, + output.created_at, + ) + db_session.expunge(output) + + return outputs + + def get_session_mcp_usage(self, session_id: uuid.UUID) -> List[MCPUsage]: + """Get MCP usage for a session.""" + with self.get_db_session() as db_session: + usages = ( + db_session.query(MCPUsage) + .filter(MCPUsage.session_id == session_id) + .order_by(MCPUsage.timestamp.asc()) + .all() + ) + + # Load all attributes for each usage to avoid DetachedInstanceError + for usage in usages: + _ = ( + usage.id, + usage.session_id, + usage.tool_name, + usage.request_data, + usage.response_data, + usage.timestamp, + usage.duration_ms, + usage.success, + usage.error_message, + ) + db_session.expunge(usage) + + return usages + + def add_message( + self, + session_id: uuid.UUID, + role: MessageRole, + content: str, + stage: Optional[Stage] = None, + message_metadata: Optional[Dict[str, Any]] = None, + ): + """Add a message to the session.""" + with self.get_db_session() as db_session: + message = Message( + session_id=session_id, + role=role, + content=content, + stage=stage, + message_metadata=( + json.dumps(message_metadata) if message_metadata else None + ), + ) + db_session.add(message) + + def add_output( + self, session_id: uuid.UUID, stage: Stage, filename: str, content: str + ): + """Add an output file to the session.""" + with self.get_db_session() as db_session: + output = Output( + session_id=session_id, stage=stage, filename=filename, content=content + ) + db_session.add(output) + + def add_mcp_usage( + self, + session_id: uuid.UUID, + tool_name: str, + request_data: Optional[Dict] = None, + response_data: Optional[Dict] = None, + duration_ms: Optional[int] = None, + success: bool = True, + error_message: Optional[str] = None, + ): + """Add MCP usage tracking.""" + with self.get_db_session() as db_session: + usage = MCPUsage( + session_id=session_id, + tool_name=tool_name, + request_data=json.dumps(request_data) if request_data else None, + response_data=json.dumps(response_data) if response_data else None, + duration_ms=duration_ms, + success=success, + error_message=error_message, + ) + db_session.add(usage) + + def update_session_status( + self, + session_id: uuid.UUID, + status: SessionStatus, + current_stage: Optional[Stage] = None, + error_message: Optional[str] = None, + ): + """Update session status and stage.""" + with self.get_db_session() as db_session: + session = db_session.query(Session).filter(Session.id == session_id).first() + if session: + session.status = status + if current_stage is not None: + session.current_stage = current_stage + if error_message is not None: + session.error_message = error_message + if status == SessionStatus.RUNNING and not session.started_at: + session.started_at = datetime.utcnow() + elif status in [SessionStatus.COMPLETED, SessionStatus.FAILED]: + session.completed_at = datetime.utcnow() + + async def process_session(self, session_id: uuid.UUID): + """Process a complete session through all stages.""" + try: + session = self.get_session(session_id) + if not session: + self.logger.error(f"Session {session_id} not found") + return + + self.logger.info(f"Starting processing for session {session_id}") + self.update_session_status(session_id, SessionStatus.RUNNING) + + # Create a temporary directory for processing + with tempfile.TemporaryDirectory() as temp_dir: + temp_path = Path(temp_dir) + + try: + # Stage 1: Refine + await self._process_refine_stage( + session_id, session.jira_key, temp_path + ) + + # Stage 2: Epics (placeholder - not implemented yet) + await self._process_epics_stage(session_id, temp_path) + + # Stage 3: Jiras + await self._process_jiras_stage( + session_id, temp_path, session.soft_mode + ) + + # Stage 4: Estimate (placeholder - not implemented yet) + await self._process_estimate_stage(session_id, temp_path) + + self.update_session_status(session_id, SessionStatus.COMPLETED) + self.add_message( + session_id, + MessageRole.SYSTEM, + "✅ All stages completed successfully!", + ) + + except Exception as e: + self.logger.error( + f"Error processing session {session_id}: {e}", exc_info=True + ) + self.update_session_status( + session_id, SessionStatus.FAILED, error_message=str(e) + ) + self.add_message( + session_id, + MessageRole.SYSTEM, + f"❌ Processing failed: {str(e)}", + ) + + except Exception as e: + self.logger.error( + f"Unexpected error in process_session: {e}", exc_info=True + ) + self.update_session_status( + session_id, + SessionStatus.FAILED, + error_message=f"Unexpected error: {str(e)}", + ) + + async def _process_refine_stage( + self, session_id: uuid.UUID, jira_key: str, temp_path: Path + ): + """Process the refine stage.""" + self.logger.info(f"Starting refine stage for session {session_id}") + self.update_session_status(session_id, SessionStatus.RUNNING, Stage.REFINE) + + self.add_message( + session_id, + MessageRole.AGENT, + f"🔍 Starting feature refinement for {jira_key}...", + Stage.REFINE, + ) + + # Load template + template_path = Path(__file__).parent.parent / "prompts" / "refine_feature.md" + with open(template_path, "r", encoding="utf-8") as f: + template = f.read() + + start_time = time.time() + + # Set up custom logging handler to capture messages + handler = SessionLogHandler(session_id, Stage.REFINE, self) + logger = logging.getLogger("refine_feature") + logger.addHandler(handler) + + try: + # Generate refinement + refinement_content = generate_refinement_with_agent(jira_key, template) + + # Save output + filename = f"refined_{jira_key}.md" + self.add_output(session_id, Stage.REFINE, filename, refinement_content) + + duration = time.time() - start_time + self.add_message( + session_id, + MessageRole.AGENT, + f"✅ Feature refinement completed in {duration:.2f}s. Generated {len(refinement_content)} characters.", + Stage.REFINE, + ) + + finally: + logger.removeHandler(handler) + + async def _process_epics_stage(self, session_id: uuid.UUID, temp_path: Path): + """Process the epics stage (placeholder).""" + self.logger.info(f"Starting epics stage for session {session_id}") + self.update_session_status(session_id, SessionStatus.RUNNING, Stage.EPICS) + + self.add_message( + session_id, + MessageRole.AGENT, + "📋 Epic creation stage is not yet implemented. Skipping to Jira drafting...", + Stage.EPICS, + ) + + async def _process_jiras_stage( + self, session_id: uuid.UUID, temp_path: Path, soft_mode: bool + ): + """Process the jiras stage.""" + self.logger.info(f"Starting jiras stage for session {session_id}") + self.update_session_status(session_id, SessionStatus.RUNNING, Stage.JIRAS) + + mode_text = "soft mode" if soft_mode else "hard mode" + self.add_message( + session_id, + MessageRole.AGENT, + f"🎫 Starting Jira ticket drafting ({mode_text})...", + Stage.JIRAS, + ) + + # Get the refined content from database + outputs = self.get_session_outputs(session_id) + refined_output = next((o for o in outputs if o.stage == Stage.REFINE), None) + + if not refined_output: + raise RuntimeError("No refined output found for Jira stage") + + # Write refined content to temp file + temp_file = temp_path / f"refined_{uuid.uuid4().hex[:8]}.md" + with open(temp_file, "w", encoding="utf-8") as f: + f.write(refined_output.content) + + start_time = time.time() + + # Set up custom logging handler + handler = SessionLogHandler(session_id, Stage.JIRAS, self) + logger = logging.getLogger("draft_jiras") + logger.addHandler(handler) + + try: + # Generate jiras + jira_content = draft_jiras_from_file(temp_file, soft_mode=soft_mode) + + # Save output + filename = f"jiras_{refined_output.filename.replace('refined_', '')}" + self.add_output(session_id, Stage.JIRAS, filename, jira_content) + + duration = time.time() - start_time + self.add_message( + session_id, + MessageRole.AGENT, + f"✅ Jira ticket drafting completed in {duration:.2f}s ({mode_text}). Generated {len(jira_content)} characters.", + Stage.JIRAS, + ) + + finally: + logger.removeHandler(handler) + + async def _process_estimate_stage(self, session_id: uuid.UUID, temp_path: Path): + """Process the estimate stage (placeholder).""" + self.logger.info(f"Starting estimate stage for session {session_id}") + self.update_session_status(session_id, SessionStatus.RUNNING, Stage.ESTIMATE) + + self.add_message( + session_id, + MessageRole.AGENT, + "📊 Estimation stage is not yet implemented. Processing complete!", + Stage.ESTIMATE, + ) + + +class SessionLogHandler(logging.Handler): + """Custom logging handler that captures log messages as session messages.""" + + def __init__(self, session_id: uuid.UUID, stage: Stage, service: SessionService): + super().__init__() + self.session_id = session_id + self.stage = stage + self.service = service + + def emit(self, record: logging.LogRecord): + """Emit a log record as a session message.""" + try: + message = self.format(record) + + # Filter out some noisy logs + if any(skip in message.lower() for skip in ["debug", "httpx", "urllib3"]): + return + + # Map log levels to message roles + if record.levelno >= logging.ERROR: + role = MessageRole.SYSTEM + message = f"❌ ERROR: {message}" + elif record.levelno >= logging.WARNING: + role = MessageRole.SYSTEM + message = f"⚠️ WARNING: {message}" + elif record.levelno >= logging.INFO: + role = MessageRole.AGENT + if "✅" not in message and "📋" not in message and "🔍" not in message: + message = f"ℹ️ {message}" + else: + return # Skip debug messages + + self.service.add_message(self.session_id, role, message, self.stage) + except Exception: + # Don't let logging errors break the main process + pass diff --git a/src/rhoai_ai_feature_sizing/llama_stack_setup.py b/src/rhoai_ai_feature_sizing/llama_stack_setup.py index e615cb9c6..ccfacb384 100644 --- a/src/rhoai_ai_feature_sizing/llama_stack_setup.py +++ b/src/rhoai_ai_feature_sizing/llama_stack_setup.py @@ -3,7 +3,7 @@ LLAMA_STACK_URL = os.getenv("LLAMA_STACK_URL") MCP_ATLASSIAN_URL = os.getenv("MCP_ATLASSIAN_URL") -DEV_MODE = os.getenv("DEV_MODE", "false") +CONFIGURE_TOOLGROUPS = os.getenv("CONFIGURE_TOOLGROUPS", "false") def get_llama_stack_client(): @@ -18,7 +18,7 @@ def get_llama_stack_client(): print(f"Creating client for {LLAMA_STACK_URL}") client = LlamaStackClient(base_url=LLAMA_STACK_URL) - if DEV_MODE == "false": + if CONFIGURE_TOOLGROUPS == "true": try: # First, try to unregister any existing toolgroup to clear old config try: diff --git a/src/rhoai_ai_feature_sizing/run_api.py b/src/rhoai_ai_feature_sizing/run_api.py new file mode 100644 index 000000000..9ba24c659 --- /dev/null +++ b/src/rhoai_ai_feature_sizing/run_api.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +"""Run the RHOAI AI Feature Sizing API server.""" +import logging +import os +import sys + + +def setup_logging(): + """Setup logging configuration.""" + log_level = os.getenv("LOG_LEVEL", "INFO").upper() + + logging.basicConfig( + level=getattr(logging, log_level, logging.INFO), + format="%(asctime)s - %(name)s - %(levelname)s - %(funcName)s:%(lineno)d - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + # Set specific logger levels + logging.getLogger("uvicorn").setLevel(logging.INFO) + logging.getLogger("sqlalchemy").setLevel(logging.WARNING) + + +def main(): + """Run the API server.""" + setup_logging() + + # Get configuration from environment + host = os.getenv("HOST", "0.0.0.0") + port = int(os.getenv("PORT", 8000)) + workers = int(os.getenv("WORKERS", 1)) + reload = os.getenv("RELOAD", "false").lower() == "true" + + logging.info(f"Starting RHOAI AI Feature Sizing API on {host}:{port}") + logging.info(f"Workers: {workers}, Reload: {reload}") + + try: + import uvicorn + + uvicorn.run( + "rhoai_ai_feature_sizing.api.main:app", + host=host, + port=port, + workers=workers if not reload else 1, + reload=reload, + log_level=os.getenv("LOG_LEVEL", "info").lower(), + ) + except ImportError: + logging.error("uvicorn not installed. Install with: pip install uvicorn") + sys.exit(1) + except Exception as e: + logging.error(f"Failed to start server: {e}") + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/test_api.py b/test_api.py new file mode 100644 index 000000000..b7f3c31c8 --- /dev/null +++ b/test_api.py @@ -0,0 +1,274 @@ +#!/usr/bin/env python3 +"""Simple test script for the RHOAI AI Feature Sizing API.""" +import asyncio +import time +import requests +import json +from typing import Dict, Any + + +class APITester: + """Test the API endpoints.""" + + def __init__(self, base_url: str = "http://localhost:8000"): + self.base_url = base_url.rstrip("/") + self.session_id = None + + def test_health_check(self) -> bool: + """Test the health check endpoint.""" + print("🩺 Testing health check...") + try: + response = requests.get(f"{self.base_url}/healthz") + response.raise_for_status() + health = response.json() + + print(f" ✅ Health check passed") + print(f" 📊 Database: {health.get('database', 'unknown')}") + print(f" 🤖 Llama Stack: {health.get('llama_stack', 'unknown')}") + return True + except Exception as e: + print(f" ❌ Health check failed: {e}") + return False + + def test_create_session(self, jira_key: str = "TEST-123") -> bool: + """Test creating a new session.""" + print(f"🚀 Creating session for {jira_key}...") + try: + payload = {"jira_key": jira_key, "soft_mode": True} + response = requests.post( + f"{self.base_url}/sessions", + json=payload, + headers={"Content-Type": "application/json"}, + ) + response.raise_for_status() + session = response.json() + + self.session_id = session["id"] + print(f" ✅ Session created: {self.session_id}") + print(f" 📋 Status: {session['status']}") + print(f" 🎯 Jira Key: {session['jira_key']}") + print(f" 🔧 Soft Mode: {session['soft_mode']}") + return True + except Exception as e: + print(f" ❌ Session creation failed: {e}") + return False + + def test_list_sessions(self) -> bool: + """Test listing sessions.""" + print("📋 Testing session list...") + try: + response = requests.get(f"{self.base_url}/sessions?page=1&page_size=10") + response.raise_for_status() + data = response.json() + + print(f" ✅ Found {data['total']} sessions (page {data['page']})") + if data["sessions"]: + print(f" 📊 Latest session: {data['sessions'][0]['jira_key']}") + return True + except Exception as e: + print(f" ❌ Session list failed: {e}") + return False + + def test_session_progress(self) -> bool: + """Test session progress endpoint.""" + if not self.session_id: + print(" ⚠️ No session ID available for progress test") + return False + + print("📈 Testing session progress...") + try: + response = requests.get( + f"{self.base_url}/sessions/{self.session_id}/progress" + ) + response.raise_for_status() + progress = response.json() + + print(f" ✅ Progress: {progress['progress_percentage']}%") + print(f" 📊 Status: {progress['status']}") + print(f" 🎭 Stage: {progress.get('current_stage', 'None')}") + if progress.get("latest_message"): + print(f" 💬 Latest: {progress['latest_message'][:100]}...") + return True + except Exception as e: + print(f" ❌ Progress check failed: {e}") + return False + + def test_session_messages(self) -> bool: + """Test session messages endpoint.""" + if not self.session_id: + print(" ⚠️ No session ID available for messages test") + return False + + print("💬 Testing session messages...") + try: + response = requests.get( + f"{self.base_url}/sessions/{self.session_id}/messages?limit=10" + ) + response.raise_for_status() + messages = response.json() + + print(f" ✅ Found {len(messages)} messages") + if messages: + latest = messages[-1] + print(f" 📝 Latest: [{latest['role']}] {latest['content'][:100]}...") + return True + except Exception as e: + print(f" ❌ Messages test failed: {e}") + return False + + def test_session_outputs(self) -> bool: + """Test session outputs endpoint.""" + if not self.session_id: + print(" ⚠️ No session ID available for outputs test") + return False + + print("📄 Testing session outputs...") + try: + response = requests.get( + f"{self.base_url}/sessions/{self.session_id}/outputs" + ) + response.raise_for_status() + outputs = response.json() + + print(f" ✅ Found {len(outputs)} outputs") + for output in outputs: + print( + f" 📋 {output['stage']}: {output['filename']} ({len(output['content'])} chars)" + ) + return True + except Exception as e: + print(f" ❌ Outputs test failed: {e}") + return False + + def test_session_detail(self) -> bool: + """Test session detail endpoint.""" + if not self.session_id: + print(" ⚠️ No session ID available for detail test") + return False + + print("🔍 Testing session detail...") + try: + response = requests.get(f"{self.base_url}/sessions/{self.session_id}") + response.raise_for_status() + session = response.json() + + print(f" ✅ Session detail retrieved") + print(f" 📊 Status: {session['status']}") + print(f" 💬 Messages: {len(session.get('messages', []))}") + print(f" 📄 Outputs: {len(session.get('outputs', []))}") + print(f" 🔧 MCP Usage: {len(session.get('mcp_usages', []))}") + return True + except Exception as e: + print(f" ❌ Session detail failed: {e}") + return False + + def poll_until_complete(self, max_wait: int = 300) -> bool: + """Poll session until completion or timeout.""" + if not self.session_id: + print(" ⚠️ No session ID available for polling") + return False + + print(f"⏳ Polling session for up to {max_wait} seconds...") + start_time = time.time() + + while time.time() - start_time < max_wait: + try: + response = requests.get( + f"{self.base_url}/sessions/{self.session_id}/progress" + ) + response.raise_for_status() + progress = response.json() + + status = progress["status"] + percentage = progress["progress_percentage"] + stage = progress.get("current_stage", "unknown") + + print(f" 📈 {percentage}% - {status} ({stage})") + + if status in ["completed", "failed"]: + if status == "completed": + print(f" ✅ Session completed successfully!") + return True + else: + print( + f" ❌ Session failed: {progress.get('error_message', 'Unknown error')}" + ) + return False + + time.sleep(5) # Poll every 5 seconds + + except Exception as e: + print(f" ⚠️ Polling error: {e}") + time.sleep(5) + + print(f" ⏰ Timeout after {max_wait} seconds") + return False + + def run_all_tests( + self, jira_key: str = "TEST-123", wait_for_completion: bool = False + ) -> bool: + """Run all API tests.""" + print("🧪 Running RHOAI AI Feature Sizing API Tests") + print("=" * 50) + + tests = [ + self.test_health_check, + self.test_list_sessions, + lambda: self.test_create_session(jira_key), + self.test_session_progress, + self.test_session_messages, + self.test_session_outputs, + self.test_session_detail, + ] + + passed = 0 + total = len(tests) + + for test in tests: + result = test() + if result: + passed += 1 + print() + + if wait_for_completion and self.session_id: + print("⏳ Waiting for session completion...") + self.poll_until_complete() + print() + + # Test outputs again after completion + print("📄 Testing outputs after completion...") + self.test_session_outputs() + print() + + print("=" * 50) + print(f"🧪 Test Results: {passed}/{total} passed") + + if passed == total: + print("✅ All tests passed!") + return True + else: + print("❌ Some tests failed!") + return False + + +def main(): + """Main test function.""" + import argparse + + parser = argparse.ArgumentParser(description="Test the RHOAI AI Feature Sizing API") + parser.add_argument("--url", default="http://localhost:8000", help="API base URL") + parser.add_argument("--jira-key", default="TEST-123", help="Jira key to test with") + parser.add_argument( + "--wait", action="store_true", help="Wait for session completion" + ) + + args = parser.parse_args() + + tester = APITester(args.url) + success = tester.run_all_tests(args.jira_key, args.wait) + + exit(0 if success else 1) + + +if __name__ == "__main__": + main() diff --git a/uv.lock b/uv.lock index e60631484..b5a9d67a2 100644 --- a/uv.lock +++ b/uv.lock @@ -341,6 +341,39 @@ 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"sha256:57df5dc6fdb5ed1a88a1ed2195fd31927e705cad62dedd86b46972752a80f576", size = 1911224 }, +] + [[package]] name = "starlette" version = "0.46.2" From 9a16dd9c1f3153aba237a28b744e01a5e94a8683 Mon Sep 17 00:00:00 2001 From: emarion1 <59747212+emarion1@users.noreply.github.com> Date: Thu, 17 Jul 2025 10:50:06 -0500 Subject: [PATCH 007/240] Update draft_jiras.md - Added Ticket Sizing Table Added Ticket Sizing table with data from 2025 sprint history to add context to estimating team-specific story point sizing using team's Average duration per story point from the table. --- .../prompts/draft_jiras.md | 26 ++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md b/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md index d11847d94..6d48f6000 100644 --- a/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md +++ b/src/rhoai_ai_feature_sizing/prompts/draft_jiras.md @@ -19,9 +19,9 @@ Generate ticket structures in **soft mode** - do NOT create actual Jira tickets. * Blocks / is blocked byOutward Description: Blocks – The source issue prevents the destination issue from starting or completing. Inward Description: Is blocked by – The destination issue cannot proceed until the source issue is resolved. Use Case: Commonly used for process dependencies where one task must be completed before another can start (e.g., a "Finish-to-Start" dependency). * Relates to / relates toOutward Description: Relates to – The source issue has a general relationship with the destination issue. Inward Description: Relates to – The destination issue is related to the source issue. Use Case: Used for loose associations where issues are connected but not necessarily dependent (e.g., for context or shared relevance). This link type may not always indicate a strict dependency, so use it cautiously for dependency management. -- Component assignments +- Component assignments using component teams listed in Ticket Sizing: Team Average Duration Per Story Point - Story point estimates using ticket sizing guidelines: - * decompose into child user stories or tasks ensuring that each is appropriately sized for completion within a three-week sprint. + * decompose into child user stories or tasks ensuring that each is appropriately sized for completion within a three-week sprint and using Average Duration per story point for componenet specific size estimation. * Story points are in Fibonacci sequence: 0, 1, 2, 3, 5, 8 and 13. * Story points do not exactly reflect the time the ticket will take, but reflect more the complexity of the ticket and overall effort required. If the ticket is 8 or 13, consider dividing it into smaller tickets. - For Feature estimation, the points of all child tickets shall be added up to provide a Feature story point estimate which will be combined with implementation plan timeline. @@ -31,8 +31,28 @@ Inward Description: Is blocked by – The destination issue cannot proceed until - detailed test steps - required documentation +## Ticket Sizing: Team Average Duration Per Story Point + +| Team/Board | Sprint Story Points Committed | Sprint Story Points Completed | Velocity Predictability (%) | Average Duration per Story Point | +|------------|-------------------------------|------------------------------|----------------------------|----------------------------| +| Kubeflow DevX | 25 | - | - | - | +| Model Explainability | 29 | - | - | - | +| Training Ray | 14 | 3 | 21.428571428571427 | 94.35333333333334 | +| RHOAIENG IDE Indigo Scrum Team | 59 | 18 | 30.508474576271187 | 1.4084259259259262 | +| Data Science Pipelines | 411 | 147 | 35.76642335766424 | 0.4174296745725317 | +| RHOAI IDE Main Board | 54 | 30 | 55.55555555555556 | 2.9058125 | +| RHOAI Platform | 155 | 97 | 62.58064516129033 | 0.583617912371134 | +| Model Serving Runtimes | 368 | 231 | 62.77173913043478 | 0.20084369531178042 | +| Workload Orchestration | 115 | 87 | 75.65217391304347 | 0.6207620263942103 | +| Model Servers and Metrics | 243 | 202 | 83.1275720164609 | 0.22003116978364504 | +| RHOAIENG IDE Teal Scrum Team | 64 | 54 | 84.375 | 0.9677731481481481 | +| Training Kubeflow | 85 | 76 | 89.41176470588236 | 0.5279844497607655 | +| Dashboard | 591 | 561 | 94.9238578680203 | 0.08809968916191571 | +| Model Registry board | 65 | 76 | 116.92307692307693 | 0.32335812356979404 | +| Feature Store | 44 | 53 | 120.45454545454545 | 1.4122700471698113 | + ## Ticket sequencing -Sequence the tickets in chronological order within appicable epic accounting for cross-ticket dependencies +Sequence the tickets in chronological order within applicable epic accounting for cross-ticket dependencies ## Examples From 361cb1c44fc1ff970d216655f75ce45d2b6bb3e0 Mon Sep 17 00:00:00 2001 From: Gage Krumbach Date: Thu, 17 Jul 2025 15:53:03 -0500 Subject: [PATCH 008/240] Refactor OpenShift deployment configuration and enhance API functionality. Update `openshift-deployment.yaml` to switch vector provider from Milvus to Faiss, adjust database paths, and add a command for the server container. Modify `pyproject.toml` to include `uvicorn` with standard extras and add new dependencies in `uv.lock`. Implement WebSocket management in the API for real-time session updates, including a new `WebSocketManager` class and related endpoint. Update message handling in the API models and schemas to incorporate message status tracking. --- frontend/.editorconfig | 17 + frontend/.eslintrc.js | 75 + frontend/.gitignore | 13 + frontend/.prettierignore | 1 + frontend/.prettierrc | 4 + frontend/JIRA_RFE_UI_README.md | 156 + frontend/LICENSE | 21 + frontend/README.md | 103 + frontend/__mocks__/fileMock.js | 1 + frontend/__mocks__/styleMock.js | 1 + frontend/jest.config.js | 32 + frontend/package-lock.json | 21524 ++++++++++++++++ frontend/package.json | 84 + frontend/src/app/AppLayout/AppLayout.tsx | 114 + frontend/src/app/Dashboard/Dashboard.tsx | 6 + frontend/src/app/NotFound/NotFound.tsx | 35 + .../src/app/__snapshots__/app.test.tsx.snap | 314 + frontend/src/app/app.css | 13 + frontend/src/app/app.test.tsx | 55 + frontend/src/app/bgimages/Patternfly-Logo.svg | 28 + frontend/src/app/index.tsx | 16 + frontend/src/app/routes.tsx | 53 + frontend/src/assets/robot-solid.svg | 1 + frontend/src/assets/user-solid.svg | 1 + frontend/src/components/ChatPanel.tsx | 414 + frontend/src/components/OutputPanel.tsx | 448 + frontend/src/components/SessionManager.tsx | 117 + frontend/src/components/SessionSidebar.tsx | 378 + frontend/src/config.ts | 3 + frontend/src/favicon.png | Bin 0 -> 706 bytes frontend/src/index.html | 18 + frontend/src/index.tsx | 25 + frontend/src/services/api.ts | 110 + frontend/src/services/websocket.ts | 108 + frontend/src/types/api.ts | 75 + frontend/src/typings.d.ts | 12 + frontend/stylePaths.js | 14 + frontend/tsconfig.json | 34 + frontend/webpack.common.js | 136 + frontend/webpack.dev.js | 34 + frontend/webpack.prod.js | 38 + openshift-deployment.yaml | 21 +- pyproject.toml | 2 +- src/rhoai_ai_feature_sizing/api/main.py | 104 +- src/rhoai_ai_feature_sizing/api/models.py | 14 +- src/rhoai_ai_feature_sizing/api/schemas.py | 38 +- src/rhoai_ai_feature_sizing/api/services.py | 563 +- .../prompts/refine_feature.md | 33 +- .../stages/refine_feature.py | 140 +- uv.lock | 155 +- 50 files changed, 25399 insertions(+), 303 deletions(-) create mode 100644 frontend/.editorconfig create mode 100644 frontend/.eslintrc.js create mode 100644 frontend/.gitignore create mode 100644 frontend/.prettierignore create mode 100644 frontend/.prettierrc create mode 100644 frontend/JIRA_RFE_UI_README.md create mode 100644 frontend/LICENSE create mode 100644 frontend/README.md create mode 100644 frontend/__mocks__/fileMock.js create mode 100644 frontend/__mocks__/styleMock.js create mode 100644 frontend/jest.config.js create mode 100644 frontend/package-lock.json create mode 100644 frontend/package.json create mode 100644 frontend/src/app/AppLayout/AppLayout.tsx create mode 100644 frontend/src/app/Dashboard/Dashboard.tsx create mode 100644 frontend/src/app/NotFound/NotFound.tsx create mode 100644 frontend/src/app/__snapshots__/app.test.tsx.snap create mode 100644 frontend/src/app/app.css create mode 100644 frontend/src/app/app.test.tsx create mode 100644 frontend/src/app/bgimages/Patternfly-Logo.svg create mode 100644 frontend/src/app/index.tsx create mode 100644 frontend/src/app/routes.tsx create mode 100644 frontend/src/assets/robot-solid.svg create mode 100644 frontend/src/assets/user-solid.svg create mode 100644 frontend/src/components/ChatPanel.tsx create mode 100644 frontend/src/components/OutputPanel.tsx create mode 100644 frontend/src/components/SessionManager.tsx create mode 100644 frontend/src/components/SessionSidebar.tsx create mode 100644 frontend/src/config.ts create mode 100644 frontend/src/favicon.png create mode 100644 frontend/src/index.html create mode 100644 frontend/src/index.tsx create mode 100644 frontend/src/services/api.ts create mode 100644 frontend/src/services/websocket.ts create mode 100644 frontend/src/types/api.ts create mode 100644 frontend/src/typings.d.ts create mode 100644 frontend/stylePaths.js create mode 100644 frontend/tsconfig.json create mode 100644 frontend/webpack.common.js create mode 100644 frontend/webpack.dev.js create mode 100644 frontend/webpack.prod.js diff --git a/frontend/.editorconfig b/frontend/.editorconfig new file mode 100644 index 000000000..b7eb84093 --- /dev/null +++ b/frontend/.editorconfig @@ -0,0 +1,17 @@ +# Editor configuration, see http://editorconfig.org +root = true + +[*] +charset = utf-8 +indent_style = space +indent_size = 2 +insert_final_newline = true +trim_trailing_whitespace = true + +[*.snap] +max_line_length = off +trim_trailing_whitespace = false + +[*.md] +max_line_length = off +trim_trailing_whitespace = false diff --git a/frontend/.eslintrc.js b/frontend/.eslintrc.js new file mode 100644 index 000000000..0fe245e4c --- /dev/null +++ b/frontend/.eslintrc.js @@ -0,0 +1,75 @@ +module.exports = { + // tells eslint to use the TypeScript parser + "parser": "@typescript-eslint/parser", + // tell the TypeScript parser that we want to use JSX syntax + "parserOptions": { + "tsx": true, + "jsx": true, + "js": true, + "useJSXTextNode": true, + "project": "./tsconfig.json", + "tsconfigRootDir": "." + }, + // we want to use the recommended rules provided from the typescript plugin + "extends": [ + "eslint:recommended", + "plugin:react/recommended", + "plugin:@typescript-eslint/recommended" + ], + "globals": { + "window": "readonly", + "describe": "readonly", + "test": "readonly", + "expect": "readonly", + "it": "readonly", + "process": "readonly", + "document": "readonly", + "insights": "readonly", + "shallow": "readonly", + "render": "readonly", + "mount": "readonly" + }, + "overrides": [ + { + "files": ["src/**/*.ts", "src/**/*.tsx"], + "parser": "@typescript-eslint/parser", + "plugins": ["@typescript-eslint"], + "extends": ["plugin:@typescript-eslint/recommended"], + "rules": { + "react/prop-types": "off", + "@typescript-eslint/no-unused-vars": "error" + }, + }, + ], + "settings": { + "react": { + "version": "^16.11.0" + } + }, + // includes the typescript specific rules found here: https://github.com/typescript-eslint/typescript-eslint/tree/master/packages/eslint-plugin#supported-rules + "plugins": [ + "@typescript-eslint", + "react-hooks", + "eslint-plugin-react-hooks" + ], + "rules": { + "sort-imports": [ + "error", + { + "ignoreDeclarationSort": true + } + ], + "@typescript-eslint/explicit-function-return-type": "off", + "react-hooks/rules-of-hooks": "error", + "react-hooks/exhaustive-deps": "warn", + "@typescript-eslint/interface-name-prefix": "off", + "prettier/prettier": "off", + "import/no-unresolved": "off", + "import/extensions": "off", + "react/prop-types": "off" + }, + "env": { + "browser": true, + "node": true + } +} diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 000000000..4634737d2 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,13 @@ +**/node_modules +dist +yarn-error.log +yarn.lock +stats.json +coverage +storybook-static +.DS_Store +.idea +.env +.history +.github +.DS_Store \ No newline at end of file diff --git a/frontend/.prettierignore b/frontend/.prettierignore new file mode 100644 index 000000000..ec6d3cdd7 --- /dev/null +++ b/frontend/.prettierignore @@ -0,0 +1 @@ +package.json diff --git a/frontend/.prettierrc b/frontend/.prettierrc new file mode 100644 index 000000000..0981b7cc0 --- /dev/null +++ b/frontend/.prettierrc @@ -0,0 +1,4 @@ +{ + "singleQuote": true, + "printWidth": 120 +} diff --git a/frontend/JIRA_RFE_UI_README.md b/frontend/JIRA_RFE_UI_README.md new file mode 100644 index 000000000..6cf73d19c --- /dev/null +++ b/frontend/JIRA_RFE_UI_README.md @@ -0,0 +1,156 @@ +# JIRA RFE Session Manager UI + +This is a React-based UI for managing JIRA RFE (Request for Enhancement) sessions using PatternFly components. + +## Features + +### 🎯 Session Management +- **Create New Sessions**: Start processing a JIRA RFE by providing a JIRA key (e.g., RHOAI-1234) +- **Session List**: View all sessions with their status, current stage, and timestamps +- **Session Selection**: Click on any session to view its details +- **Auto-refresh**: Sessions automatically refresh every 5 seconds when running + +### 📱 Split View Layout +The UI features a three-panel layout: + +1. **Left Sidebar**: Collapsible session management panel +2. **Center Panel**: Chat session showing messages and MCP tool usage +3. **Right Panel**: Output files with tabbed interface for different stages + +### 💬 Chat & Activity Panel +- **Messages**: View all messages between user and AI assistant +- **MCP Usage**: See which tools the AI is using and their input/output +- **Stage Filtering**: Filter by processing stage (refine, epics, jiras, estimate) +- **Real-time Updates**: Auto-refreshes every 3 seconds during active sessions + +### 📄 Output Panel +- **Tabbed Interface**: Organized by processing stages +- **Markdown Rendering**: Full markdown support with GitHub Flavored Markdown +- **Stage Badges**: Visual indicators for each processing stage +- **File Management**: View all output files generated during processing + +## UI Components + +### Session Status Colors +- **Grey**: Pending - Session queued for processing +- **Blue**: Running - Currently being processed +- **Green**: Completed - Successfully finished +- **Red**: Failed - Error occurred during processing +- **Orange**: Cancelled - Session was cancelled + +### Processing Stages +1. **Refine** (Blue) - Feature refinement and analysis +2. **Epics** (Orange) - Epic creation and breakdown +3. **JIRAs** (Green) - JIRA ticket creation +4. **Estimate** (Grey) - Effort estimation + +## Setup and Development + +### Prerequisites +- Node.js 18+ (Note: Some dependencies may show warnings with Node 18) +- npm or yarn + +### Installation +```bash +cd frontend +npm install +``` + +### Development +```bash +npm run start:dev +``` + +### Build +```bash +npm run build +``` + +### Environment Configuration +The UI expects the API to be running on `http://localhost:8000` by default. You can override this by setting the `REACT_APP_API_URL` environment variable: + +```bash +REACT_APP_API_URL=http://your-api-host:8000 npm run start:dev +``` + +## Usage + +### Creating a New Session +1. Click the "New" button in the sidebar +2. Enter the JIRA key (e.g., RHOAI-1234) +3. Optionally enable "Soft Mode" for less strict processing +4. Click "Create Session" + +### Viewing Session Details +1. Select a session from the sidebar +2. View real-time chat and activity in the center panel +3. Browse output files in the right panel using tabs +4. Filter messages by stage using the dropdown + +### Managing Sessions +- **Delete**: Click the trash icon next to any session +- **Refresh**: Sessions auto-refresh, but you can manually refresh the page +- **Collapse Sidebar**: Click the arrow icon to collapse/expand the sidebar + +## Technical Details + +### Dependencies +- **React 18**: Modern React with hooks +- **PatternFly 6**: Enterprise-grade UI components +- **axios**: HTTP client for API communication +- **react-markdown**: Markdown rendering with GitHub Flavored Markdown support +- **TypeScript**: Type safety throughout the application + +### API Integration +The UI communicates with the backend API through: +- REST endpoints for session management +- Real-time polling for updates +- Structured data types for type safety + +### Responsive Design +- Mobile-friendly layout +- Collapsible sidebar for smaller screens +- Adaptive component sizing +- Accessibility features built-in + +## Troubleshooting + +### Common Issues + +1. **API Connection Error**: + - Ensure the backend API is running on the correct port + - Check the `REACT_APP_API_URL` environment variable + +2. **Build Warnings**: + - Node version warnings are expected with Node 18 + - Bundle size warnings are normal for PatternFly applications + +3. **Session Not Loading**: + - Check browser console for API errors + - Verify the session ID is valid + - Ensure the backend is processing the session + +### Performance Notes +- Auto-refresh intervals are optimized for real-time updates +- Large markdown files are contained in scrollable areas +- Bundle size is optimized for production builds + +## Architecture + +The UI follows a component-based architecture: + +``` +src/ +├── components/ +│ ├── SessionSidebar.tsx # Session management sidebar +│ ├── ChatPanel.tsx # Chat and MCP usage display +│ ├── OutputPanel.tsx # Markdown output with tabs +│ └── SessionManager.tsx # Main layout coordinator +├── services/ +│ └── api.ts # API service layer +├── types/ +│ └── api.ts # TypeScript definitions +└── config.ts # Configuration management +``` + +This architecture ensures maintainability, type safety, and clear separation of concerns. \ No newline at end of file diff --git a/frontend/LICENSE b/frontend/LICENSE new file mode 100644 index 000000000..46a7d0443 --- /dev/null +++ b/frontend/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018 Red Hat + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 000000000..f4507bc19 --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,103 @@ +# Patternfly Seed + +Patternfly Seed is an open source build scaffolding utility for web apps. The primary purpose of this project is to give developers a jump start when creating new projects that will use patternfly. A secondary purpose of this project is to serve as a reference for how to configure various aspects of an application that uses patternfly, webpack, react, typescript, etc. + +Out of the box you'll get an app layout with chrome (header/sidebar), routing, build pipeline, test suite, and some code quality tools. Basically, all the essentials. + +Out of box dashboard view of patternfly seed + +## Quick-start + +```bash +git clone https://github.com/patternfly/patternfly-react-seed +cd patternfly-react-seed +npm install && npm run start:dev +``` +## Development scripts +```sh +# Install development/build dependencies +npm install + +# Start the development server +npm run start:dev + +# Run a production build (outputs to "dist" dir) +npm run build + +# Run the test suite +npm run test + +# Run the test suite with coverage +npm run test:coverage + +# Run the linter +npm run lint + +# Run the code formatter +npm run format + +# Launch a tool to inspect the bundle size +npm run bundle-profile:analyze + +# Start the express server (run a production build first) +npm run start +``` + +## Configurations +* [TypeScript Config](./tsconfig.json) +* [Webpack Config](./webpack.common.js) +* [Jest Config](./jest.config.js) +* [Editor Config](./.editorconfig) + +## Raster image support + +To use an image asset that's shipped with PatternFly core, you'll prefix the paths with "@assets". `@assets` is an alias for the PatternFly assets directory in node_modules. + +For example: +```js +import imgSrc from '@assets/images/g_sizing.png'; +Some image +``` + +You can use a similar technique to import assets from your local app, just prefix the paths with "@app". `@app` is an alias for the main src/app directory. + +```js +import loader from '@app/assets/images/loader.gif'; +Content loading +``` + +## Vector image support +Inlining SVG in the app's markup is also possible. + +```js +import logo from '@app/assets/images/logo.svg'; + +``` + +You can also use SVG when applying background images with CSS. To do this, your SVG's must live under a `bgimages` directory (this directory name is configurable in [webpack.common.js](./webpack.common.js#L5)). This is necessary because you may need to use SVG's in several other context (inline images, fonts, icons, etc.) and so we need to be able to differentiate between these usages so the appropriate loader is invoked. +```css +body { + background: url(./assets/bgimages/img_avatar.svg); +} +``` + +## Adding custom CSS +When importing CSS from a third-party package for the first time, you may encounter the error `Module parse failed: Unexpected token... You may need an appropriate loader to handle this file typ...`. You need to register the path to the stylesheet directory in [stylePaths.js](./stylePaths.js). We specify these explicitly for performance reasons to avoid webpack needing to crawl through the entire node_modules directory when parsing CSS modules. + +## Code quality tools +* For accessibility compliance, we use [react-axe](https://github.com/dequelabs/react-axe) +* To keep our bundle size in check, we use [webpack-bundle-analyzer](https://github.com/webpack-contrib/webpack-bundle-analyzer) +* To keep our code formatting in check, we use [prettier](https://github.com/prettier/prettier) +* To keep our code logic and test coverage in check, we use [jest](https://github.com/facebook/jest) +* To ensure code styles remain consistent, we use [eslint](https://eslint.org/) + +## Multi environment configuration +This project uses [dotenv-webpack](https://www.npmjs.com/package/dotenv-webpack) for exposing environment variables to your code. Either export them at the system level like `export MY_ENV_VAR=http://dev.myendpoint.com && npm run start:dev` or simply drop a `.env` file in the root that contains your key-value pairs like below: + +```sh +ENV_1=http://1.myendpoint.com +ENV_2=http://2.myendpoint.com +``` + + +With that in place, you can use the values in your code like `console.log(process.env.ENV_1);` diff --git a/frontend/__mocks__/fileMock.js b/frontend/__mocks__/fileMock.js new file mode 100644 index 000000000..86059f362 --- /dev/null +++ b/frontend/__mocks__/fileMock.js @@ -0,0 +1 @@ +module.exports = 'test-file-stub'; diff --git a/frontend/__mocks__/styleMock.js b/frontend/__mocks__/styleMock.js new file mode 100644 index 000000000..f053ebf79 --- /dev/null +++ b/frontend/__mocks__/styleMock.js @@ -0,0 +1 @@ +module.exports = {}; diff --git a/frontend/jest.config.js b/frontend/jest.config.js new file mode 100644 index 000000000..fe74bc887 --- /dev/null +++ b/frontend/jest.config.js @@ -0,0 +1,32 @@ +// For a detailed explanation regarding each configuration property, visit: +// https://jestjs.io/docs/en/configuration.html + +module.exports = { + // Automatically clear mock calls and instances between every test + clearMocks: true, + + // Indicates whether the coverage information should be collected while executing the test + collectCoverage: false, + + // The directory where Jest should output its coverage files + coverageDirectory: 'coverage', + + // An array of directory names to be searched recursively up from the requiring module's location + moduleDirectories: [ + "node_modules", + "/src" + ], + + // A map from regular expressions to module names that allow to stub out resources with a single module + moduleNameMapper: { + '\\.(css|less)$': '/__mocks__/styleMock.js', + "\\.(jpg|jpeg|png|gif|eot|otf|webp|svg|ttf|woff|woff2|mp4|webm|wav|mp3|m4a|aac|oga)$": "/__mocks__/fileMock.js", + "@app/(.*)": '/src/app/$1' + }, + + // A preset that is used as a base for Jest's configuration + preset: "ts-jest/presets/js-with-ts", + + // The test environment that will be used for testing. + testEnvironment: "jest-fixed-jsdom", +}; diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 000000000..70363a48c --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,21524 @@ +{ + "name": "patternfly-seed", + "version": "0.0.2", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "patternfly-seed", + "version": "0.0.2", + "license": "MIT", + "dependencies": { + "@patternfly/chatbot": "^6.3.0-prerelease.25", + "@patternfly/react-core": "^6.0.0", + "@patternfly/react-icons": "^6.0.0", + "@patternfly/react-styles": "^6.0.0", + "axios": "^1.10.0", + "react": "^18", + "react-dom": "^18", + "react-markdown": "^10.1.0", + "remark-gfm": "^4.0.1", + "sirv-cli": "^3.0.0" + }, + "devDependencies": { + "@testing-library/jest-dom": "^6.6.3", + "@testing-library/react": "^16.2.0", + "@testing-library/user-event": "^14.6.1", + 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"license": "MIT", + "private": true, + "scripts": { + "prebuild": "npm run type-check && npm run clean", + "build": "webpack --config webpack.prod.js", + "start": "sirv dist --cors --single --host --port 8080", + "start:dev": "webpack serve --color --progress --config webpack.dev.js", + "test": "jest", + "test:watch": "jest --watch", + "test:coverage": "jest --coverage", + "eslint": "eslint --ext .tsx,.js ./src/", + "lint": "npm run eslint", + "format": "prettier --check --write ./src/**/*.{tsx,ts}", + "type-check": "tsc --noEmit", + "ci-checks": "npm run type-check && npm run lint && npm run test:coverage", + "build:bundle-profile": "webpack --config webpack.prod.js --profile --json > stats.json", + "bundle-profile:analyze": "npm run build:bundle-profile && webpack-bundle-analyzer ./stats.json", + "clean": "rimraf dist" + }, + "devDependencies": { + "@testing-library/jest-dom": "^6.6.3", + "@testing-library/react": "^16.2.0", + "@testing-library/user-event": "^14.6.1", + "@types/jest": "^29.5.14", + "@types/react-router-dom": "^5.3.3", + "@typescript-eslint/eslint-plugin": "^8.17.0", + "@typescript-eslint/parser": "^8.17.0", + "copy-webpack-plugin": "^12.0.2", + "css-loader": "^7.1.2", + "css-minimizer-webpack-plugin": "^7.0.0", + "dotenv-webpack": "^8.1.0", + "eslint": "^8.57.1", + "eslint-plugin-prettier": "^5.2.1", + "eslint-plugin-react": "^7.37.2", + "eslint-plugin-react-hooks": "^5.0.0", + "html-webpack-plugin": "^5.6.3", + "imagemin": "^9.0.0", + "jest-environment-jsdom": "^29.7.0", + "jest-fixed-jsdom": "^0.0.9", + "mini-css-extract-plugin": "^2.9.2", + "postcss": "^8.4.49", + "prettier": "^3.4.2", + "prop-types": "^15.8.1", + "raw-loader": "^4.0.2", + "react-axe": "^3.5.4", + "react-docgen-typescript-loader": "^3.7.2", + "react-router-dom": "^7.0.2", + "regenerator-runtime": "^0.14.1", + "rimraf": "^6.0.1", + "style-loader": "^4.0.0", + "svg-url-loader": "^8.0.0", + "terser-webpack-plugin": "^5.3.10", + "ts-jest": "^29.2.5", + "ts-loader": "^9.5.1", + "tsconfig-paths-webpack-plugin": "^4.2.0", + "tslib": "^2.8.1", + "typescript": "^5.7.2", + "url-loader": "^4.1.1", + "webpack": "^5.97.0", + "webpack-bundle-analyzer": "^4.10.2", + "webpack-cli": "^5.1.4", + "webpack-dev-server": "^5.1.0", + "webpack-merge": "^6.0.1" + }, + "dependencies": { + "@patternfly/chatbot": "^6.3.0-prerelease.25", + "@patternfly/react-core": "^6.0.0", + "@patternfly/react-icons": "^6.0.0", + "@patternfly/react-styles": "^6.0.0", + "axios": "^1.10.0", + "react": "^18", + "react-dom": "^18", + "react-markdown": "^10.1.0", + "remark-gfm": "^4.0.1", + "sirv-cli": "^3.0.0" + }, + "packageManager": "yarn@1.22.22+sha512.a6b2f7906b721bba3d67d4aff083df04dad64c399707841b7acf00f6b133b7ac24255f2652fa22ae3534329dc6180534e98d17432037ff6fd140556e2bb3137e" +} diff --git a/frontend/src/app/AppLayout/AppLayout.tsx b/frontend/src/app/AppLayout/AppLayout.tsx new file mode 100644 index 000000000..164290cdf --- /dev/null +++ b/frontend/src/app/AppLayout/AppLayout.tsx @@ -0,0 +1,114 @@ +import * as React from 'react'; +import { useNavigate } from 'react-router-dom'; +import { + Masthead, + MastheadBrand, + MastheadLogo, + MastheadMain, + Page, + PageSidebar, + PageSidebarBody, + SkipToContent, +} from '@patternfly/react-core'; +// import { BarsIcon } from '@patternfly/react-icons'; +import SessionSidebar from '../../components/SessionSidebar'; +import { Session } from '../../types/api'; + +interface IAppLayout { + children: React.ReactNode; +} + +const AppLayout: React.FunctionComponent = ({ children }) => { + const [selectedSession, setSelectedSession] = React.useState(null); + const navigate = useNavigate(); + + const handleSessionSelect = (session: Session) => { + setSelectedSession(session); + console.log(session); + navigate(`/sessions/${session.id}`); + }; + + const masthead = ( + + + + + + JIRA RFE Sessions + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ); + + const Sidebar = ( + + + + + + ); + + const pageId = 'primary-app-container'; + + const PageSkipToContent = ( + { + event.preventDefault(); + const primaryContentContainer = document.getElementById(pageId); + primaryContentContainer?.focus(); + }} + href={`#${pageId}`} + > + Skip to Content + + ); + + return ( + + {children} + + ); +}; + +export { AppLayout }; diff --git a/frontend/src/app/Dashboard/Dashboard.tsx b/frontend/src/app/Dashboard/Dashboard.tsx new file mode 100644 index 000000000..d40e696d4 --- /dev/null +++ b/frontend/src/app/Dashboard/Dashboard.tsx @@ -0,0 +1,6 @@ +import * as React from 'react'; +import SessionManager from '../../components/SessionManager'; + +const Dashboard: React.FunctionComponent = () => ; + +export { Dashboard }; diff --git a/frontend/src/app/NotFound/NotFound.tsx b/frontend/src/app/NotFound/NotFound.tsx new file mode 100644 index 000000000..d6b3fcef2 --- /dev/null +++ b/frontend/src/app/NotFound/NotFound.tsx @@ -0,0 +1,35 @@ +import * as React from 'react'; +import { ExclamationTriangleIcon } from '@patternfly/react-icons'; +import { + Button, + EmptyState, + EmptyStateBody, + EmptyStateFooter, + PageSection, +} from '@patternfly/react-core'; +import { useNavigate } from 'react-router-dom'; + +const NotFound: React.FunctionComponent = () => { + function GoHomeBtn() { + const navigate = useNavigate(); + function handleClick() { + navigate('/'); + } + return ( + + ); + } + + return ( + + + + We didn't find a page that matches the address you navigated to. + + + + + ) +}; + +export { NotFound }; diff --git a/frontend/src/app/__snapshots__/app.test.tsx.snap b/frontend/src/app/__snapshots__/app.test.tsx.snap new file mode 100644 index 000000000..91140c701 --- /dev/null +++ b/frontend/src/app/__snapshots__/app.test.tsx.snap @@ -0,0 +1,314 @@ +// Jest Snapshot v1, https://goo.gl/fbAQLP + +exports[`App tests should render default App component 1`] = ` + +
+
+ + + Skip to Content + + +
+
+
+ + + +
+ +
+
+
+
+
+ +
+
+
+
+
+

+ Dashboard Page Title! +

+
+
+
+
+ +`; diff --git a/frontend/src/app/app.css b/frontend/src/app/app.css new file mode 100644 index 000000000..afeae2aab --- /dev/null +++ b/frontend/src/app/app.css @@ -0,0 +1,13 @@ +@import '@patternfly/chatbot/dist/css/main.css'; + +html, +body, +#root { + height: 100%; +} + +.pf-v6-c-content { + --pf-v6-c-content--small--Color: var(--pf-t--global--color--status--danger--default); /* changes all color to the semantic token for danger */ + --pf-v6-c-content--blockquote--BorderLeftColor: var(--pf-t--global--color--nonstatus--purple--default); /* changes all
left border color to the semantic token for non-status (purple) */ + --pf-v6-c-content--hr--BackgroundColor: var(--pf-t--global--color--nonstatus--yellow--default); /* changes a
color to the semantic token for non-status (yellow) */ +} diff --git a/frontend/src/app/app.test.tsx b/frontend/src/app/app.test.tsx new file mode 100644 index 000000000..8baed345b --- /dev/null +++ b/frontend/src/app/app.test.tsx @@ -0,0 +1,55 @@ +import * as React from 'react'; +import App from '@app/index'; +import { render, screen } from '@testing-library/react'; +import userEvent from '@testing-library/user-event'; +import '@testing-library/jest-dom'; + +describe('App tests', () => { + test('should render default App component', () => { + const { asFragment } = render(); + + expect(asFragment()).toMatchSnapshot(); + }); + + it('should render a nav-toggle button', () => { + render(); + + expect(screen.getByRole('button', { name: 'Global navigation' })).toBeVisible(); + }); + + // I'm fairly sure that this test not going to work properly no matter what we do since JSDOM doesn't actually + // draw anything. We could potentially make something work, likely using a different test environment, but + // using Cypress for this kind of test would be more efficient. + it.skip('should hide the sidebar on smaller viewports', () => { + Object.defineProperty(window, 'innerWidth', { writable: true, configurable: true, value: 600 }); + + render(); + + window.dispatchEvent(new Event('resize')); + + expect(screen.queryByRole('link', { name: 'Dashboard' })).not.toBeInTheDocument(); + }); + + it('should expand the sidebar on larger viewports', () => { + render(); + + window.dispatchEvent(new Event('resize')); + + expect(screen.getByRole('link', { name: 'Dashboard' })).toBeVisible(); + }); + + it('should hide the sidebar when clicking the nav-toggle button', async () => { + const user = userEvent.setup(); + + render(); + + window.dispatchEvent(new Event('resize')); + const button = screen.getByRole('button', { name: 'Global navigation' }); + + expect(screen.getByRole('link', { name: 'Dashboard' })).toBeVisible(); + + await user.click(button); + + expect(screen.queryByRole('link', { name: 'Dashboard' })).not.toBeInTheDocument(); + }); +}); diff --git a/frontend/src/app/bgimages/Patternfly-Logo.svg b/frontend/src/app/bgimages/Patternfly-Logo.svg new file mode 100644 index 000000000..7b105b833 --- /dev/null +++ b/frontend/src/app/bgimages/Patternfly-Logo.svg @@ -0,0 +1,28 @@ + + + Patternfly Logo + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/frontend/src/app/index.tsx b/frontend/src/app/index.tsx new file mode 100644 index 000000000..82b7700b7 --- /dev/null +++ b/frontend/src/app/index.tsx @@ -0,0 +1,16 @@ +import * as React from 'react'; +import '@patternfly/react-core/dist/styles/base.css'; +import { BrowserRouter as Router } from 'react-router-dom'; +import { AppLayout } from '@app/AppLayout/AppLayout'; +import { AppRoutes } from '@app/routes'; +import '@app/app.css'; + +const App: React.FunctionComponent = () => ( + + + + + +); + +export default App; diff --git a/frontend/src/app/routes.tsx b/frontend/src/app/routes.tsx new file mode 100644 index 000000000..b913f62b3 --- /dev/null +++ b/frontend/src/app/routes.tsx @@ -0,0 +1,53 @@ +import * as React from 'react'; +import { Route, Routes } from 'react-router-dom'; +import { NotFound } from '@app/NotFound/NotFound'; +import SessionManager from '../components/SessionManager'; + +export interface IAppRoute { + label?: string; // Excluding the label will exclude the route from the nav sidebar in AppLayout + /* eslint-disable @typescript-eslint/no-explicit-any */ + element: React.ReactElement; + /* eslint-enable @typescript-eslint/no-explicit-any */ + exact?: boolean; + path: string; + title: string; + routes?: undefined; +} + +export interface IAppRouteGroup { + label: string; + routes: IAppRoute[]; +} + +export type AppRouteConfig = IAppRoute | IAppRouteGroup; + +const routes: AppRouteConfig[] = [ + { + element: , + exact: true, + path: '/', + title: 'JIRA RFE Sessions | Dashboard', + }, + { + element: , + exact: true, + path: '/sessions/:sessionId', + title: 'JIRA RFE Sessions | Session Details', + }, +]; + +const flattenedRoutes: IAppRoute[] = routes.reduce( + (flattened, route) => [...flattened, ...(route.routes ? route.routes : [route])], + [] as IAppRoute[], +); + +const AppRoutes = (): React.ReactElement => ( + + {flattenedRoutes.map(({ path, element }, idx) => ( + + ))} + } /> + +); + +export { AppRoutes, routes }; diff --git a/frontend/src/assets/robot-solid.svg b/frontend/src/assets/robot-solid.svg new file mode 100644 index 000000000..c0bd68210 --- /dev/null +++ b/frontend/src/assets/robot-solid.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/assets/user-solid.svg b/frontend/src/assets/user-solid.svg new file mode 100644 index 000000000..c35a87000 --- /dev/null +++ b/frontend/src/assets/user-solid.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/components/ChatPanel.tsx b/frontend/src/components/ChatPanel.tsx new file mode 100644 index 000000000..622545974 --- /dev/null +++ b/frontend/src/components/ChatPanel.tsx @@ -0,0 +1,414 @@ +import React, { useCallback, useEffect, useMemo, useState } from 'react'; +import { + Alert, + Badge, + Card, + CardBody, + CardHeader, + CardTitle, + Dropdown, + DropdownItem, + DropdownList, + EmptyState, + EmptyStateBody, + ExpandableSection, + Flex, + FlexItem, + MenuToggle, + MenuToggleElement, + Panel, + PanelHeader, + PanelMain, + PanelMainBody, + Spinner, + Title, +} from '@patternfly/react-core'; +import { CommentIcon, FilterIcon } from '@patternfly/react-icons'; +import Message from '@patternfly/chatbot/dist/dynamic/Message'; +import { MCPUsage, Message as MessageType, Session, Stage } from '../types/api'; +import { apiService } from '../services/api'; +import { WebSocketMessage, webSocketService } from '../services/websocket'; +import UserIconSvg from '../assets/user-solid.svg'; +import RobotIconSvg from '../assets/robot-solid.svg'; + +type ChatPanelProps = { + session: Session; +}; + +const formatTimestamp = (timestamp: string) => { + return new Date(timestamp).toLocaleString(); +}; + +const getStageColor = (stage: Stage): 'blue' | 'green' | 'orange' | 'red' | 'grey' => { + switch (stage) { + case 'refine': + return 'blue'; + case 'epics': + return 'orange'; + case 'jiras': + return 'green'; + case 'estimate': + return 'grey'; + default: + return 'grey'; + } +}; + +const renderMessage = (message: MessageType, userAvatarUrl: string, robotAvatarUrl: string) => { + const isError: boolean = message.status === 'error'; + const isLoading: boolean = message.status === 'loading'; + + return ( + + + + {message.stage} + + + + ), + }} + /> + ); +}; + +const ChatPanel: React.FunctionComponent = React.memo(({ session }) => { + const [messages, setMessages] = useState([]); + const [mcpUsages, setMcpUsages] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const [selectedStage, setSelectedStage] = useState(null); + const [isStageDropdownOpen, setIsStageDropdownOpen] = useState(false); + + // Convert raw SVG strings to data URLs + const userAvatarUrl = `data:image/svg+xml;utf8,${encodeURIComponent(UserIconSvg)}`; + const robotAvatarUrl = `data:image/svg+xml;utf8,${encodeURIComponent(RobotIconSvg)}`; + + const loadChatData = useCallback( + async (showLoader: boolean = false) => { + if (!session) return; + + try { + if (showLoader) { + setLoading(true); + } + setError(null); + + const [messagesResponse, mcpUsageResponse] = await Promise.all([ + apiService.getSessionMessages(session.id, 100, selectedStage || undefined), + apiService.getSessionMCPUsage(session.id), + ]); + + // Only update state if data has actually changed + if (JSON.stringify(messagesResponse) !== JSON.stringify(messages)) { + setMessages(messagesResponse); + } + if (JSON.stringify(mcpUsageResponse) !== JSON.stringify(mcpUsages)) { + setMcpUsages(mcpUsageResponse); + } + } catch (err) { + setError(err instanceof Error ? err.message : 'Failed to load chat data'); + } finally { + if (showLoader) { + setLoading(false); + } + } + }, + [session, selectedStage, messages, mcpUsages], + ); + + useEffect(() => { + loadChatData(true); // Show loader for initial load and stage changes + }, [session, selectedStage, loadChatData]); + + // WebSocket connection for real-time updates + useEffect(() => { + if (!session) return; + + // Connect to WebSocket + webSocketService.connect(session.id); + + // Subscribe to messages + const unsubscribe = webSocketService.subscribe(session.id, (wsMessage: WebSocketMessage) => { + if (wsMessage.type === 'message') { + const message = wsMessage.data as MessageType; + if (!selectedStage || message.stage === selectedStage) { + setMessages((prevMessages) => { + // Check if message already exists to avoid duplicates + const exists = prevMessages.some((m) => m.id === message.id); + if (!exists) { + return [...prevMessages, message].sort( + (a, b) => new Date(a.timestamp).getTime() - new Date(b.timestamp).getTime(), + ); + } + return prevMessages; + }); + } + } else if (wsMessage.type === 'mcp_usage') { + const mcpUsage = wsMessage.data as MCPUsage; + if (!selectedStage || mcpUsage.stage === selectedStage) { + setMcpUsages((prevUsages) => { + // Check if usage already exists to avoid duplicates + const exists = prevUsages.some((u) => u.id === mcpUsage.id); + if (!exists) { + return [...prevUsages, mcpUsage].sort( + (a, b) => new Date(a.timestamp).getTime() - new Date(b.timestamp).getTime(), + ); + } + return prevUsages; + }); + } + } + }); + + // Cleanup on unmount + return () => { + unsubscribe(); + webSocketService.disconnect(); + }; + }, [session, selectedStage]); + + const filteredMcpUsages = useMemo(() => { + return mcpUsages.filter((usage) => !selectedStage || usage.stage === selectedStage); + }, [mcpUsages, selectedStage]); + + // Create a merged timeline of messages and MCP usage + type TimelineItem = { + type: 'message' | 'mcp'; + timestamp: string; + data: MessageType | MCPUsage; + }; + + const timeline = useMemo(() => { + const timelineItems: TimelineItem[] = [ + ...messages.map( + (message): TimelineItem => ({ + type: 'message', + timestamp: message.timestamp, + data: message, + }), + ), + ...filteredMcpUsages.map( + (usage): TimelineItem => ({ + type: 'mcp', + timestamp: usage.timestamp, + data: usage, + }), + ), + ]; + + return timelineItems.sort((a, b) => new Date(a.timestamp).getTime() - new Date(b.timestamp).getTime()); + }, [messages, filteredMcpUsages]); + + const stages: Stage[] = ['refine', 'epics', 'jiras', 'estimate']; + + const stageDropdownItems = [ + setSelectedStage(null)}> + All Stages + , + ...stages.map((stage) => ( + setSelectedStage(stage)}> + {stage.charAt(0).toUpperCase() + stage.slice(1)} + + )), + ]; + + if (loading) { + return ( + + + + + + + + + + ); + } + + if (error) { + return ( + + + + + {error} + + + + + ); + } + + return ( + + + + + + Session Chat & Activity + + + + setIsStageDropdownOpen(false)} + toggle={(toggleRef: React.Ref) => ( + setIsStageDropdownOpen(!isStageDropdownOpen)} + isExpanded={isStageDropdownOpen} + icon={} + > + {selectedStage ? selectedStage.charAt(0).toUpperCase() + selectedStage.slice(1) : 'All Stages'} + + )} + > + {stageDropdownItems} + + + + + + + + + + + + + + + Session Timeline ({timeline.length}) + + + + + {timeline.length === 0 ? ( + + + No activity yet. Messages and tool usage will appear here as the session progresses. + + + ) : ( + + {timeline.map((item, index) => ( + + {item.type === 'message' ? ( +
{renderMessage(item.data as MessageType, userAvatarUrl, robotAvatarUrl)}
+ ) : ( +
+ + {(item.data as MCPUsage).stage} + + ), + afterMainContent: ( + + + + + +
+ 
+                                              {JSON.stringify((item.data as MCPUsage).input_data, null, 2)}
+
+
+ + + + +
+ 
+                                              {JSON.stringify((item.data as MCPUsage).output_data, null, 2)}
+
+
+ + + + + + ), + }} + /> +
+ )} + + ))} + + )} + + + + + + ); +}); + +ChatPanel.displayName = 'ChatPanel'; + +export default ChatPanel; diff --git a/frontend/src/components/OutputPanel.tsx b/frontend/src/components/OutputPanel.tsx new file mode 100644 index 000000000..09ec077d1 --- /dev/null +++ b/frontend/src/components/OutputPanel.tsx @@ -0,0 +1,448 @@ +import React, { useCallback, useEffect, useMemo, useState } from 'react'; +import { + Alert, + Badge, + Card, + CardBody, + CardHeader, + CardTitle, + EmptyState, + EmptyStateBody, + Flex, + FlexItem, + Panel, + PanelHeader, + PanelMain, + PanelMainBody, + Spinner, + Tab, + TabContent, + TabTitleText, + Tabs, + Title, +} from '@patternfly/react-core'; +import { FileIcon } from '@patternfly/react-icons'; +import { Output, Session, Stage } from '../types/api'; +import { apiService } from '../services/api'; +import Markdown from 'react-markdown'; +import remarkGfm from 'remark-gfm'; + +type OutputPanelProps = { + session: Session; +}; + +const getStageColor = (stage: Stage): 'blue' | 'green' | 'orange' | 'red' | 'grey' => { + switch (stage) { + case 'refine': + return 'blue'; + case 'epics': + return 'orange'; + case 'jiras': + return 'green'; + case 'estimate': + return 'grey'; + default: + return 'grey'; + } +}; + +const getStageLabel = (stage: Stage): string => { + switch (stage) { + case 'refine': + return 'Refinement'; + case 'epics': + return 'Epics'; + case 'jiras': + return 'JIRAs'; + case 'estimate': + return 'Estimate'; + default: + return stage; + } +}; + +const OutputPanel: React.FunctionComponent = React.memo(({ session }) => { + const [outputs, setOutputs] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const [activeTabKey, setActiveTabKey] = useState(''); + + const loadOutputs = useCallback( + async (showLoader: boolean = false) => { + if (!session) return; + + try { + if (showLoader) { + setLoading(true); + } + setError(null); + + const outputsResponse = await apiService.getSessionOutputs(session.id); + + // Only update state if data has actually changed + if (JSON.stringify(outputsResponse) !== JSON.stringify(outputs)) { + setOutputs(outputsResponse); + } + + // Set the first available tab as active + if (outputsResponse.length > 0 && !activeTabKey) { + setActiveTabKey(outputsResponse[0].stage); + } + } catch (err) { + setError(err instanceof Error ? err.message : 'Failed to load outputs'); + } finally { + if (showLoader) { + setLoading(false); + } + } + }, + [session, outputs, activeTabKey], + ); + + useEffect(() => { + loadOutputs(true); // Show loader for initial load + }, [session]); + + // Auto-refresh every 5 seconds when session is running + useEffect(() => { + if (session.status === 'running' || session.status === 'pending') { + const interval = setInterval(() => loadOutputs(false), 5000); // Don't show loader for auto-refresh + return () => clearInterval(interval); + } + return undefined; + }, [session.status, loadOutputs]); + + const handleTabClick = useCallback((event: React.MouseEvent, tabIndex: string | number) => { + setActiveTabKey(tabIndex.toString()); + }, []); + + // Group outputs by stage - memoized to prevent unnecessary recalculation + const outputsByStage = useMemo(() => { + return outputs.reduce( + (acc, output) => { + if (!acc[output.stage]) { + acc[output.stage] = []; + } + acc[output.stage].push(output); + return acc; + }, + {} as Record, + ); + }, [outputs]); + + const stages: Stage[] = ['refine', 'epics', 'jiras', 'estimate']; + const availableStages = useMemo(() => { + return stages.filter((stage) => outputsByStage[stage]?.length > 0); + }, [stages, outputsByStage]); + + if (loading) { + return ( + + + + + + + + + + ); + } + + if (error) { + return ( + + + + + {error} + + + + + ); + } + + if (outputs.length === 0) { + return ( + + + + Session Outputs + + + + + + + + No outputs yet. Output files will appear here as the session progresses through different stages. + + + + + + ); + } + + return ( + + + + Session Outputs + + + + + + + {availableStages.map((stage) => ( + + + {getStageLabel(stage)} + + {outputsByStage[stage].length} + + + + } + > + + + {outputsByStage[stage].map((output, index) => ( + + + + + + + + + {output.filename} + + {output.stage} + + + + + +
+ ( +

+ {children} +

+ ), + h2: ({ children }) => ( +

+ {children} +

+ ), + h3: ({ children }) => ( +

+ {children} +

+ ), + p: ({ children }) => ( +

+ {children} +

+ ), + ul: ({ children }) => ( +
    + {children} +
+ ), + ol: ({ children }) => ( +
    + {children} +
+ ), + li: ({ children }) => ( +
  • + {children} +
    • + ), + code: ({ children, className }) => { + const isInline = !className?.includes('language-'); + return isInline ? ( + + {children} + + ) : ( + + {children} + + ); + }, + pre: ({ children }) => 
    {children}
    , + table: ({ children }) => ( + + {children} +
    + ), + th: ({ children }) => ( + + {children} + + ), + td: ({ children }) => ( + + {children} + + ), + blockquote: ({ children }) => ( +
    + {children} +
    + ), + strong: ({ children }) => ( + + {children} + + ), + em: ({ children }) => ( + + {children} + + ), + }} + > + {(() => { + // Strip markdown code fence wrapper if present + let content = output.content; + if (content.startsWith('```markdown\n') || content.startsWith('```markdown ')) { + content = content.replace(/^```markdown\s*\n?/, ''); + } + if (content.endsWith('\n```') || content.endsWith('```')) { + content = content.replace(/\n?```$/, ''); + } + return content; + })()} +     +
    +     +     +     + ))} +     +     +     + ))} +     +     +     +     + ); +}); + +OutputPanel.displayName = 'OutputPanel'; + +export default OutputPanel; diff --git a/frontend/src/components/SessionManager.tsx b/frontend/src/components/SessionManager.tsx new file mode 100644 index 000000000..b9749be1f --- /dev/null +++ b/frontend/src/components/SessionManager.tsx @@ -0,0 +1,117 @@ +import React, { useEffect, useState } from 'react'; +import { useParams } from 'react-router-dom'; +import { Alert, Flex, FlexItem, PageSection, Split, SplitItem, Title } from '@patternfly/react-core'; +import { Session } from '../types/api'; +import { apiService } from '../services/api'; +import ChatPanel from './ChatPanel'; +import OutputPanel from './OutputPanel'; + +const SessionManager: React.FunctionComponent = () => { + const { sessionId } = useParams<{ sessionId: string }>(); + const [selectedSession, setSelectedSession] = useState(null); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + + // Load session when sessionId changes + useEffect(() => { + if (sessionId) { + loadSession(sessionId); + } else { + setSelectedSession(null); + } + }, [sessionId]); + + const loadSession = async (id: string) => { + try { + setLoading(true); + setError(null); + const session = await apiService.getSession(id); + setSelectedSession(session); + } catch (err) { + setError(err instanceof Error ? err.message : 'Failed to load session'); + setSelectedSession(null); + } finally { + setLoading(false); + } + }; + + if (loading) { + return ( + + + + + Loading session... + + + + + ); + } + + if (error) { + return ( + + + + + {error} + + + + + ); + } + + return ( + + {selectedSession ? ( + + {/* Chat Panel - Left Side */} + + + + + {/* Output Panel - Right Side */} + + + + + ) : ( + + + + JIRA RFE Session Manager + + + + + Select a session from the sidebar to view its chat and output files, or create a new session to get + started. + + + + )} + + ); +}; + +export default SessionManager; diff --git a/frontend/src/components/SessionSidebar.tsx b/frontend/src/components/SessionSidebar.tsx new file mode 100644 index 000000000..16fac0e2c --- /dev/null +++ b/frontend/src/components/SessionSidebar.tsx @@ -0,0 +1,378 @@ +import React, { useCallback, useEffect, useState } from 'react'; +import { useNavigate } from 'react-router-dom'; +import { + Alert, + Badge, + Button, + Checkbox, + EmptyState, + EmptyStateBody, + Flex, + FlexItem, + FormGroup, + List, + ListItem, + Modal, + ModalBody, + ModalFooter, + ModalHeader, + ModalVariant, + Pagination, + PaginationVariant, + Spinner, + TextInput, + Title, +} from '@patternfly/react-core'; +import { CubeIcon, PlusIcon, TrashIcon } from '@patternfly/react-icons'; +import { CreateSessionRequest, Session, SessionStatus } from '../types/api'; +import { apiService } from '../services/api'; + +type SessionSidebarProps = { + selectedSession: Session | null; + onSessionSelect: (session: Session) => void; +}; + +const getStatusColor = (status: SessionStatus): 'blue' | 'green' | 'red' | 'orange' | 'grey' => { + switch (status) { + case 'pending': + return 'grey'; + case 'running': + return 'blue'; + case 'completed': + return 'green'; + case 'failed': + return 'red'; + case 'cancelled': + return 'orange'; + default: + return 'grey'; + } +}; + +const SessionSidebar: React.FunctionComponent = React.memo( + ({ selectedSession, onSessionSelect }) => { + const navigate = useNavigate(); + const [sessions, setSessions] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const [page, setPage] = useState(1); + const [pageSize] = useState(10); + const [totalSessions, setTotalSessions] = useState(0); + const [showCreateModal, setShowCreateModal] = useState(false); + const [jiraKey, setJiraKey] = useState(''); + const [softMode, setSoftMode] = useState(true); + const [creating, setCreating] = useState(false); + const [createError, setCreateError] = useState(null); + + const loadSessions = useCallback( + async (currentPage: number = page, showLoader: boolean = false) => { + try { + if (showLoader) { + setLoading(true); + } + setError(null); + const response = await apiService.listSessions(currentPage, pageSize); + + // Only update state if data has actually changed + if (JSON.stringify(response.sessions) !== JSON.stringify(sessions) || response.total !== totalSessions) { + setSessions(response.sessions); + setTotalSessions(response.total); + } + } catch (err) { + setError(err instanceof Error ? err.message : 'Failed to load sessions'); + } finally { + if (showLoader) { + setLoading(false); + } + } + }, + [page, pageSize, sessions, totalSessions], + ); + + const handleCreateSession = useCallback(async () => { + if (!jiraKey.trim()) { + setCreateError('JIRA key is required'); + return; + } + + try { + setCreating(true); + setCreateError(null); + + const request: CreateSessionRequest = { + jira_key: jiraKey.trim(), + soft_mode: softMode, + }; + + const newSession = await apiService.createSession(request); + + // Add the new session to the beginning of the list + setSessions((prev) => [newSession, ...prev]); + + // Clear form and close modal + setJiraKey(''); + setSoftMode(false); + setShowCreateModal(false); + + // Select the new session + onSessionSelect(newSession); + } catch (err) { + setCreateError(err instanceof Error ? err.message : 'Failed to create session'); + } finally { + setCreating(false); + } + }, [jiraKey, softMode, onSessionSelect]); + + const handleDeleteSession = useCallback( + async (sessionId: string, event: React.MouseEvent) => { + event.stopPropagation(); + + try { + await apiService.deleteSession(sessionId); + setSessions((prev) => prev.filter((s) => s.id !== sessionId)); + + // If the deleted session was selected, clear selection + if (selectedSession?.id === sessionId) { + // Find the next available session to select, or null if none + const remainingSessions = sessions.filter((s) => s.id !== sessionId); + const nextSession = remainingSessions.length > 0 ? remainingSessions[0] : null; + + // This will trigger navigation to the next session or back to the main page + if (nextSession) { + onSessionSelect(nextSession); + } else { + // Navigate back to the main page when no sessions remain + navigate('/'); + } + } + } catch (err) { + console.error('Failed to delete session:', err); + } + }, + [selectedSession, sessions, navigate, onSessionSelect], + ); + + const handlePageChange = useCallback( + (newPage: number) => { + setPage(newPage); + loadSessions(newPage, true); // Show loader for page changes + }, + [loadSessions], + ); + + useEffect(() => { + loadSessions(page, true); // Show loader for initial load + // eslint-disable-next-line react-hooks/exhaustive-deps + }, []); + + // Auto-refresh every 5 seconds when there are running sessions + useEffect(() => { + const hasRunningSessions = sessions.some((s) => s.status === 'running' || s.status === 'pending'); + + if (hasRunningSessions) { + const interval = setInterval(() => { + loadSessions(page, false); // Don't show loader for auto-refresh + }, 5000); + + return () => clearInterval(interval); + } + return undefined; + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [sessions, page]); + + // Component content directly + + return ( + <> + + + + JIRA RFE Sessions + + + + + + + + Sessions + + + + + + + + + + {loading ? ( + + + + ) : error ? ( + + {error} + + ) : sessions.length === 0 ? ( + + + No sessions yet. Create your first session to get started. + + ) : ( + + {sessions.map((session) => ( + onSessionSelect(session)} + style={{ + cursor: 'pointer', + backgroundColor: + selectedSession?.id === session.id + ? 'var(--pf-v5-global--BackgroundColor--200)' + : 'transparent', + padding: '0.75rem', + borderRadius: '4px', + border: + selectedSession?.id === session.id + ? '1px solid var(--pf-v5-global--BorderColor--100)' + : '1px solid transparent', + marginBottom: '0.25rem', + }} + > + + + + + {session.jira_key} + + + + + + + + + + + + ); + }, +); + +SessionSidebar.displayName = 'SessionSidebar'; + +export default SessionSidebar; diff --git a/frontend/src/config.ts b/frontend/src/config.ts new file mode 100644 index 000000000..ccb0d8684 --- /dev/null +++ b/frontend/src/config.ts @@ -0,0 +1,3 @@ +export const config = { + apiUrl: process.env.REACT_APP_API_URL || 'http://localhost:8000', +}; \ No newline at end of file diff --git a/frontend/src/favicon.png b/frontend/src/favicon.png new file mode 100644 index 0000000000000000000000000000000000000000..dccdce0296926f9d0939f2f96c5765e63787d0e9 GIT binary patch literal 706 zcmV;z0zLhSP)NdY%F6&NarnJd8QfLsOmDo8rOC8Pt`WuBNfLNfAaf@U;!99i$}?%UNmO=K+p z{5Nu`WUgiE{iVh^1^A21Jo$mKfaMnaQD%Gmd(-+B3)vOO>L>=_{E}IQ2&^r*06)a; z7$WHqzLHBLbH#4wFlY4R5P^Ggyb%I6GIw(OO5jR(<7zA;i%t<%EBg*V^8ol7z}Dru z{!ZT2ko~4-pCK4JT7*qEs~wN*h?tUnvS9@=>y3@o(ij5&Kp;nq6G~w__Rv&EX}+_e zR{hAcaUPJD5Aw2Ztl-bM8q&ztBhZT0Y~3tKCWL|AtrpjTOeiVSy3qszX{8%3AyD6F zXkG2A12Mn`R#Z+(<_LlQ_uzT&mdI{>B(n%`n^qNBvXRY}5ZgzLvCbuJg{RfkTW7K! zU{(1GTxh6NtGo9kC|f@oheQeQd=Xx62#7gvc7O{zw=l{2f)29=I#Gi6SXTEHxy%7C z;qEoLyO0g;TyUp47UT&H)e&Ah!)wX61O)sD%MGQ~bH$5Wf_@(L7MBTxs@M?ll9aDGfBT&W7C0CZQubJIL#eu#{Z#o|)Pft-gD)p& zI#+8YKn=uuMyhr_T~%UF!*nL_9LkUGtZeABnH*r_-VJH%W+C8dG?I%E@YMVEq6ARy z%~@23ns ody2UV(V$Oq^a$E#oZkf)0KOg$5Dm-T3IG5A07*qoM6N<$f;Q + + + + + Patternfly Seed + + + + + + + + +
    + + + diff --git a/frontend/src/index.tsx b/frontend/src/index.tsx new file mode 100644 index 000000000..8b1898891 --- /dev/null +++ b/frontend/src/index.tsx @@ -0,0 +1,25 @@ +import React from "react"; +import ReactDOM from "react-dom/client"; +import App from '@app/index'; + +if (process.env.NODE_ENV !== "production") { + const config = { + rules: [ + { + id: 'color-contrast', + enabled: false + } + ] + }; + // eslint-disable-next-line @typescript-eslint/no-require-imports + const axe = require("react-axe"); + axe(React, ReactDOM, 1000, config); +} + +const root = ReactDOM.createRoot(document.getElementById("root") as Element); + +root.render( + + + +) diff --git a/frontend/src/services/api.ts b/frontend/src/services/api.ts new file mode 100644 index 000000000..1213dc413 --- /dev/null +++ b/frontend/src/services/api.ts @@ -0,0 +1,110 @@ +import axios, { AxiosInstance } from 'axios'; +import { config } from '../config'; +import { + CreateSessionRequest, + MCPUsage, + Message, + Output, + ProgressResponse, + Session, + SessionDetail, + SessionListResponse, + Stage, +} from '../types/api'; + +class ApiService { + private api: AxiosInstance; + + constructor() { + this.api = axios.create({ + baseURL: config.apiUrl, + headers: { + 'Content-Type': 'application/json', + }, + }); + + // Add request interceptor for logging + this.api.interceptors.request.use( + (config) => { + console.log(`API Request: ${config.method?.toUpperCase()} ${config.url}`); + return config; + }, + (error) => { + return Promise.reject(error); + } + ); + + // Add response interceptor for error handling + this.api.interceptors.response.use( + (response) => { + return response; + }, + (error) => { + console.error('API Error:', error.response?.data || error.message); + return Promise.reject(error); + } + ); + } + + // Health check + async healthCheck(): Promise<{ database: string; llama_stack: string }> { + const response = await this.api.get('/healthz'); + return response.data; + } + + // Session management + async createSession(request: CreateSessionRequest): Promise { + const response = await this.api.post('/sessions', request); + return response.data; + } + + async listSessions(page: number = 1, pageSize: number = 20): Promise { + const response = await this.api.get('/sessions', { + params: { page, page_size: pageSize } + }); + return response.data; + } + + async getSession(sessionId: string): Promise { + const response = await this.api.get(`/sessions/${sessionId}`); + return response.data; + } + + async getSessionProgress(sessionId: string): Promise { + const response = await this.api.get(`/sessions/${sessionId}/progress`); + return response.data; + } + + async getSessionMessages(sessionId: string, limit: number = 50, stage?: Stage): Promise { + const response = await this.api.get(`/sessions/${sessionId}/messages`, { + params: { limit, stage } + }); + return response.data; + } + + async getSessionOutputs(sessionId: string, stage?: Stage): Promise { + const response = await this.api.get(`/sessions/${sessionId}/outputs`, { + params: { stage } + }); + return response.data; + } + + async getSessionOutputByStage(sessionId: string, stage: Stage): Promise { + const response = await this.api.get(`/sessions/${sessionId}/outputs/${stage}`); + return response.data; + } + + async getSessionMCPUsage(sessionId: string): Promise { + const response = await this.api.get(`/sessions/${sessionId}/mcp-usage`); + return response.data; + } + + async deleteSession(sessionId: string): Promise<{ message: string }> { + const response = await this.api.delete(`/sessions/${sessionId}`); + console.log(response); + return response.data; + } +} + +export const apiService = new ApiService(); +export default apiService; \ No newline at end of file diff --git a/frontend/src/services/websocket.ts b/frontend/src/services/websocket.ts new file mode 100644 index 000000000..4b2e3d75a --- /dev/null +++ b/frontend/src/services/websocket.ts @@ -0,0 +1,108 @@ +import { config } from '../config'; +import { MCPUsage, Message as MessageType } from '../types/api'; + +export type WebSocketMessage = { + type: 'message' | 'mcp_usage'; + data: MessageType | MCPUsage; +}; + +export type WebSocketEventHandler = (message: WebSocketMessage) => void; + +export class WebSocketService { + private ws: WebSocket | null = null; + private handlers: Map = new Map(); + private reconnectAttempts = 0; + private maxReconnectAttempts = 5; + private reconnectDelay = 1000; + + connect(sessionId: string): void { + if (this.ws && this.ws.readyState === WebSocket.OPEN) { + return; + } + + const wsUrl = `${config.apiUrl.replace('http', 'ws')}/ws/${sessionId}`; + console.log('Connecting to WebSocket:', wsUrl); + + this.ws = new WebSocket(wsUrl); + + this.ws.onopen = (event) => { + console.log('WebSocket connected:', event); + this.reconnectAttempts = 0; + }; + + this.ws.onmessage = (event) => { + try { + const message: WebSocketMessage = JSON.parse(event.data); + this.notifyHandlers(sessionId, message); + } catch (error) { + console.error('Error parsing WebSocket message:', error); + } + }; + + this.ws.onclose = (event) => { + console.log('WebSocket disconnected:', event); + this.ws = null; + + // Attempt to reconnect if not a normal closure + if (event.code !== 1000 && this.reconnectAttempts < this.maxReconnectAttempts) { + setTimeout(() => { + this.reconnectAttempts++; + console.log(`Reconnecting... (${this.reconnectAttempts}/${this.maxReconnectAttempts})`); + this.connect(sessionId); + }, this.reconnectDelay * this.reconnectAttempts); + } + }; + + this.ws.onerror = (error) => { + console.error('WebSocket error:', error); + }; + } + + disconnect(): void { + if (this.ws) { + this.ws.close(1000, 'Client disconnecting'); + this.ws = null; + } + } + + subscribe(sessionId: string, handler: WebSocketEventHandler): () => void { + if (!this.handlers.has(sessionId)) { + this.handlers.set(sessionId, []); + } + this.handlers.get(sessionId)!.push(handler); + + // Return unsubscribe function + return () => { + const sessionHandlers = this.handlers.get(sessionId); + if (sessionHandlers) { + const index = sessionHandlers.indexOf(handler); + if (index > -1) { + sessionHandlers.splice(index, 1); + } + if (sessionHandlers.length === 0) { + this.handlers.delete(sessionId); + } + } + }; + } + + private notifyHandlers(sessionId: string, message: WebSocketMessage): void { + const sessionHandlers = this.handlers.get(sessionId); + if (sessionHandlers) { + sessionHandlers.forEach(handler => { + try { + handler(message); + } catch (error) { + console.error('Error in WebSocket handler:', error); + } + }); + } + } + + isConnected(): boolean { + return this.ws !== null && this.ws.readyState === WebSocket.OPEN; + } +} + +// Export singleton instance +export const webSocketService = new WebSocketService(); \ No newline at end of file diff --git a/frontend/src/types/api.ts b/frontend/src/types/api.ts new file mode 100644 index 000000000..3aac7ec68 --- /dev/null +++ b/frontend/src/types/api.ts @@ -0,0 +1,75 @@ +export type SessionStatus = 'pending' | 'running' | 'completed' | 'failed' | 'cancelled'; + +export type Stage = 'refine' | 'epics' | 'jiras' | 'estimate'; + +export type MessageStatus = 'loading' | 'success' | 'error'; + +export type Session = { + id: string; + jira_key: string; + soft_mode: boolean; + status: SessionStatus; + current_stage: Stage | null; + error_message: string | null; + started_at: string; + completed_at: string | null; + created_at: string; + updated_at: string; +}; + +export type Message = { + id: string; + session_id: string; + stage: Stage; + role: 'user' | 'assistant'; + content: string; + status: MessageStatus; + timestamp: string; +}; + +export type Output = { + id: string; + session_id: string; + stage: Stage; + filename: string; + content: string; + created_at: string; +}; + +export type MCPUsage = { + id: string; + session_id: string; + stage: Stage; + tool_name: string; + input_data: unknown; + output_data: unknown; + timestamp: string; +}; + +export type SessionDetail = Session & { + messages: Message[]; + outputs: Output[]; + mcp_usages: MCPUsage[]; +}; + +export type ProgressResponse = { + session_id: string; + status: SessionStatus; + current_stage: Stage | null; + progress_percentage: number; + latest_message: string | null; + error_message: string | null; + started_at: string; +}; + +export type SessionListResponse = { + sessions: Session[]; + total: number; + page: number; + page_size: number; +}; + +export type CreateSessionRequest = { + jira_key: string; + soft_mode: boolean; +}; \ No newline at end of file diff --git a/frontend/src/typings.d.ts b/frontend/src/typings.d.ts new file mode 100644 index 000000000..16df0f513 --- /dev/null +++ b/frontend/src/typings.d.ts @@ -0,0 +1,12 @@ +declare module '*.png'; +declare module '*.jpg'; +declare module '*.jpeg'; +declare module '*.gif'; +declare module '*.svg'; +declare module '*.css'; +declare module '*.wav'; +declare module '*.mp3'; +declare module '*.m4a'; +declare module '*.rdf'; +declare module '*.ttl'; +declare module '*.pdf'; diff --git a/frontend/stylePaths.js b/frontend/stylePaths.js new file mode 100644 index 000000000..2ca7405a7 --- /dev/null +++ b/frontend/stylePaths.js @@ -0,0 +1,14 @@ +const path = require('path'); +module.exports = { + stylePaths: [ + path.resolve(__dirname, 'src'), + path.resolve(__dirname, 'node_modules/patternfly'), + path.resolve(__dirname, 'node_modules/@patternfly/patternfly'), + path.resolve(__dirname, 'node_modules/@patternfly/react-styles/css'), + path.resolve(__dirname, 'node_modules/@patternfly/react-core/dist/styles/base.css'), + path.resolve(__dirname, 'node_modules/@patternfly/react-core/dist/esm/@patternfly/patternfly'), + path.resolve(__dirname, 'node_modules/@patternfly/react-core/node_modules/@patternfly/react-styles/css'), + path.resolve(__dirname, 'node_modules/@patternfly/react-table/node_modules/@patternfly/react-styles/css'), + path.resolve(__dirname, 'node_modules/@patternfly/react-inline-edit-extension/node_modules/@patternfly/react-styles/css') + ] +} diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json new file mode 100644 index 000000000..9c2485fa5 --- /dev/null +++ b/frontend/tsconfig.json @@ -0,0 +1,34 @@ +{ + "compilerOptions": { + "baseUrl": ".", + "rootDir": ".", + "outDir": "dist", + "module": "esnext", + "target": "es5", + "lib": ["es6", "dom"], + "sourceMap": true, + "jsx": "react", + "moduleResolution": "node", + "forceConsistentCasingInFileNames": true, + "noImplicitReturns": true, + "noImplicitThis": true, + "noImplicitAny": false, + "allowJs": true, + "esModuleInterop": true, + "allowSyntheticDefaultImports": true, + "strict": true, + "paths": { + "@app/*": ["src/app/*"], + "@assets/*": ["node_modules/@patternfly/react-core/dist/styles/assets/*"] + }, + "importHelpers": true, + "skipLibCheck": true + }, + "include": [ + "**/*.ts", + "**/*.tsx", + "**/*.jsx", + "**/*.js" + ], + "exclude": ["node_modules"] +} diff --git a/frontend/webpack.common.js b/frontend/webpack.common.js new file mode 100644 index 000000000..0a5e73f31 --- /dev/null +++ b/frontend/webpack.common.js @@ -0,0 +1,136 @@ +/* eslint-disable @typescript-eslint/no-var-requires */ + +const path = require('path'); +const HtmlWebpackPlugin = require('html-webpack-plugin'); +const CopyPlugin = require('copy-webpack-plugin'); +const TsconfigPathsPlugin = require('tsconfig-paths-webpack-plugin'); +const Dotenv = require('dotenv-webpack'); +const BG_IMAGES_DIRNAME = 'bgimages'; +const ASSET_PATH = process.env.ASSET_PATH || '/'; +module.exports = (env) => { + return { + module: { + rules: [ + { + test: /\.(tsx|ts|jsx)?$/, + use: [ + { + loader: 'ts-loader', + options: { + transpileOnly: true, + experimentalWatchApi: true, + }, + }, + ], + }, + { + test: /\.(svg|ttf|eot|woff|woff2)$/, + type: 'asset/resource', + // only process modules with this loader + // if they live under a 'fonts' or 'pficon' directory + include: [ + path.resolve(__dirname, 'node_modules/patternfly/dist/fonts'), + path.resolve(__dirname, 'node_modules/@patternfly/react-core/dist/styles/assets/fonts'), + path.resolve(__dirname, 'node_modules/@patternfly/react-core/dist/styles/assets/pficon'), + path.resolve(__dirname, 'node_modules/@patternfly/patternfly/assets/fonts'), + path.resolve(__dirname, 'node_modules/@patternfly/patternfly/assets/pficon'), + ], + }, + { + test: /\.svg$/, + type: 'asset/inline', + include: (input) => input.indexOf('background-filter.svg') > 1, + use: [ + { + options: { + limit: 5000, + outputPath: 'svgs', + name: '[name].[ext]', + }, + }, + ], + }, + { + test: /\.svg$/, + // only process SVG modules with this loader if they live under a 'bgimages' directory + // this is primarily useful when applying a CSS background using an SVG + include: (input) => input.indexOf(BG_IMAGES_DIRNAME) > -1, + type: 'asset/inline', + }, + { + test: /\.svg$/, + // only process SVG modules with this loader when they don't live under a 'bgimages', + // 'fonts', or 'pficon' directory, those are handled with other loaders + include: (input) => + input.indexOf(BG_IMAGES_DIRNAME) === -1 && + input.indexOf('fonts') === -1 && + input.indexOf('background-filter') === -1 && + input.indexOf('pficon') === -1, + use: { + loader: 'raw-loader', + options: {}, + }, + }, + { + test: /\.(jpg|jpeg|png|gif)$/i, + include: [ + path.resolve(__dirname, 'src'), + path.resolve(__dirname, 'node_modules/patternfly'), + path.resolve(__dirname, 'node_modules/@patternfly/patternfly/assets/images'), + path.resolve(__dirname, 'node_modules/@patternfly/react-styles/css/assets/images'), + path.resolve(__dirname, 'node_modules/@patternfly/react-core/dist/styles/assets/images'), + path.resolve( + __dirname, + 'node_modules/@patternfly/react-core/node_modules/@patternfly/react-styles/css/assets/images' + ), + path.resolve( + __dirname, + 'node_modules/@patternfly/react-table/node_modules/@patternfly/react-styles/css/assets/images' + ), + path.resolve( + __dirname, + 'node_modules/@patternfly/react-inline-edit-extension/node_modules/@patternfly/react-styles/css/assets/images' + ), + ], + type: 'asset/inline', + use: [ + { + options: { + limit: 5000, + outputPath: 'images', + name: '[name].[ext]', + }, + }, + ], + }, + ], + }, + output: { + filename: '[name].bundle.js', + path: path.resolve(__dirname, 'dist'), + publicPath: ASSET_PATH, + }, + plugins: [ + new HtmlWebpackPlugin({ + template: path.resolve(__dirname, 'src', 'index.html'), + }), + new Dotenv({ + systemvars: true, + silent: true, + }), + new CopyPlugin({ + patterns: [{ from: './src/favicon.png', to: 'images' }], + }), + ], + resolve: { + extensions: ['.js', '.ts', '.tsx', '.jsx'], + plugins: [ + new TsconfigPathsPlugin({ + configFile: path.resolve(__dirname, './tsconfig.json'), + }), + ], + symlinks: false, + cacheWithContext: false, + }, + }; +}; diff --git a/frontend/webpack.dev.js b/frontend/webpack.dev.js new file mode 100644 index 000000000..f959390b1 --- /dev/null +++ b/frontend/webpack.dev.js @@ -0,0 +1,34 @@ +/* eslint-disable @typescript-eslint/no-var-requires */ + +const path = require('path'); +const { merge } = require('webpack-merge'); +const common = require('./webpack.common.js'); +const { stylePaths } = require('./stylePaths'); +const HOST = process.env.HOST || 'localhost'; +const PORT = process.env.PORT || '9000'; + +module.exports = merge(common('development'), { + mode: 'development', + devtool: 'eval-source-map', + devServer: { + host: HOST, + port: PORT, + historyApiFallback: true, + open: true, + static: { + directory: path.resolve(__dirname, 'dist'), + }, + client: { + overlay: true, + }, + }, + module: { + rules: [ + { + test: /\.css$/, + include: [...stylePaths], + use: ['style-loader', 'css-loader'], + }, + ], + }, +}); diff --git a/frontend/webpack.prod.js b/frontend/webpack.prod.js new file mode 100644 index 000000000..eb6f453dd --- /dev/null +++ b/frontend/webpack.prod.js @@ -0,0 +1,38 @@ +/* eslint-disable @typescript-eslint/no-var-requires */ + +const { merge } = require('webpack-merge'); +const common = require('./webpack.common.js'); +const { stylePaths } = require('./stylePaths'); +const MiniCssExtractPlugin = require('mini-css-extract-plugin'); +const CssMinimizerPlugin = require('css-minimizer-webpack-plugin'); +const TerserJSPlugin = require('terser-webpack-plugin'); + +module.exports = merge(common('production'), { + mode: 'production', + devtool: 'source-map', + optimization: { + minimizer: [ + new TerserJSPlugin({}), + new CssMinimizerPlugin({ + minimizerOptions: { + preset: ['default', { mergeLonghand: false }], + }, + }), + ], + }, + plugins: [ + new MiniCssExtractPlugin({ + filename: '[name].css', + chunkFilename: '[name].bundle.css', + }), + ], + module: { + rules: [ + { + test: /\.css$/, + include: [...stylePaths], + use: [MiniCssExtractPlugin.loader, 'css-loader'], + }, + ], + }, +}); diff --git a/openshift-deployment.yaml b/openshift-deployment.yaml index 7975df61c..6aa3b53e3 100644 --- a/openshift-deployment.yaml +++ b/openshift-deployment.yaml @@ -42,14 +42,12 @@ data: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing}/responses_store.db vector_io: - - provider_id: milvus - provider_type: inline::milvus + - provider_id: faiss + provider_type: inline::faiss config: - db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing/milvus.db} kvstore: type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing}/${env.MILVUS_KVSTORE_DB_PATH:=~/.llama/distributions/llamastack-rhoai-ai-feature-sizing/milvus_registry.db} + db_path: ${env.SQLITE_STORE_DIR:=/tmp}/vector_kvstore.db tool_runtime: - provider_id: model-context-protocol provider_type: remote::model-context-protocol @@ -84,9 +82,9 @@ data: benchmarks: [] tool_groups: - toolgroup_id: mcp::atlassian - provider_id: model-context-protocol - mcp_endpoint: - uri: ${env.MCP_ATLASSIAN_URL} + provider_id: model-context-protocol + mcp_endpoint: + uri: ${env.MCP_ATLASSIAN_URL} logging: null server: port: 8321 @@ -115,6 +113,7 @@ spec: containers: - name: llama-stack image: llamastack/distribution-starter:latest + command: ["python", "-m", "llama_stack.distribution.server.server", "--config", "/app/run.yaml"] ports: - containerPort: 8321 env: @@ -136,10 +135,8 @@ spec: key: VLLM_INFERENCE_MODEL - name: SQLITE_STORE_DIR value: "/tmp" - - name: MILVUS_DB_PATH - value: "/tmp/milvus.db" - - name: MILVUS_KVSTORE_DB_PATH - value: "/tmp/milvus_registry.db" + - name: VECTOR_STORAGE_DIR + value: "/tmp/vector_storage" - name: OTEL_SERVICE_NAME value: "llama-stack" - name: TELEMETRY_SINKS diff --git a/pyproject.toml b/pyproject.toml index 3c0cf2bf3..97135a790 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,7 @@ dependencies = [ "llama-stack-client>=0.2.10", "requests>=2.32.3", "fastapi>=0.104.0", - "uvicorn>=0.24.0", + "uvicorn[standard]>=0.24.0", "python-multipart>=0.0.6", "sqlalchemy>=2.0.0", "psycopg2-binary>=2.9.0", diff --git a/src/rhoai_ai_feature_sizing/api/main.py b/src/rhoai_ai_feature_sizing/api/main.py index 2102faf37..3349fa936 100644 --- a/src/rhoai_ai_feature_sizing/api/main.py +++ b/src/rhoai_ai_feature_sizing/api/main.py @@ -4,9 +4,17 @@ import uuid import asyncio from datetime import datetime -from typing import List, Optional - -from fastapi import FastAPI, HTTPException, BackgroundTasks, Query, Depends +from typing import List, Optional, Dict + +from fastapi import ( + FastAPI, + HTTPException, + BackgroundTasks, + Query, + Depends, + WebSocket, + WebSocketDisconnect, +) from fastapi.middleware.cors import CORSMiddleware from contextlib import asynccontextmanager @@ -30,6 +38,45 @@ session_service = None +class WebSocketManager: + """Manager for WebSocket connections.""" + + def __init__(self): + self.active_connections: Dict[uuid.UUID, List[WebSocket]] = {} + + async def connect(self, websocket: WebSocket, session_id: uuid.UUID): + """Connect a WebSocket for a specific session.""" + await websocket.accept() + if session_id not in self.active_connections: + self.active_connections[session_id] = [] + self.active_connections[session_id].append(websocket) + + def disconnect(self, websocket: WebSocket, session_id: uuid.UUID): + """Disconnect a WebSocket from a session.""" + if session_id in self.active_connections: + self.active_connections[session_id].remove(websocket) + if not self.active_connections[session_id]: + del self.active_connections[session_id] + + async def send_message(self, session_id: uuid.UUID, message: dict): + """Send a message to all connected clients for a session.""" + if session_id in self.active_connections: + disconnected = [] + for websocket in self.active_connections[session_id]: + try: + await websocket.send_json(message) + except Exception: + disconnected.append(websocket) + + # Remove disconnected clients + for websocket in disconnected: + self.disconnect(websocket, session_id) + + +# Global WebSocket manager +ws_manager = WebSocketManager() + + @asynccontextmanager async def lifespan(app: FastAPI): """Application lifespan manager.""" @@ -42,6 +89,9 @@ async def lifespan(app: FastAPI): logging.info("Creating session service...") session_service = SessionService() + # Set WebSocket manager on session service + session_service.set_websocket_manager(ws_manager) + logging.info("API startup complete") yield @@ -100,7 +150,6 @@ async def health_check(): @app.post("/sessions", response_model=SessionResponse) async def create_session( request: CreateSessionRequest, - background_tasks: BackgroundTasks, service: SessionService = Depends(get_session_service), ): """Create a new processing session and start processing.""" @@ -108,8 +157,9 @@ async def create_session( # Create session session_id = service.create_session(request.jira_key, request.soft_mode) - # Start processing in background - background_tasks.add_task(service.process_session, session_id) + # Start processing in background using asyncio.create_task + # This ensures proper async handling without blocking the event loop + asyncio.create_task(service.process_session(session_id)) # Return session info session = service.get_session(session_id) @@ -130,7 +180,7 @@ async def list_sessions( try: sessions, total = service.get_sessions(page, page_size) return SessionListResponse( - sessions=[SessionResponse.model_validate(s) for s in sessions], + sessions=sessions, total=total, page=page, page_size=page_size, @@ -156,10 +206,10 @@ async def get_session( mcp_usages = service.get_session_mcp_usage(session_id) return SessionDetailResponse( - **SessionResponse.model_validate(session).model_dump(), - messages=[MessageResponse.model_validate(m) for m in messages], - outputs=[OutputResponse.model_validate(o) for o in outputs], - mcp_usages=[MCPUsageResponse.model_validate(u) for u in mcp_usages], + **session.model_dump(), + messages=messages, + outputs=outputs, + mcp_usages=mcp_usages, ) except HTTPException: raise @@ -232,7 +282,7 @@ async def get_session_messages( if stage: messages = [m for m in messages if m.stage == stage] - return [MessageResponse.model_validate(m) for m in messages] + return messages except HTTPException: raise except Exception as e: @@ -258,7 +308,7 @@ async def get_session_outputs( if stage: outputs = [o for o in outputs if o.stage == stage] - return [OutputResponse.model_validate(o) for o in outputs] + return outputs except HTTPException: raise except Exception as e: @@ -286,7 +336,7 @@ async def get_session_output_by_stage( status_code=404, detail=f"No output found for stage {stage}" ) - return OutputResponse.model_validate(stage_output) + return stage_output except HTTPException: raise except Exception as e: @@ -307,7 +357,7 @@ async def get_session_mcp_usage( raise HTTPException(status_code=404, detail="Session not found") usages = service.get_session_mcp_usage(session_id) - return [MCPUsageResponse.model_validate(u) for u in usages] + return usages except HTTPException: raise except Exception as e: @@ -321,15 +371,13 @@ async def delete_session( ): """Delete a session and all related data.""" try: - session = service.get_session(session_id) - if not session: - raise HTTPException(status_code=404, detail="Session not found") + # Attempt to delete the session + deleted = service.delete_session(session_id) - # TODO: Implement session deletion - # For now, just update status to cancelled - service.update_session_status(session_id, SessionStatus.CANCELLED) + if not deleted: + raise HTTPException(status_code=404, detail="Session not found") - return {"message": "Session cancelled"} + return {"message": "Session deleted successfully"} except HTTPException: raise except Exception as e: @@ -337,6 +385,18 @@ async def delete_session( raise HTTPException(status_code=500, detail=str(e)) +@app.websocket("/ws/{session_id}") +async def websocket_endpoint(websocket: WebSocket, session_id: uuid.UUID): + """WebSocket endpoint for real-time session updates.""" + await ws_manager.connect(websocket, session_id) + try: + while True: + # Keep connection alive - we only send, don't receive + await websocket.receive_text() + except WebSocketDisconnect: + ws_manager.disconnect(websocket, session_id) + + if __name__ == "__main__": import uvicorn import os diff --git a/src/rhoai_ai_feature_sizing/api/models.py b/src/rhoai_ai_feature_sizing/api/models.py index 0c8c8ae43..258461cad 100644 --- a/src/rhoai_ai_feature_sizing/api/models.py +++ b/src/rhoai_ai_feature_sizing/api/models.py @@ -88,6 +88,14 @@ class MessageRole(str, Enum): AGENT = "agent" +class MessageStatus(str, Enum): + """Status of a message.""" + + LOADING = "loading" + SUCCESS = "success" + ERROR = "error" + + class Session(Base): """Main session tracking table.""" @@ -135,6 +143,9 @@ class Message(Base): role = Column(SQLEnum(MessageRole), nullable=False) content = Column(Text, nullable=False) stage = Column(SQLEnum(Stage), nullable=True) + status = Column( + SQLEnum(MessageStatus), default=MessageStatus.SUCCESS, nullable=False + ) timestamp = Column(DateTime, default=datetime.utcnow, nullable=False) message_metadata = Column(Text, nullable=True) # JSON string for additional data @@ -142,7 +153,7 @@ class Message(Base): session = relationship("Session", back_populates="messages") def __repr__(self): - return f"" + return f"" class Output(Base): @@ -171,6 +182,7 @@ class MCPUsage(Base): id = Column(GUID(), primary_key=True, default=uuid.uuid4) session_id = Column(GUID(), ForeignKey("sessions.id"), nullable=False, index=True) + stage = Column(SQLEnum(Stage, name="stage"), nullable=False) tool_name = Column(String(100), nullable=False) request_data = Column(Text, nullable=True) # JSON string response_data = Column(Text, nullable=True) # JSON string diff --git a/src/rhoai_ai_feature_sizing/api/schemas.py b/src/rhoai_ai_feature_sizing/api/schemas.py index a4db298bd..766ce472f 100644 --- a/src/rhoai_ai_feature_sizing/api/schemas.py +++ b/src/rhoai_ai_feature_sizing/api/schemas.py @@ -5,7 +5,7 @@ from typing import List, Optional, Dict, Any from pydantic import BaseModel, Field -from .models import SessionStatus, Stage, MessageRole +from .models import SessionStatus, Stage, MessageRole, MessageStatus # Request schemas @@ -27,6 +27,7 @@ class MessageResponse(BaseModel): role: MessageRole content: str stage: Optional[Stage] + status: MessageStatus timestamp: datetime message_metadata: Optional[Dict[str, Any]] = None @@ -51,12 +52,47 @@ class MCPUsageResponse(BaseModel): """MCP usage tracking response.""" id: uuid.UUID + stage: Stage tool_name: str + input_data: Optional[dict] = None + output_data: Optional[dict] = None timestamp: datetime duration_ms: Optional[int] success: bool error_message: Optional[str] + @classmethod + def from_model(cls, mcp_usage) -> "MCPUsageResponse": + """Create response from model, converting JSON strings to dicts.""" + import json + + input_data = None + output_data = None + + if mcp_usage.request_data: + try: + input_data = json.loads(mcp_usage.request_data) + except json.JSONDecodeError: + input_data = {"raw": mcp_usage.request_data} + + if mcp_usage.response_data: + try: + output_data = json.loads(mcp_usage.response_data) + except json.JSONDecodeError: + output_data = {"raw": mcp_usage.response_data} + + return cls( + id=mcp_usage.id, + stage=mcp_usage.stage, + tool_name=mcp_usage.tool_name, + input_data=input_data, + output_data=output_data, + timestamp=mcp_usage.timestamp, + duration_ms=mcp_usage.duration_ms, + success=mcp_usage.success, + error_message=mcp_usage.error_message, + ) + class Config: from_attributes = True diff --git a/src/rhoai_ai_feature_sizing/api/services.py b/src/rhoai_ai_feature_sizing/api/services.py index 8e5e34483..1edd46e6f 100644 --- a/src/rhoai_ai_feature_sizing/api/services.py +++ b/src/rhoai_ai_feature_sizing/api/services.py @@ -4,12 +4,14 @@ import logging import time import uuid +import asyncio from datetime import datetime from pathlib import Path from typing import Optional, Dict, Any, List from contextlib import contextmanager import tempfile import os +from concurrent.futures import ThreadPoolExecutor from sqlalchemy.orm import Session as DBSession @@ -21,19 +23,57 @@ SessionStatus, Stage, MessageRole, + MessageStatus, create_session_factory, ) -from .schemas import StageProgressUpdate +from .schemas import ( + StageProgressUpdate, + SessionResponse, + MessageResponse, + OutputResponse, + MCPUsageResponse, +) from ..stages.refine_feature import generate_refinement_with_agent from ..stages.draft_jiras import draft_jiras_from_file +class SessionLogHandler(logging.Handler): + """Custom logging handler to capture log messages for sessions.""" + + def __init__(self, session_id: uuid.UUID, stage: Stage, service: "SessionService"): + super().__init__() + self.session_id = session_id + self.stage = stage + self.service = service + + def emit(self, record): + """Emit a log record as a session message.""" + try: + message = self.format(record) + # Skip debug messages and internal logging + if record.levelno >= logging.INFO and not record.name.startswith("uvicorn"): + self.service.add_message( + self.session_id, MessageRole.SYSTEM, message, self.stage + ) + except Exception: + # Don't let logging errors crash the application + pass + + class SessionService: """Service for managing processing sessions.""" def __init__(self): self.session_factory = create_session_factory() self.logger = logging.getLogger("SessionService") + # Thread pool for running blocking I/O operations + self.executor = ThreadPoolExecutor(max_workers=4) + # WebSocket manager for real-time updates + self.ws_manager = None + + def set_websocket_manager(self, ws_manager): + """Set the WebSocket manager for real-time updates.""" + self.ws_manager = ws_manager @contextmanager def get_db_session(self): @@ -48,159 +88,68 @@ def get_db_session(self): finally: db_session.close() - def create_session(self, jira_key: str, soft_mode: bool = True) -> uuid.UUID: + def create_session(self, jira_key: str, soft_mode: bool = False) -> uuid.UUID: """Create a new processing session.""" + session_id = uuid.uuid4() + with self.get_db_session() as db_session: session = Session( - jira_key=jira_key, soft_mode=soft_mode, status=SessionStatus.PENDING + id=session_id, + jira_key=jira_key, + soft_mode=soft_mode, + status=SessionStatus.PENDING, + started_at=datetime.now(), + created_at=datetime.now(), + updated_at=datetime.now(), ) db_session.add(session) - db_session.flush() - - # Add initial message - initial_message = Message( - session_id=session.id, - role=MessageRole.SYSTEM, - content=f"Session created for Jira issue {jira_key} ({'soft mode' if soft_mode else 'hard mode'})", - stage=None, - ) - db_session.add(initial_message) - self.logger.info(f"Created session {session.id} for {jira_key}") - return session.id + self.logger.info(f"Created session {session_id} for JIRA key {jira_key}") + return session_id - def get_session(self, session_id: uuid.UUID) -> Optional[Session]: - """Get session by ID.""" + def get_session(self, session_id: uuid.UUID) -> Optional[SessionResponse]: + """Get a session by ID.""" with self.get_db_session() as db_session: session = db_session.query(Session).filter(Session.id == session_id).first() - if session: - # Load all attributes while session is active to avoid DetachedInstanceError - _ = ( - session.id, - session.jira_key, - session.status, - session.current_stage, - session.soft_mode, - session.created_at, - session.updated_at, - session.started_at, - session.completed_at, - session.error_message, - ) - db_session.expunge(session) - return session + return SessionResponse.model_validate(session) if session else None def get_sessions( self, page: int = 1, page_size: int = 20 - ) -> tuple[List[Session], int]: + ) -> tuple[List[SessionResponse], int]: """Get paginated list of sessions.""" with self.get_db_session() as db_session: - offset = (page - 1) * page_size - sessions = ( - db_session.query(Session) - .order_by(Session.created_at.desc()) - .offset(offset) - .limit(page_size) - .all() - ) - total = db_session.query(Session).count() - - # Load all attributes for each session to avoid DetachedInstanceError - for session in sessions: - _ = ( - session.id, - session.jira_key, - session.status, - session.current_stage, - session.soft_mode, - session.created_at, - session.updated_at, - session.started_at, - session.completed_at, - session.error_message, - ) - db_session.expunge(session) - - return sessions, total - - def get_session_messages( - self, session_id: uuid.UUID, limit: int = 50 - ) -> List[Message]: - """Get messages for a session.""" - with self.get_db_session() as db_session: - messages = ( - db_session.query(Message) - .filter(Message.session_id == session_id) - .order_by(Message.timestamp.asc()) - .limit(limit) - .all() - ) + query = db_session.query(Session).order_by(Session.created_at.desc()) - # Load all attributes for each message to avoid DetachedInstanceError - for message in messages: - _ = ( - message.id, - message.session_id, - message.role, - message.content, - message.stage, - message.timestamp, - message.message_metadata, - ) - db_session.expunge(message) + # Get total count + total = query.count() - return messages + # Apply pagination + sessions = query.offset((page - 1) * page_size).limit(page_size).all() - def get_session_outputs(self, session_id: uuid.UUID) -> List[Output]: - """Get outputs for a session.""" - with self.get_db_session() as db_session: - outputs = ( - db_session.query(Output) - .filter(Output.session_id == session_id) - .order_by(Output.created_at.asc()) - .all() - ) + # Convert to Pydantic models while still in database session + session_responses = [SessionResponse.model_validate(s) for s in sessions] - # Load all attributes for each output to avoid DetachedInstanceError - for output in outputs: - _ = ( - output.id, - output.session_id, - output.stage, - output.filename, - output.content, - output.created_at, - ) - db_session.expunge(output) + return session_responses, total - return outputs - - def get_session_mcp_usage(self, session_id: uuid.UUID) -> List[MCPUsage]: - """Get MCP usage for a session.""" + def update_session_status( + self, + session_id: uuid.UUID, + status: SessionStatus, + stage: Optional[Stage] = None, + error_message: Optional[str] = None, + ): + """Update session status and current stage.""" with self.get_db_session() as db_session: - usages = ( - db_session.query(MCPUsage) - .filter(MCPUsage.session_id == session_id) - .order_by(MCPUsage.timestamp.asc()) - .all() - ) - - # Load all attributes for each usage to avoid DetachedInstanceError - for usage in usages: - _ = ( - usage.id, - usage.session_id, - usage.tool_name, - usage.request_data, - usage.response_data, - usage.timestamp, - usage.duration_ms, - usage.success, - usage.error_message, - ) - db_session.expunge(usage) - - return usages + session = db_session.query(Session).filter(Session.id == session_id).first() + if session: + session.status = status + if stage is not None: + session.current_stage = stage + if error_message is not None: + session.error_message = error_message + if status == SessionStatus.COMPLETED: + session.completed_at = datetime.now() + session.updated_at = datetime.now() def add_message( self, @@ -208,74 +157,233 @@ def add_message( role: MessageRole, content: str, stage: Optional[Stage] = None, - message_metadata: Optional[Dict[str, Any]] = None, - ): + status: MessageStatus = MessageStatus.SUCCESS, + ) -> uuid.UUID: """Add a message to the session.""" + message_data = None + message_id = uuid.uuid4() + with self.get_db_session() as db_session: message = Message( + id=message_id, session_id=session_id, role=role, content=content, stage=stage, - message_metadata=( - json.dumps(message_metadata) if message_metadata else None - ), + status=status, + timestamp=datetime.now(), ) db_session.add(message) + # Prepare WebSocket message data while message is still attached to session + if self.ws_manager: + message_data = { + "type": "message", + "data": { + "id": str(message.id), + "session_id": str(session_id), + "role": role.value, + "content": content, + "stage": stage.value if stage else None, + "status": status.value, + "timestamp": message.timestamp.isoformat(), + }, + } + + self.logger.info( + f"Added {role} message to session {session_id}: {content[:100]}... (status: {status})" + ) + + # Emit message via WebSocket if available + if message_data: + try: + # Schedule WebSocket send (non-blocking) + asyncio.create_task( + self.ws_manager.send_message(session_id, message_data) + ) + except Exception as e: + self.logger.debug(f"Failed to send WebSocket message: {e}") + # Don't let WebSocket errors crash the application + pass + + return message_id + + def update_message_status( + self, + message_id: uuid.UUID, + status: MessageStatus, + content: Optional[str] = None, + ): + """Update the status of a message and optionally its content.""" + message_data = None + message_session_id = None + + with self.get_db_session() as db_session: + message = db_session.query(Message).filter(Message.id == message_id).first() + if message: + message.status = status + if content is not None: + message.content = content + message.timestamp = ( + datetime.now() + ) # Update timestamp for real-time updates + + # Extract session_id while we're still in the database session context + message_session_id = message.session_id + + # Prepare WebSocket message data + if self.ws_manager: + message_data = { + "type": "message", + "data": { + "id": str(message.id), + "session_id": str(message.session_id), + "role": message.role.value, + "content": message.content, + "stage": message.stage.value if message.stage else None, + "status": status.value, + "timestamp": message.timestamp.isoformat(), + }, + } + + # Emit updated message via WebSocket if available + if message_data and message_session_id: + try: + # Schedule WebSocket send (non-blocking) + asyncio.create_task( + self.ws_manager.send_message(message_session_id, message_data) + ) + except Exception as e: + self.logger.debug(f"Failed to send WebSocket message: {e}") + # Don't let WebSocket errors crash the application + pass + + def get_session_messages( + self, + session_id: uuid.UUID, + limit: Optional[int] = None, + stage: Optional[Stage] = None, + ) -> List[MessageResponse]: + """Get messages for a session.""" + with self.get_db_session() as db_session: + query = db_session.query(Message).filter(Message.session_id == session_id) + + if stage: + query = query.filter(Message.stage == stage) + + query = query.order_by(Message.timestamp.desc()) + + if limit: + query = query.limit(limit) + + messages = query.all() + return [MessageResponse.model_validate(m) for m in messages] + def add_output( self, session_id: uuid.UUID, stage: Stage, filename: str, content: str ): """Add an output file to the session.""" with self.get_db_session() as db_session: output = Output( - session_id=session_id, stage=stage, filename=filename, content=content + id=uuid.uuid4(), + session_id=session_id, + stage=stage, + filename=filename, + content=content, + created_at=datetime.now(), ) db_session.add(output) + self.logger.info( + f"Added output {filename} to session {session_id} for stage {stage}" + ) + + def get_session_outputs( + self, session_id: uuid.UUID, stage: Optional[Stage] = None + ) -> List[OutputResponse]: + """Get outputs for a session.""" + with self.get_db_session() as db_session: + query = db_session.query(Output).filter(Output.session_id == session_id) + + if stage: + query = query.filter(Output.stage == stage) + + outputs = query.order_by(Output.created_at.desc()).all() + return [OutputResponse.model_validate(o) for o in outputs] + + def get_session_mcp_usage(self, session_id: uuid.UUID) -> List[MCPUsageResponse]: + """Get MCP usage for a session.""" + with self.get_db_session() as db_session: + mcp_usages = ( + db_session.query(MCPUsage) + .filter(MCPUsage.session_id == session_id) + .order_by(MCPUsage.timestamp.desc()) + .all() + ) + return [MCPUsageResponse.from_model(u) for u in mcp_usages] + def add_mcp_usage( self, session_id: uuid.UUID, + stage: Stage, tool_name: str, - request_data: Optional[Dict] = None, - response_data: Optional[Dict] = None, + input_data: Optional[Dict[str, Any]] = None, + output_data: Optional[Dict[str, Any]] = None, duration_ms: Optional[int] = None, success: bool = True, error_message: Optional[str] = None, ): - """Add MCP usage tracking.""" + """Add MCP usage tracking entry.""" + import json + + mcp_usage_id = None + mcp_usage_timestamp = None + with self.get_db_session() as db_session: - usage = MCPUsage( + mcp_usage = MCPUsage( + id=uuid.uuid4(), session_id=session_id, + stage=stage, tool_name=tool_name, - request_data=json.dumps(request_data) if request_data else None, - response_data=json.dumps(response_data) if response_data else None, + request_data=json.dumps(input_data) if input_data else None, + response_data=json.dumps(output_data) if output_data else None, + timestamp=datetime.now(), duration_ms=duration_ms, success=success, error_message=error_message, ) - db_session.add(usage) + db_session.add(mcp_usage) - def update_session_status( - self, - session_id: uuid.UUID, - status: SessionStatus, - current_stage: Optional[Stage] = None, - error_message: Optional[str] = None, - ): - """Update session status and stage.""" - with self.get_db_session() as db_session: - session = db_session.query(Session).filter(Session.id == session_id).first() - if session: - session.status = status - if current_stage is not None: - session.current_stage = current_stage - if error_message is not None: - session.error_message = error_message - if status == SessionStatus.RUNNING and not session.started_at: - session.started_at = datetime.utcnow() - elif status in [SessionStatus.COMPLETED, SessionStatus.FAILED]: - session.completed_at = datetime.utcnow() + # Extract values while we're still in the database session context + mcp_usage_id = mcp_usage.id + mcp_usage_timestamp = mcp_usage.timestamp + + self.logger.info( + f"Added MCP usage for tool {tool_name} in session {session_id} (stage: {stage})" + ) + + # Emit MCP usage via WebSocket if available + if self.ws_manager and mcp_usage_id and mcp_usage_timestamp: + mcp_data = { + "type": "mcp_usage", + "data": { + "id": str(mcp_usage_id), + "session_id": str(session_id), + "stage": stage.value, + "tool_name": tool_name, + "input_data": input_data, + "output_data": output_data, + "timestamp": mcp_usage_timestamp.isoformat(), + "success": success, + }, + } + try: + # Schedule WebSocket send (non-blocking) + asyncio.create_task(self.ws_manager.send_message(session_id, mcp_data)) + except Exception as e: + self.logger.debug(f"Failed to send WebSocket MCP message: {e}") + # Don't let WebSocket errors crash the application + pass async def process_session(self, session_id: uuid.UUID): """Process a complete session through all stages.""" @@ -326,7 +434,8 @@ async def process_session(self, session_id: uuid.UUID): self.add_message( session_id, MessageRole.SYSTEM, - f"❌ Processing failed: {str(e)}", + f"Processing failed: {str(e)}", + status=MessageStatus.ERROR, ) except Exception as e: @@ -346,11 +455,13 @@ async def _process_refine_stage( self.logger.info(f"Starting refine stage for session {session_id}") self.update_session_status(session_id, SessionStatus.RUNNING, Stage.REFINE) - self.add_message( + # Add loading message + loading_message_id = self.add_message( session_id, MessageRole.AGENT, f"🔍 Starting feature refinement for {jira_key}...", Stage.REFINE, + MessageStatus.LOADING, ) # Load template @@ -366,21 +477,37 @@ async def _process_refine_stage( logger.addHandler(handler) try: - # Generate refinement - refinement_content = generate_refinement_with_agent(jira_key, template) + # Generate refinement using thread pool to avoid blocking + loop = asyncio.get_event_loop() + refinement_content = await loop.run_in_executor( + self.executor, + generate_refinement_with_agent, + jira_key, + template, + session_id, + ) # Save output filename = f"refined_{jira_key}.md" self.add_output(session_id, Stage.REFINE, filename, refinement_content) duration = time.time() - start_time - self.add_message( - session_id, - MessageRole.AGENT, + # Update loading message to success + self.update_message_status( + loading_message_id, + MessageStatus.SUCCESS, f"✅ Feature refinement completed in {duration:.2f}s. Generated {len(refinement_content)} characters.", - Stage.REFINE, ) + except Exception as e: + # Update loading message to error + self.update_message_status( + loading_message_id, + MessageStatus.ERROR, + f"❌ Feature refinement failed: {str(e)}", + ) + raise # Re-raise to maintain existing error handling + finally: logger.removeHandler(handler) @@ -404,11 +531,13 @@ async def _process_jiras_stage( self.update_session_status(session_id, SessionStatus.RUNNING, Stage.JIRAS) mode_text = "soft mode" if soft_mode else "hard mode" - self.add_message( + # Add loading message + loading_message_id = self.add_message( session_id, MessageRole.AGENT, f"🎫 Starting Jira ticket drafting ({mode_text})...", Stage.JIRAS, + MessageStatus.LOADING, ) # Get the refined content from database @@ -431,21 +560,33 @@ async def _process_jiras_stage( logger.addHandler(handler) try: - # Generate jiras - jira_content = draft_jiras_from_file(temp_file, soft_mode=soft_mode) + # Generate jiras using thread pool to avoid blocking + loop = asyncio.get_event_loop() + jira_content = await loop.run_in_executor( + self.executor, draft_jiras_from_file, temp_file, soft_mode + ) # Save output filename = f"jiras_{refined_output.filename.replace('refined_', '')}" self.add_output(session_id, Stage.JIRAS, filename, jira_content) duration = time.time() - start_time - self.add_message( - session_id, - MessageRole.AGENT, + # Update loading message to success + self.update_message_status( + loading_message_id, + MessageStatus.SUCCESS, f"✅ Jira ticket drafting completed in {duration:.2f}s ({mode_text}). Generated {len(jira_content)} characters.", - Stage.JIRAS, ) + except Exception as e: + # Update loading message to error + self.update_message_status( + loading_message_id, + MessageStatus.ERROR, + f"❌ Jira ticket drafting failed: {str(e)}", + ) + raise # Re-raise to maintain existing error handling + finally: logger.removeHandler(handler) @@ -457,44 +598,36 @@ async def _process_estimate_stage(self, session_id: uuid.UUID, temp_path: Path): self.add_message( session_id, MessageRole.AGENT, - "📊 Estimation stage is not yet implemented. Processing complete!", + "📊 Estimation stage is not yet implemented. Completing session...", Stage.ESTIMATE, ) + def delete_session(self, session_id: uuid.UUID) -> bool: + """Delete a session and all related data. -class SessionLogHandler(logging.Handler): - """Custom logging handler that captures log messages as session messages.""" + Args: + session_id: The UUID of the session to delete - def __init__(self, session_id: uuid.UUID, stage: Stage, service: SessionService): - super().__init__() - self.session_id = session_id - self.stage = stage - self.service = service - - def emit(self, record: logging.LogRecord): - """Emit a log record as a session message.""" + Returns: + bool: True if session was deleted, False if not found + """ try: - message = self.format(record) + with self.get_db_session() as db_session: + # Get the session + session = ( + db_session.query(Session).filter(Session.id == session_id).first() + ) - # Filter out some noisy logs - if any(skip in message.lower() for skip in ["debug", "httpx", "urllib3"]): - return + if not session: + return False - # Map log levels to message roles - if record.levelno >= logging.ERROR: - role = MessageRole.SYSTEM - message = f"❌ ERROR: {message}" - elif record.levelno >= logging.WARNING: - role = MessageRole.SYSTEM - message = f"⚠️ WARNING: {message}" - elif record.levelno >= logging.INFO: - role = MessageRole.AGENT - if "✅" not in message and "📋" not in message and "🔍" not in message: - message = f"ℹ️ {message}" - else: - return # Skip debug messages - - self.service.add_message(self.session_id, role, message, self.stage) - except Exception: - # Don't let logging errors break the main process - pass + # Delete the session (cascade will handle related data) + db_session.delete(session) + db_session.commit() + + self.logger.info(f"Successfully deleted session {session_id}") + return True + + except Exception as e: + self.logger.error(f"Error deleting session {session_id}: {e}") + raise diff --git a/src/rhoai_ai_feature_sizing/prompts/refine_feature.md b/src/rhoai_ai_feature_sizing/prompts/refine_feature.md index a5ca97645..894005b16 100644 --- a/src/rhoai_ai_feature_sizing/prompts/refine_feature.md +++ b/src/rhoai_ai_feature_sizing/prompts/refine_feature.md @@ -3,18 +3,17 @@ ## Objective Fetch Jira issue {{issue_key}} and create a comprehensive RHOAI feature refinement document following the provided template and examples. -## Step-by-Step Process -1. **Fetch Complete Issue Details**: Use the Jira tools to get comprehensive information about {{issue_key}}: - - Summary and description - - Components and labels - - Priority and status - - Reporter and assignee information - - Any linked issues or epics - - Comments and attachments (if relevant) +## Instructions -2. **Analyze and Transform**: Convert the Jira content into a structured refinement document +You will be provided with real Jira issue data for `{{issue_key}}`. Use this data to create a comprehensive refinement document following the template structure below. -3. **Generate Complete Document**: Create the full refinement document using the template structure below +**IMPORTANT:** +- Do not make up or assume any information about the issue. +- Use only the provided Jira data to fill in the template. +- If information is missing from the Jira data, mark as "TBD - requires stakeholder input". +- Ensure all sections are meaningful and actionable based on the real Jira information. + +Create a comprehensive refinement document following the template structure below. ## Examples @@ -250,11 +249,11 @@ Now create a refinement document for {{issue_key}} following this exact structur ## Output Requirements -- Return ONLY the complete markdown refinement document -- Replace ALL placeholder text with specific information from the Jira issue -- Use the examples as a quality standard -- If information is missing from Jira, mark as "TBD - requires stakeholder input" -- Ensure all sections are meaningful and actionable -- Follow the exact markdown formatting shown in examples +1. Return ONLY the complete markdown refinement document based on the provided Jira data +2. Replace ALL placeholder text with specific information from the actual Jira issue +3. Use the examples as a quality standard +4. If information is missing from Jira, mark as "TBD - requires stakeholder input" +5. Ensure all sections are meaningful and actionable +6. Follow the exact markdown formatting shown in examples -Begin by fetching the Jira issue details for {{issue_key}}, then generate the complete refinement document following the template and examples above. \ No newline at end of file +Use the provided Jira data to create the refinement document now. \ No newline at end of file diff --git a/src/rhoai_ai_feature_sizing/stages/refine_feature.py b/src/rhoai_ai_feature_sizing/stages/refine_feature.py index 298ed92a6..518306427 100644 --- a/src/rhoai_ai_feature_sizing/stages/refine_feature.py +++ b/src/rhoai_ai_feature_sizing/stages/refine_feature.py @@ -2,11 +2,38 @@ import os import time from pathlib import Path -from llama_stack_client import Agent +from llama_stack_client import Agent, AgentEventLogger from rhoai_ai_feature_sizing.llama_stack_setup import get_llama_stack_client -INSTRUCTIONS = "You are a senior product manager expert at creating RHOAI feature refinement documents. Follow the provided template and examples precisely." +def _filter_session_id_from_tool_calls(tool_calls): + """Filter out session_id from tool call arguments to prevent MCP validation errors.""" + if not tool_calls: + return tool_calls + + filtered_calls = [] + for call in tool_calls: + if isinstance(call, dict) and "arguments" in call: + # Remove session_id from arguments + filtered_args = { + k: v for k, v in call["arguments"].items() if k != "session_id" + } + filtered_call = call.copy() + filtered_call["arguments"] = filtered_args + filtered_calls.append(filtered_call) + else: + filtered_calls.append(call) + + return filtered_calls + + +INSTRUCTIONS = """You are a senior product manager expert at creating RHOAI feature refinement documents. + +You will be provided with real Jira issue data. Use this data to create a comprehensive refinement document following the provided template and examples precisely. + +CRITICAL: Do not make up or assume any information about the issue. Use only the provided Jira data. + +Create a comprehensive refinement document following the provided template and examples precisely.""" def _load_validation_template() -> str: @@ -20,7 +47,9 @@ def _load_validation_template() -> str: return f.read() -def generate_refinement_with_agent(issue_key: str, template: str) -> str: +def generate_refinement_with_agent( + issue_key: str, template: str, session_id=None +) -> str: """ Generate a comprehensive refinement document using the Llama Stack agent. Uses LLM-based validation with iteration for quality improvement. @@ -42,6 +71,39 @@ def generate_refinement_with_agent(issue_key: str, template: str) -> str: try: client = get_llama_stack_client() + # Get Jira data directly through the client + response = client.tool_runtime.invoke_tool( + tool_name="jira_get_issue", kwargs={"issue_key": issue_key} + ) + + if response.error_message: + raise RuntimeError( + f"Failed to retrieve Jira issue data: {response.error_code} - {response.error_message}" + ) + + try: + + # Llama Stack sometimes returns an array even if the type says otherwise. + content = response.content + if isinstance(content, list): + if not content: + raise RuntimeError("Jira response content is an empty list") + content = content[0] + + # Now extract the text attribute as before + jira_data = getattr(content, "text", None) + if jira_data is None: + raise RuntimeError( + "Jira response content does not have a 'text' attribute" + ) + except Exception as e: + logger.error(f"Failed to retrieve Jira issue data: {e}") + raise RuntimeError(f"Failed to retrieve Jira issue data: {e}") from e + + logger.info( + f"Fetched Jira issue {issue_key}: {jira_data[:500]}{'...' if len(jira_data) > 500 else ''}" + ) + # Load validation template validation_template = _load_validation_template() @@ -50,7 +112,6 @@ def generate_refinement_with_agent(issue_key: str, template: str) -> str: client, model=INFERENCE_MODEL, instructions=INSTRUCTIONS, - tools=["mcp::atlassian"], ) # Create validation agent @@ -58,13 +119,13 @@ def generate_refinement_with_agent(issue_key: str, template: str) -> str: client, model=INFERENCE_MODEL, instructions=validation_template, - tools=[], ) # Generate and iterate on the document best_content = None best_score = 0.0 max_iterations = 3 + validation_result = None for iteration in range(max_iterations): start_time = time.time() @@ -78,27 +139,38 @@ def generate_refinement_with_agent(issue_key: str, template: str) -> str: # Create prompt based on iteration if iteration == 0: - # First attempt: use template directly - prompt = template.replace("{{issue_key}}", issue_key) + # First attempt: use template with Jira data + prompt = f"""Here is the Jira issue data: + --- + {jira_data} + --- + {template.replace("{{issue_key}}", issue_key)}""" else: # Subsequent attempts: include validation feedback prompt = _create_improvement_prompt( - issue_key, template, validation_result + issue_key, template, validation_result, jira_data ) # Generate content - response = generation_agent.create_turn( - messages=[ - { - "role": "user", - "content": prompt, - } - ], - session_id=session_id, - stream=False, - ) + try: + response = generation_agent.create_turn( + messages=[ + { + "role": "user", + "content": prompt, + } + ], + session_id=session_id, + stream=False, + ) + + except Exception as e: + logger.error(f"Error creating turn: {e}") + # Try to continue with next iteration + continue content = response.output_message.content + duration = time.time() - start_time logger.info( @@ -121,7 +193,7 @@ def generate_refinement_with_agent(issue_key: str, template: str) -> str: # Check if we have a passing result if validation_result.get("passed", False) and current_score >= 0.8: logger.info( - f"✅ Document passed validation on iteration {iteration + 1} with score {current_score:.2f}" + f"Document passed validation on iteration {iteration + 1} with score {current_score:.2f}" ) return content @@ -157,12 +229,10 @@ def _validate_with_llm(validation_agent: Agent, content: str, issue_key: str) -> ) validation_prompt = f"""Please evaluate this RHOAI feature refinement document: - ---- -{content} ---- - -Provide your assessment as JSON following the specified format.""" + --- + {content} + --- + Provide your assessment as JSON following the specified format.""" response = validation_agent.create_turn( messages=[{"role": "user", "content": validation_prompt}], @@ -211,7 +281,7 @@ def _extract_validation_json(content: str) -> dict: def _create_improvement_prompt( - issue_key: str, template: str, validation_result: dict + issue_key: str, template: str, validation_result: dict, jira_data: str ) -> str: """Create an improvement prompt based on validation feedback.""" issues = validation_result.get("issues", []) @@ -226,15 +296,21 @@ def _create_improvement_prompt( return f"""Based on the feedback below, please improve the RHOAI feature refinement document for issue {issue_key}. -PREVIOUS ISSUES TO ADDRESS: -{feedback_text} + PREVIOUS ISSUES TO ADDRESS: + {feedback_text} + + STRENGTHS TO MAINTAIN: + {', '.join(validation_result.get('strengths', []))} + + Here is the Jira issue data: -STRENGTHS TO MAINTAIN: -{', '.join(validation_result.get('strengths', []))} + --- + {jira_data} + --- -{template.replace("{{issue_key}}", issue_key)} + {template.replace("{{issue_key}}", issue_key)} -Focus on addressing the specific issues mentioned while maintaining the document's strengths. Ensure all content is specific, actionable, and follows the examples provided.""" + Focus on addressing the specific issues mentioned while maintaining the document's strengths. Ensure all content is specific, actionable, and follows the examples provided.""" def _basic_fallback_validation(content: str) -> dict: diff --git a/uv.lock b/uv.lock index b5a9d67a2..e11c09199 100644 --- a/uv.lock +++ b/uv.lock @@ -411,6 +411,28 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784 }, ] +[[package]] +name = "httptools" +version = "0.6.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/a7/9a/ce5e1f7e131522e6d3426e8e7a490b3a01f39a6696602e1c4f33f9e94277/httptools-0.6.4.tar.gz", hash = "sha256:4e93eee4add6493b59a5c514da98c939b244fce4a0d8879cd3f466562f4b7d5c", size = 240639 } +wheels = [ + { url = 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Added a new endpoint for JIRA metrics to retrieve comprehensive metrics for a JIRA issue and its children. Updated deployment scripts and configuration to support PostgreSQL database integration. Removed outdated contributing and custom API examples documentation. Updated frontend components to include new settings and metrics features. --- API.md | 44 ++ CONTRIBUTING.md | 298 ------------ CUSTOM-API-EXAMPLES.md | 268 ----------- Dockerfile | 98 ++++ OPENSHIFT-DEPLOYMENT.md | 164 ++++++- README.md | 8 +- deploy-to-openshift.sh | 90 +++- frontend/src/app/AppLayout/AppLayout.tsx | 39 +- frontend/src/components/JiraMetrics.tsx | 245 ++++++++++ frontend/src/components/PromptSettings.tsx | 357 +++++++++++++++ frontend/src/components/SessionSidebar.tsx | 16 +- frontend/src/config.ts | 6 +- frontend/src/services/api.ts | 20 + frontend/src/services/localStorage.ts | 115 +++++ frontend/src/types/api.ts | 19 + openshift-deployment.yaml | 143 +++++- src/rhoai_ai_feature_sizing/api/main.py | 90 +++- src/rhoai_ai_feature_sizing/api/models.py | 1 + src/rhoai_ai_feature_sizing/api/schemas.py | 71 +++ src/rhoai_ai_feature_sizing/api/services.py | 431 +++++++++++++++++- .../stages/draft_jiras.py | 30 +- .../stages/refine_feature.py | 14 +- test_jira_metrics.py | 108 +++++ 23 files changed, 2023 insertions(+), 652 deletions(-) delete mode 100644 CONTRIBUTING.md delete mode 100644 CUSTOM-API-EXAMPLES.md create mode 100644 Dockerfile create mode 100644 frontend/src/components/JiraMetrics.tsx create mode 100644 frontend/src/components/PromptSettings.tsx create mode 100644 frontend/src/services/localStorage.ts create mode 100755 test_jira_metrics.py diff --git a/API.md b/API.md index b3c5a270d..52552c56f 100644 --- a/API.md +++ b/API.md @@ -282,6 +282,50 @@ GET /sessions/{session_id}/mcp-usage Get MCP tool usage statistics for analytics. +### JIRA Metrics + +```http +POST /jira/metrics +Content-Type: application/json + +{ + "jira_key": "PROJ-123" +} +``` + +Get comprehensive metrics for a JIRA issue and all its children recursively. This endpoint: + +- Recursively fetches the specified JIRA issue and all its children (subtasks, child issues, etc.) +- Only processes issues with resolution status "Done" +- Calculates story points and completion time metrics per component +- Returns aggregated totals across all components + +**Response Example:** +```json +{ + "components": { + "some-component": { + "total_story_points": 45, + "total_days_to_done": 120.5 + }, + "another-component": { + "total_story_points": 23, + "total_days_to_done": 85.2 + } + }, + "total_story_points": 68, + "total_days_to_done": 120.5, + "processed_issues": 15, + "done_issues": 12 +} +``` + +**Key Metrics:** +- `total_story_points`: Sum of story points for all "Done" issues +- `total_days_to_done`: Days from earliest creation date to latest resolution date (only for "Done" issues) +- `processed_issues`: Total number of issues found recursively +- `done_issues`: Number of issues with "Done" resolution + ## Processing Stages The API processes Jira issues through four stages: diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 10a91f855..000000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,298 +0,0 @@ -# Contributing to RHOAI AI Feature Sizing - -Thank you for your interest in contributing to the RHOAI AI Feature Sizing project! This guide will help you get started with contributing to our Llama Stack-based AI feature estimation tool. - -## 🚀 Quick Start for Contributors - -### Prerequisites - -- Python 3.12+ -- [uv](https://docs.astral.sh/uv/) for dependency management -- Git for version control -- Access to Llama Stack (see [Llama Stack Quickstart](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html#step-1-install-and-setup)) - -### Development Setup - -1. **Fork and Clone** - ```bash - git fork - git clone - cd rhoai-ai-feature-sizing - ``` - -2. **Set up Development Environment** - ```bash - # Install dependencies using uv - uv sync --extra dev - uv pip install -e . - source .venv/bin/activate - ``` - -3. **Configure Environment** - Create a `.env` file with necessary environment variables: - ```bash - LLAMA_STACK_BASE_URL=http://localhost:8321 - LLAMA_STACK_CLIENT_LOG=debug - LLAMA_STACK_PORT=8321 - LLAMA_STACK_CONFIG= - # Add other API keys as needed - ``` - -4. **Run Tests** - ```bash - # Run the test suite - uv run pytest -v - - # Run with environment file - uv run --env-file .env pytest -v - ``` - -## 📋 How to Contribute - -### Discussions → Issues → Pull Requests - -We follow a structured contribution process: - -1. **Have an idea or question?** → Open a [Discussion] -2. **Found a bug with reproduction steps?** → Open an [Issue] -3. **Ready to implement?** → Create a [Pull Request] - -### Types of Contributions - -- 🐛 **Bug Fixes** - Fix existing functionality -- ✨ **New Features** - Add new AI feature sizing capabilities -- 📚 **Documentation** - Improve or add documentation -- 🧪 **Testing** - Add or improve test coverage -- 🎨 **UI/UX** - Enhance user experience -- 🔧 **Infrastructure** - Improve development tools and processes - -### Contribution Workflow - -1. **Fork the Repository** - ```bash - git fork - git clone - cd rhoai-ai-feature-sizing - ``` - -2. **Create a Feature Branch** - ```bash - git checkout -b feature/your-feature-name - # or - git checkout -b bugfix/issue-number - ``` - -3. **Make Your Changes** - - Follow our [coding standards](#coding-standards) - - Add tests for new functionality - - Update documentation as needed - - Ensure all tests pass - -4. **Commit Your Changes** - ```bash - git add . - git commit -m "feat: add new feature estimation algorithm" - ``` - -5. **Push and Create Pull Request** - ```bash - git push origin feature/your-feature-name - ``` - -## 🎯 Coding Standards - -### Python Code Style - -- Follow [PEP 8](https://pep8.org/) style guidelines -- Use meaningful variable and function names -- Add docstrings to all public functions and classes -- Maximum line length: 88 characters (Black formatter) - -### Commit Message Format - -We use [Conventional Commits](https://www.conventionalcommits.org/): - -``` -(): - -[optional body] - -[optional footer(s)] -``` - -**Types:** -- `feat:` - New feature -- `fix:` - Bug fix -- `docs:` - Documentation changes -- `style:` - Code style changes (formatting, etc.) -- `refactor:` - Code refactoring -- `test:` - Adding or updating tests -- `chore:` - Maintenance tasks - -**Examples:** -```bash -feat(estimation): add new ML-based sizing algorithm -fix(jira): resolve authentication timeout issue -docs: update API documentation for new endpoints -test(stages): add unit tests for refine_feature module -``` - -### Code Quality Tools - -We use the following tools to maintain code quality: - -```bash -# Code formatting with Black -uv run black . - -# Import sorting with isort -uv run isort . - -# Linting with flake8 -uv run flake8 . - -# Type checking with mypy -uv run mypy . - -# Pre-commit hooks (recommended) -uv run pre-commit install -uv run pre-commit run --all-files -``` - -## 🧪 Testing Guidelines - -### Running Tests - -```bash -# Run all tests -uv run pytest - -# Run specific test file -uv run pytest tests/test_estimation.py - -# Run with coverage -uv run pytest --cov=src --cov-report=html - -# Run integration tests -uv run pytest tests/integration/ -``` - -### Writing Tests - -- Write unit tests for all new functions and classes -- Include integration tests for Llama Stack interactions -- Use descriptive test names that explain what is being tested -- Mock external dependencies appropriately -- Aim for at least 80% code coverage - -### Test Structure - -```python -def test_feature_estimation_with_valid_input(): - """Test that feature estimation returns expected results with valid input.""" - # Arrange - input_data = {...} - expected_result = {...} - - # Act - result = estimate_feature(input_data) - - # Assert - assert result == expected_result -``` - -## 📚 Documentation Standards - -### Documentation Updates - -When contributing, please ensure: - -1. **Update relevant documentation in `/docs`** - - Add new features to appropriate guides - - Update API documentation for new endpoints - - Include examples and usage patterns - -2. **Update `/docs/README.md`** - - Add links to new documentation - - Update the document status table - -3. **Keep README.md current** - - Update project overview if needed - - Add new quick start steps - -### Documentation Style - -- Use clear, concise language -- Include code examples where helpful -- Use proper markdown formatting -- Add diagrams for complex workflows (using Mermaid when possible) -- Reference [Llama Stack documentation](https://llama-stack.readthedocs.io/en/latest/) for consistency - -## 🔄 Development Workflow - -### Setting Up Llama Stack - -Follow the [Llama Stack Quickstart](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html#step-1-install-and-setup): - -1. **Install and setup** - ```bash - # Install uv (if not already installed) - curl -LsSf https://astral.sh/uv/install.sh | sh - - # Run inference on a Llama model with Ollama - ollama run llama3.2:3b --keepalive 60m - ``` - -2. **Run Llama Stack server** - ```bash - INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template ollama --image-type venv --run - ``` - -### Development Best Practices - -1. **Keep branches focused** - One feature/fix per branch -2. **Write clear commit messages** - Use conventional commits format -3. **Test your changes** - Run tests before submitting PR -4. **Update documentation** - Keep docs current with changes -5. **Review your own PR** - Check diff before requesting review - -### Working with Llama Stack Components - -When developing features that interact with Llama Stack: - -- Refer to the [Llama Stack API Reference](https://llama-stack.readthedocs.io/en/latest/references/api_reference.html) -- Use the official [Llama Stack Client](https://llama-stack.readthedocs.io/en/latest/references/llama_stack_client_python_reference.html) -- Follow [Llama Stack contributing guidelines](https://llama-stack.readthedocs.io/en/latest/contributing/index.html) - -## ❓ Getting Help - -### Resources - -- 📖 [Project Documentation](./docs/README.md) -- 🚀 [Getting Started Guide](./docs/user-guide/getting-started.md) -- 🏗️ [Architecture Overview](./docs/architecture/overview.md) -- 🔧 [Development Setup](./docs/development/setup.md) -- 📚 [Llama Stack Documentation](https://llama-stack.readthedocs.io/en/latest/) - -### Support Channels - -- 💬 **GitHub Discussions** - For questions and ideas -- 🐛 **GitHub Issues** - For bug reports and feature requests -- 📧 **Maintainers** - For sensitive issues or security concerns - -### Contribution Recognition - -We value all contributions! Contributors will be: -- Listed in our contributors section -- Mentioned in release notes for significant contributions -- Given credit in relevant documentation - -## 📄 License - -By contributing to this project, you agree that your contributions will be licensed under the same license as the project. - ---- - -Thank you for contributing to RHOAI AI Feature Sizing! 🙏 - -*For more detailed technical information, please refer to our [comprehensive documentation](./docs/README.md).* \ No newline at end of file diff --git a/CUSTOM-API-EXAMPLES.md b/CUSTOM-API-EXAMPLES.md deleted file mode 100644 index 300e93c61..000000000 --- a/CUSTOM-API-EXAMPLES.md +++ /dev/null @@ -1,268 +0,0 @@ -# Custom API Examples for LLAMA Stack - -This guide provides specific examples for connecting common hosted LLM APIs to LLAMA Stack on OpenShift. - -## 🎯 Quick Reference - -Most modern LLM APIs are **OpenAI-compatible**, meaning they can use the OpenAI provider with a custom `base_url`. Here are common patterns: - -### API Compatibility Test - -First, test if your API is OpenAI-compatible: - -```bash -./test-api-compatibility.sh "https://your-api-endpoint.com" -``` - -## 📋 Common API Examples - -### 1. Red Hat AI Services (RHOAI) Models - -For APIs like your Mistral endpoint: -``` -https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443 -``` - -**Configuration:** -- **API Base URL**: `https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443` -- **API Key**: Usually not required for internal RHOAI services, or provided separately -- **Model Name**: `mistral-small` (for LLAMA Stack) -- **Model ID**: Often same as model name or `auto` - -**Deploy Command:** -```bash -./deploy-to-openshift.sh custom -``` - -**Example Values During Deployment:** -``` -API base URL: https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443 -API key: [leave empty if not required] -Model name for LLAMA Stack: mistral-small -Actual model ID: mistral-small -``` - -### 2. Google Gemini via API - -For company-hosted Gemini models: - -**Configuration:** -- **API Base URL**: `https://your-gemini-proxy.company.com/v1` -- **API Key**: Your Gemini API key -- **Model Name**: `gemini-pro` (for LLAMA Stack) -- **Model ID**: `gemini-1.5-pro` (actual Gemini model ID) - -### 3. Anthropic Claude via API Gateway - -**Configuration:** -- **API Base URL**: `https://your-claude-gateway.company.com/v1` -- **API Key**: Your Claude API key -- **Model Name**: `claude-3-sonnet` (for LLAMA Stack) -- **Model ID**: `claude-3-sonnet-20240229` - -### 4. Self-Hosted vLLM/TGI - -For your own hosted inference servers: - -**Configuration:** -- **API Base URL**: `https://your-vllm-server.company.com/v1` -- **API Key**: [usually not required] -- **Model Name**: `llama2-7b-chat` (for LLAMA Stack) -- **Model ID**: `meta-llama/Llama-2-7b-chat-hf` - -### 5. Hugging Face Inference Endpoints - -**Configuration:** -- **API Base URL**: `https://your-endpoint.endpoints.huggingface.cloud/v1` -- **API Key**: Your HF token -- **Model Name**: `custom-model` (for LLAMA Stack) -- **Model ID**: The model deployed on your endpoint - -## 🔧 Manual Configuration - -If you prefer manual configuration over the script: - -### 1. Create Secrets - -```bash -# For APIs with authentication -oc create secret generic llama-stack-secrets-custom \ - --from-literal=CUSTOM_API_BASE_URL="https://your-api.com" \ - --from-literal=CUSTOM_API_KEY="your-api-key" \ - --from-literal=CUSTOM_MODEL_NAME="your-model" \ - --from-literal=CUSTOM_MODEL_ID="actual-model-id" \ - -n llama-stack - -# For APIs without authentication (remove API_KEY) -oc create secret generic llama-stack-secrets-custom \ - --from-literal=CUSTOM_API_BASE_URL="https://your-api.com" \ - --from-literal=CUSTOM_MODEL_NAME="your-model" \ - --from-literal=CUSTOM_MODEL_ID="actual-model-id" \ - -n llama-stack -``` - -### 2. Deploy Configuration - -```bash -oc apply -f openshift-deployment-custom-api.yaml -``` - -## 🛠️ Advanced Configuration - -### Custom Headers - -Some APIs require custom headers. Edit the ConfigMap: - -```yaml -providers: - inference: - - provider_id: custom-api - provider_type: remote::openai - config: - api_key: ${env.CUSTOM_API_KEY} - base_url: ${env.CUSTOM_API_BASE_URL} - extra_headers: - X-Custom-Header: ${env.CUSTOM_HEADER_VALUE} - Authorization: "Bearer ${env.CUSTOM_API_KEY}" -``` - -### Multiple Models - -To support multiple models from the same API: - -```yaml -models: - - metadata: {} - model_id: model-1 - provider_id: custom-api - provider_model_id: actual-model-1-id - model_type: llm - - metadata: {} - model_id: model-2 - provider_id: custom-api - provider_model_id: actual-model-2-id - model_type: llm -``` - -### Custom Timeouts - -For slower APIs, adjust timeouts: - -```yaml -providers: - inference: - - provider_id: custom-api - provider_type: remote::openai - config: - api_key: ${env.CUSTOM_API_KEY} - base_url: ${env.CUSTOM_API_BASE_URL} - timeout: 120 # 2 minutes - max_retries: 3 -``` - -## 🔍 Troubleshooting - -### Common Issues - -1. **404 Not Found** - - Check if API URL includes `/v1` suffix - - Try with and without `/v1` - - Verify the full endpoint path - -2. **Authentication Errors** - - Verify API key format - - Check if key should be in headers vs query params - - Some APIs use different auth schemes - -3. **Model Not Found** - - List available models: `curl https://your-api.com/v1/models` - - Use exact model ID from the API - - Some APIs use different model naming - -4. **Timeout Errors** - - Increase timeout values - - Check network connectivity - - Monitor API response times - -### Debug Mode - -To enable debug logging, add to the deployment: - -```yaml -env: -- name: LLAMA_STACK_LOG_LEVEL - value: "DEBUG" -``` - -### Testing Your Configuration - -Once deployed, test with: - -```bash -# Get the route URL -ROUTE_URL=$(oc get route llama-stack-route-custom -n llama-stack -o jsonpath='{.spec.host}') - -# Test health -curl http://$ROUTE_URL/v1/health - -# Test models list -curl http://$ROUTE_URL/v1/models - -# Test inference -curl -X POST http://$ROUTE_URL/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "your-model-name", - "messages": [{"role": "user", "content": "Hello"}], - "max_tokens": 50 - }' -``` - -## 📚 API Format Requirements - -For an API to work with LLAMA Stack's OpenAI provider, it should support: - -### Required Endpoints - -1. **Models**: `GET /v1/models` - ```json - { - "data": [ - {"id": "model-name", "object": "model"} - ] - } - ``` - -2. **Chat Completions**: `POST /v1/chat/completions` - ```json - { - "model": "model-name", - "messages": [{"role": "user", "content": "Hello"}] - } - ``` - -### Response Format - -Chat completions should return: -```json -{ - "choices": [ - { - "message": { - "role": "assistant", - "content": "Response text" - }, - "finish_reason": "stop" - } - ] -} -``` - -## 🤝 Getting Help - -- **API not working?** Use the compatibility test script first -- **Authentication issues?** Check API documentation for auth format -- **Model errors?** Verify model names with the provider's model list -- **Performance issues?** Monitor logs and adjust timeouts - -Most hosted LLM services that claim "OpenAI compatibility" should work with minimal configuration! \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 000000000..b38a02456 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,98 @@ +# Multi-stage build for frontend and backend +FROM node:18-alpine as frontend-builder + +# Set working directory for frontend +WORKDIR /frontend + +# Copy frontend package files +COPY frontend/package*.json ./ +COPY frontend/yarn.lock ./ + +# Install frontend dependencies +RUN npm install + +# Copy frontend source code +COPY frontend/ ./ + +# Build the frontend for production +RUN npm run build + +# Backend stage +FROM python:3.12-slim as backend + +# Set working directory +WORKDIR /app + +# Install system dependencies +RUN apt-get update && apt-get install -y \ + curl \ + nginx \ + && rm -rf /var/lib/apt/lists/* + +# Copy pyproject.toml and install Python dependencies +COPY pyproject.toml ./ +RUN pip install --no-cache-dir -e . + +# Copy source code +COPY src/ ./src/ + +# Install the Python package in development mode +RUN pip install --no-cache-dir -e . + +# Copy built frontend from frontend-builder stage +COPY --from=frontend-builder /frontend/dist /var/www/html + +# Create nginx configuration for serving frontend and proxying API +RUN echo 'server { \ + listen 80; \ + server_name localhost; \ + \ + # Serve frontend static files \ + location / { \ + root /var/www/html; \ + try_files $uri $uri/ /index.html; \ + add_header Cache-Control "no-cache, no-store, must-revalidate"; \ + add_header Pragma "no-cache"; \ + add_header Expires "0"; \ + } \ + \ + # Proxy API requests to backend \ + location /api/ { \ + proxy_pass http://127.0.0.1:8000/; \ + proxy_set_header Host $host; \ + proxy_set_header X-Real-IP $remote_addr; \ + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; \ + proxy_set_header X-Forwarded-Proto $scheme; \ + } \ + \ + # Health check endpoint \ + location /health { \ + proxy_pass http://127.0.0.1:8000/health; \ + proxy_set_header Host $host; \ + proxy_set_header X-Real-IP $remote_addr; \ + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; \ + proxy_set_header X-Forwarded-Proto $scheme; \ + } \ +}' > /etc/nginx/sites-available/default + +# Create directory for SQLite databases +RUN mkdir -p /tmp + +# Expose the nginx port (frontend + API proxy) +EXPOSE 80 + +# Set environment variables +ENV PYTHONPATH=/app +ENV PYTHONUNBUFFERED=1 + +# Create startup script that runs both nginx and the Python API +RUN echo '#!/bin/bash \ +set -e \ +echo "Starting nginx..." \ +nginx -g "daemon on;" \ +echo "Starting Python API..." \ +exec python -m rhoai_ai_feature_sizing.run_api' > /app/start.sh && \ +chmod +x /app/start.sh + +# Default command to run both services +CMD ["/app/start.sh"] \ No newline at end of file diff --git a/OPENSHIFT-DEPLOYMENT.md b/OPENSHIFT-DEPLOYMENT.md index 7901ad63e..0b688595d 100644 --- a/OPENSHIFT-DEPLOYMENT.md +++ b/OPENSHIFT-DEPLOYMENT.md @@ -1,23 +1,63 @@ -# Deploying LLAMA Stack to OpenShift with External Model Providers +# Deploying RHOAI AI Feature Sizing with Frontend to OpenShift -This guide shows you how to deploy LLAMA Stack to OpenShift using external model providers instead of hosting your own GPU-based models. +This guide shows you how to deploy the complete RHOAI AI Feature Sizing application to OpenShift, including both the React frontend and Python backend with external model providers. ## 🎯 Overview -Instead of using Ollama locally, this deployment connects to external model providers like: +This deployment provides a **scalable, production-ready** setup with: + +### **Complete Web Application** +- **React Frontend** with PatternFly UI components for JIRA session management +- **Interactive Chat Panel** for real-time session monitoring +- **Session Management** with create, view, delete functionality +- **Custom Prompt Configuration** via web interface +- **nginx Proxy** serving frontend and routing API requests + +### **External Model Providers** +Instead of using Ollama locally, connects to external providers like: - **OpenAI** (GPT-4o, GPT-4o-mini) - **Azure OpenAI** (Your deployed models) - **External vLLM services** (Hosted elsewhere) - **HuggingFace TGI endpoints** (Hosted services) - **Custom APIs** (Mistral, Gemini, Claude, RHOAI models, etc.) +### **Production Database** +- **PostgreSQL 15** with persistent storage for scalability +- **Multi-pod capable** (no SQLite limitations) +- **Automatic database initialization** and schema management +- **Secure credential management** via Kubernetes secrets + +## 🏗️ Architecture Overview + +The deployment creates a complete web application with the following architecture: + +``` +📱 User Browser → 🌐 OpenShift Route → nginx (Port 80) → { + 📄 Static Files (React App) + 🔀 /api/* → Python API (Port 8000) + } + ↓ + 🐍 Python API → 🦙 LLAMA Stack → 🤖 External LLM + ↓ ↓ + 🗄️ PostgreSQL ← 📋 Jira MCP +``` + +**Components:** +- **nginx**: Serves React frontend and proxies API requests +- **React Frontend**: PatternFly-based UI for session management +- **Python API**: FastAPI backend for session processing +- **LLAMA Stack**: AI orchestration layer +- **PostgreSQL**: Persistent session storage +- **Jira MCP**: Jira integration service + ## 🚀 Quick Start ### Prerequisites -1. OpenShift cluster access with `oc` CLI configured -2. Appropriate permissions to create namespaces, deployments, secrets, and routes -3. API credentials for your chosen provider: +1. **OpenShift cluster access** with `oc` CLI configured +2. **Docker** and access to a **container registry** (e.g., Quay.io, Docker Hub, OpenShift internal registry) +3. **Appropriate permissions** to create namespaces, deployments, secrets, and routes +4. **API credentials** for your chosen provider: - **OpenAI**: API key from https://platform.openai.com/api-keys - **Azure OpenAI**: API key, endpoint, and deployment names - **vLLM**: URL of your hosted vLLM service @@ -32,7 +72,27 @@ cd rhoai-ai-feature-sizing chmod +x deploy-to-openshift.sh ``` -### 2. Deploy to OpenShift +### 2. Build and Push Custom Docker Image + +The RHOAI AI Feature Sizing application requires a custom Docker image that includes both the React frontend and Python backend: + +```bash +# Build and push to your container registry (replace with your registry URL) +docker build -t quay.io/yourusername/rhoai-ai-feature-sizing:latest . +docker push quay.io/yourusername/rhoai-ai-feature-sizing:latest +``` + +**The Dockerfile creates a multi-stage build that:** +- **Stage 1**: Builds the React frontend using Node.js and webpack +- **Stage 2**: Sets up Python backend, nginx, and combines everything + +**Important**: Update `openshift-deployment.yaml` with your actual image URL: +```yaml +# Replace this line in the rhoai-ai-feature-sizing deployment: +image: quay.io/yourusername/rhoai-ai-feature-sizing:latest +``` + +### 3. Deploy to OpenShift Run the deployment script: @@ -51,7 +111,8 @@ The script will: - Create the `llama-stack` namespace - Prompt you for API details and credentials - Deploy LLAMA Stack configured for your API -- Deploy the Jira MCP service +- Deploy the Jira MCP service +- **Deploy PostgreSQL database** for scalable data storage - Create routes for external access ### 3. Update Your Environment @@ -66,10 +127,16 @@ cp env.openshift.example .env ### 4. Test Your Deployment ```bash -# Test the health endpoint +# Open the web interface in your browser +open http://your-app-route-url + +# Test the API health endpoint +curl http://your-app-route-url/health + +# Test the LLAMA Stack endpoint curl http://your-llama-stack-route-url/v1/health -# Test with your existing CLI +# Test with your existing CLI (update API URL in .env) uv run python -m rhoai_ai_feature_sizing.main stage refine PROJ-123 ``` @@ -77,12 +144,27 @@ uv run python -m rhoai_ai_feature_sizing.main stage refine PROJ-123 If you prefer manual deployment or need to customize the configuration: -### 1. Create Namespace +### 1. Build and Push Custom Image + +First, build and push the custom Docker image that includes both frontend and backend: + +```bash +# Build the multi-stage image (frontend + backend) +docker build -t quay.io/yourusername/rhoai-ai-feature-sizing:latest . + +# Push to registry +docker push quay.io/yourusername/rhoai-ai-feature-sizing:latest + +# Update openshift-deployment.yaml with your image URL +sed -i 's|quay.io/yourusername/rhoai-ai-feature-sizing:latest|your-registry/your-image:tag|g' openshift-deployment.yaml +``` + +### 2. Create Namespace ```bash oc create namespace llama-stack ``` -### 2. Create Secrets +### 3. Create Secrets ```bash # Create API secrets @@ -101,12 +183,30 @@ oc create secret generic jira-secrets \ -n llama-stack ``` -### 3. Deploy Configuration +### 4. Deploy Configuration ```bash oc apply -f openshift-deployment.yaml ``` +## 🗄️ Database Architecture + +The deployment includes a **PostgreSQL database** for scalability and production readiness: + +- **PostgreSQL 15** container with persistent storage (5GB) +- **Dedicated secrets** for database credentials +- **Health checks** with readiness and liveness probes +- **Resource limits** for stable operation +- **Cluster-internal service** for secure database access + +### Database Connection +- **Internal URL**: `postgresql-service.llama-stack.svc.cluster.local:5432` +- **Database**: `rhoai_sessions` +- **Credentials**: Stored in `database-secrets` secret +- **Persistence**: 5GB PersistentVolumeClaim + +This replaces SQLite for multi-pod scalability and data persistence. + ## 📊 Supported APIs LLAMA Stack works with **any OpenAI-compatible API endpoint**. Examples include: @@ -304,6 +404,44 @@ Refer to external guides like: - [OpenShift AI vLLM examples](https://github.com/rh-aiservices-bu/llm-on-openshift) - [Community vLLM deployments](https://github.com/rcarrat-AI/hftgi-llms) +## 🔍 Troubleshooting + +### Database Issues +```bash +# Check PostgreSQL pod status +oc get pods -n llama-stack -l app=postgresql + +# View PostgreSQL logs +oc logs -n llama-stack deployment/postgresql + +# Test database connectivity from API pod +oc exec -n llama-stack deployment/rhoai-ai-feature-sizing-api -- python -c " +import os +from sqlalchemy import create_engine +url = os.getenv('DATABASE_URL') +engine = create_engine(url) +with engine.connect() as conn: + result = conn.execute('SELECT version()') + print('Database connected:', result.fetchone()[0]) +" +``` + +### API Health Check +```bash +# Check API health (includes database status) +ROUTE_URL=$(oc get route rhoai-ai-feature-sizing-api-route -n llama-stack -o jsonpath='{.spec.host}') +curl http://$ROUTE_URL/health +``` + +### Scaling Issues +```bash +# Scale API pods (PostgreSQL supports multiple connections) +oc scale deployment/rhoai-ai-feature-sizing-api --replicas=3 -n llama-stack + +# Check all pods are ready +oc get pods -n llama-stack -l app=rhoai-ai-feature-sizing-api +``` + ## 🐛 Getting Help - Check the [LLAMA Stack documentation](https://llama-stack.readthedocs.io/) diff --git a/README.md b/README.md index ffaddba22..6759e5183 100644 --- a/README.md +++ b/README.md @@ -59,7 +59,10 @@ docker-compose ps **OpenShift Production (New):** - Uses any OpenAI-compatible API (OpenAI, Mistral, Gemini, Azure OpenAI, vLLM, etc.) +- **PostgreSQL database** for multi-pod scalability (no SQLite limitations) +- **Persistent storage** for data reliability - No local GPU required +- Requires building and pushing a custom Docker image - See [OpenShift Deployment Guide](OPENSHIFT-DEPLOYMENT.md) ### Basic Usage @@ -326,7 +329,10 @@ rhoai-ai-feature-sizing/ │ ├── main.py # Core logic │ └── stages/ # Processing pipelines ├── tests/ # Test files -├── docker-compose.yml # Services configuration +├── docker-compose.yml # Local services configuration +├── Dockerfile # Custom Docker image for OpenShift +├── openshift-deployment.yaml # OpenShift deployment configuration +├── deploy-to-openshift.sh # OpenShift deployment script ├── pyproject.toml # Python dependencies └── README.md ``` diff --git a/deploy-to-openshift.sh b/deploy-to-openshift.sh index 523dfee21..f48f36a68 100755 --- a/deploy-to-openshift.sh +++ b/deploy-to-openshift.sh @@ -7,7 +7,7 @@ set -e NAMESPACE="llama-stack" -echo "🚀 Deploying LLAMA Stack to OpenShift..." +echo "🚀 Deploying LLAMA Stack with Frontend and Backend to OpenShift..." # Function to encode base64 encode_base64() { @@ -47,13 +47,11 @@ echo "📡 API Configuration:" prompt_input "Enter your API base URL (e.g., https://api.openai.com/v1, https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443)" API_BASE_URL prompt_input "Enter your API key (leave empty if not required)" API_KEY prompt_input "Enter the model name for LLAMA Stack (e.g., gpt-4o-mini, mistral-small)" MODEL_NAME -prompt_input "Enter the actual model ID used by the API (often same as above, or 'auto')" MODEL_ID echo "" echo "📋 Jira Configuration:" prompt_input "Enter your Jira URL (e.g., https://your-company.atlassian.net)" JIRA_URL -prompt_input "Enter your Jira API Token" JIRA_API_TOKEN true -prompt_input "Enter your Jira Username" JIRA_USERNAME +prompt_input "Enter your Jira API Token" JIRA_PERSONAL_TOKEN true # Create secrets with base64 encoded values echo "🔐 Creating secrets..." @@ -66,10 +64,7 @@ metadata: type: Opaque data: VLLM_URL: $(encode_base64 "$API_BASE_URL") - VLLM_INFERENCE_MODEL: $(encode_base64 "$MODEL_ID") - CUSTOM_API_BASE_URL: $(encode_base64 "$API_BASE_URL") - CUSTOM_MODEL_NAME: $(encode_base64 "$MODEL_NAME") - CUSTOM_MODEL_ID: $(encode_base64 "$MODEL_ID") + VLLM_INFERENCE_MODEL: $(encode_base64 "$MODEL_NAME") EOF # Only add API key if provided @@ -78,7 +73,6 @@ if [ -n "$API_KEY" ]; then oc patch secret llama-stack-secrets -n $NAMESPACE --patch="$(cat </dev/null || echo "Route not available") +APP_ROUTE_URL=$(oc get route rhoai-ai-feature-sizing-route -n $NAMESPACE -o jsonpath='{.spec.host}' 2>/dev/null || echo "Route not available") +LLAMA_ROUTE_URL=$(oc get route llama-stack-route -n $NAMESPACE -o jsonpath='{.spec.host}' 2>/dev/null || echo "Route not available") -if [ "$ROUTE_URL" != "Route not available" ]; then - echo "🌐 LLAMA Stack URL: http://$ROUTE_URL" +if [ "$APP_ROUTE_URL" != "Route not available" ]; then + echo "🌐 RHOAI AI Feature Sizing App (Frontend + API): http://$APP_ROUTE_URL" + echo "🔧 LLAMA Stack URL: http://$LLAMA_ROUTE_URL" + echo "" + echo "🎯 Frontend Features:" + echo " • React-based web interface for managing JIRA RFE sessions" + echo " • Interactive chat panel for real-time session monitoring" + echo " • Session management with create, view, and delete functionality" + echo " • Custom prompt configuration support" + echo "" echo "🔧 Update your .env file with:" - echo "LLAMA_STACK_URL=http://$ROUTE_URL" + echo "LLAMA_STACK_URL=http://$LLAMA_ROUTE_URL" echo "MCP_ATLASSIAN_URL=http://jira-mcp-service.$NAMESPACE.svc.cluster.local:9000/sse" echo "" echo "📝 Your API configuration:" @@ -128,28 +153,49 @@ if [ "$ROUTE_URL" != "Route not available" ]; then echo "" echo "🎯 Jira MCP configuration:" echo " • Jira URL: $JIRA_URL" - echo " • Username: $JIRA_USERNAME" echo " • MCP Service: jira-mcp-service.$NAMESPACE.svc.cluster.local:9000" echo " • Available Jira tools: search, get_issue, create_issue, update_issue, transitions, etc." + echo "" + echo "🗄️ Database configuration:" + echo " • PostgreSQL 15 with persistent storage (5GB)" + echo " • Database: rhoai_sessions" + echo " • Internal URL: postgresql-service.$NAMESPACE.svc.cluster.local:5432" + echo " • Multi-pod scalable (replaces SQLite)" else echo "⚠️ Route not created. Access via port-forward:" + echo "oc port-forward svc/rhoai-ai-feature-sizing-service 8080:80 -n $NAMESPACE" echo "oc port-forward svc/llama-stack-service 8321:8321 -n $NAMESPACE" fi echo "" echo "🧪 Test your deployment:" -echo "curl http://$ROUTE_URL/v1/health" +echo "# Frontend (web interface):" +echo "curl http://$APP_ROUTE_URL" +echo "# API health check:" +echo "curl http://$APP_ROUTE_URL/health" +echo "# LLAMA Stack health:" +echo "curl http://$LLAMA_ROUTE_URL/v1/health" echo "" echo "🔍 Test Jira MCP service (from within cluster):" echo "oc exec -n $NAMESPACE deployment/llama-stack -- curl http://jira-mcp-service:9000/health" echo "" +echo "🗄️ Test PostgreSQL database:" +echo "oc exec -n $NAMESPACE deployment/postgresql -- psql -U rhoai_user -d rhoai_sessions -c 'SELECT version();'" +echo "" echo "📚 Next steps:" -echo "1. Update your application's LLAMA_STACK_URL environment variable" -echo "2. Test the API endpoints" -echo "3. Run your existing CLI commands against the new endpoint" +echo "1. 🌐 Open the web interface: http://$APP_ROUTE_URL" +echo "2. 🔧 Create a new JIRA session through the UI" +echo "3. 💬 Monitor session progress in real-time via the chat panel" +echo "4. 🛠️ Configure custom prompts via the UI settings (if needed)" +echo "5. 📊 Use the existing CLI commands against the new endpoint" echo "" echo "🎯 Example API configurations that work with this setup:" echo " • OpenAI: https://api.openai.com/v1 + gpt-4o-mini" echo " • Mistral: https://mistral-small-24b-w8a8-maas-apicast-production.apps.prod.rhoai.rh-aiservices-bu.com:443 + mistral-small" echo " • Azure OpenAI: https://your-resource.openai.azure.com/ + your-deployment-name" -echo " • Any other OpenAI-compatible API" \ No newline at end of file +echo " • Any other OpenAI-compatible API" +echo "" +echo "🎨 Architecture Overview:" +echo " 📱 Frontend (React/PatternFly) → nginx proxy → 🐍 Python API → 🦙 LLAMA Stack → 🤖 External LLM" +echo " ↓" +echo " 🗄️ PostgreSQL Database" \ No newline at end of file diff --git a/frontend/src/app/AppLayout/AppLayout.tsx b/frontend/src/app/AppLayout/AppLayout.tsx index 164290cdf..bf4af3666 100644 --- a/frontend/src/app/AppLayout/AppLayout.tsx +++ b/frontend/src/app/AppLayout/AppLayout.tsx @@ -1,17 +1,24 @@ import * as React from 'react'; import { useNavigate } from 'react-router-dom'; import { + Button, Masthead, MastheadBrand, + MastheadContent, MastheadLogo, MastheadMain, Page, PageSidebar, PageSidebarBody, SkipToContent, + Toolbar, + ToolbarContent, + ToolbarItem, } from '@patternfly/react-core'; -// import { BarsIcon } from '@patternfly/react-icons'; +import { ChartLineIcon, CogIcon } from '@patternfly/react-icons'; import SessionSidebar from '../../components/SessionSidebar'; +import PromptSettings from '../../components/PromptSettings'; +import JiraMetrics from '../../components/JiraMetrics'; import { Session } from '../../types/api'; interface IAppLayout { @@ -20,6 +27,8 @@ interface IAppLayout { const AppLayout: React.FunctionComponent = ({ children }) => { const [selectedSession, setSelectedSession] = React.useState(null); + const [isSettingsOpen, setIsSettingsOpen] = React.useState(false); + const [isJiraMetricsOpen, setIsJiraMetricsOpen] = React.useState(false); const navigate = useNavigate(); const handleSessionSelect = (session: Session) => { @@ -78,6 +87,32 @@ const AppLayout: React.FunctionComponent = ({ children }) => { + + + + + + + + + + + + ); @@ -107,6 +142,8 @@ const AppLayout: React.FunctionComponent = ({ children }) => { return ( {children} + setIsSettingsOpen(false)} /> + setIsJiraMetricsOpen(false)} /> ); }; diff --git a/frontend/src/components/JiraMetrics.tsx b/frontend/src/components/JiraMetrics.tsx new file mode 100644 index 000000000..6da2540ce --- /dev/null +++ b/frontend/src/components/JiraMetrics.tsx @@ -0,0 +1,245 @@ +import React, { useState } from 'react'; +import { + Alert, + AlertVariant, + Button, + Card, + CardBody, + CardTitle, + DescriptionList, + DescriptionListDescription, + DescriptionListGroup, + DescriptionListTerm, + Form, + FormGroup, + Grid, + GridItem, + Label, + Modal, + ModalBody, + ModalFooter, + ModalVariant, + Spinner, + Split, + SplitItem, + Stack, + StackItem, + TextInput, +} from '@patternfly/react-core'; +import { ChartLineIcon, CheckCircleIcon, ClockIcon, ListIcon } from '@patternfly/react-icons'; +import { apiService } from '../services/api'; +import { JiraMetricsResponse } from '../types/api'; + +type JiraMetricsProps = { + isOpen: boolean; + onClose: () => void; +}; + +const JiraMetrics: React.FunctionComponent = ({ isOpen, onClose }) => { + const [jiraKey, setJiraKey] = useState(''); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + const [metrics, setMetrics] = useState(null); + + const handleSubmit = async (e: React.FormEvent) => { + e.preventDefault(); + if (!jiraKey.trim()) { + setError('Please enter a JIRA key'); + return; + } + + setLoading(true); + setError(null); + setMetrics(null); + + try { + const response = await apiService.getJiraMetrics({ jira_key: jiraKey.trim() }); + setMetrics(response); + } catch (err: unknown) { + const errorMessage = + (err as { response?: { data?: { detail?: string } } })?.response?.data?.detail || + 'Failed to fetch JIRA metrics'; + setError(errorMessage); + } finally { + setLoading(false); + } + }; + + const handleClose = () => { + setJiraKey(''); + setError(null); + setMetrics(null); + setLoading(false); + onClose(); + }; + + const formatDays = (days: number): string => { + if (days === 0) return '0 days'; + if (days < 1) return `${Math.round(days * 24)} hours`; + return `${Math.round(days)} days`; + }; + + return ( + + + + +
    + + + + setJiraKey(value)} + placeholder="e.g., PROJ-123" + isDisabled={loading} + /> + + + + + + +
    +     + + {error && ( + + + {error} + + + )} + + {metrics && ( + + + {/* Overall Summary */} + + + + + + Overall Summary + + + + + + + + + + + + + Total Story Points + + + + + + Total Days to Done + + + + + + + + + + Issues Processed + + + + + + Issues Done + + + + + + + + + + + + {/* Component Breakdown */} + {Object.keys(metrics.components).length > 0 && ( + + + Component Breakdown + + + {Object.entries(metrics.components).map(([componentName, componentMetrics]) => ( + + + {componentName} + + + + Story Points + + + + + + Days to Done + + + + + + + + + ))} + + + + + )} + + {Object.keys(metrics.components).length === 0 && ( + + + All issues may be unassigned to components or have no 'Done' resolution. + + + )} + + + )} +     +     + + + +     + ); +}; + +export default JiraMetrics; diff --git a/frontend/src/components/PromptSettings.tsx b/frontend/src/components/PromptSettings.tsx new file mode 100644 index 000000000..9feb8f37b --- /dev/null +++ b/frontend/src/components/PromptSettings.tsx @@ -0,0 +1,357 @@ +import React, { useCallback, useEffect, useMemo, useState } from 'react'; +import { + Alert, + Button, + Dropdown, + DropdownItem, + DropdownList, + Flex, + FlexItem, + FormGroup, + MenuToggle, + MenuToggleElement, + Modal, + ModalBody, + ModalFooter, + ModalHeader, + ModalVariant, + TextArea, + Title, +} from '@patternfly/react-core'; +import { + CogIcon, + ExternalLinkAltIcon, + FileDownloadIcon, + FileUploadIcon, + RedoIcon, + SaveIcon, +} from '@patternfly/react-icons'; +import { apiService } from '../services/api'; +import { + CustomPrompts, + deleteCustomPrompt, + exportCustomPromptsToFile, + importCustomPromptsFromFile, + loadCustomPrompts, + saveCustomPrompt, +} from '../services/localStorage'; + +type PromptSettingsProps = { + isOpen: boolean; + onClose: () => void; +}; + +const GITHUB_BASE_URL = + 'https://github.com/rhoai-feature-sizing/rhoai-ai-feature-sizing/tree/main/src/rhoai_ai_feature_sizing/prompts'; + +const PromptSettings: React.FunctionComponent = ({ isOpen, onClose }) => { + const [availablePrompts, setAvailablePrompts] = useState([]); + const [selectedPrompt, setSelectedPrompt] = useState(''); + const [originalContent, setOriginalContent] = useState(''); + const [currentContent, setCurrentContent] = useState(''); + const [customPrompts, setCustomPrompts] = useState({}); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + const [isDropdownOpen, setIsDropdownOpen] = useState(false); + const [hasUnsavedChanges, setHasUnsavedChanges] = useState(false); + + // Load available prompts when modal opens + useEffect(() => { + if (isOpen) { + loadAvailablePrompts(); + setCustomPrompts(loadCustomPrompts()); + } + }, [isOpen]); + + // Track unsaved changes + useEffect(() => { + const isCustom = customPrompts[selectedPrompt] !== undefined; + const comparisonContent = isCustom ? customPrompts[selectedPrompt] : originalContent; + setHasUnsavedChanges(currentContent !== comparisonContent && currentContent !== ''); + }, [currentContent, originalContent, customPrompts, selectedPrompt]); + + const loadAvailablePrompts = useCallback(async () => { + try { + setLoading(true); + setError(null); + const prompts = await apiService.getAvailablePrompts(); + setAvailablePrompts(prompts); + + // Select first prompt by default + if (prompts.length > 0 && !selectedPrompt) { + setSelectedPrompt(prompts[0]); + } + } catch (err) { + setError(err instanceof Error ? err.message : 'Failed to load prompts'); + } finally { + setLoading(false); + } + }, [selectedPrompt]); + + const loadPromptContent = useCallback( + async (promptName: string) => { + if (!promptName) return; + + try { + setLoading(true); + setError(null); + + // Check if we have a custom version first + const customContent = customPrompts[promptName]; + if (customContent) { + setCurrentContent(customContent); + setOriginalContent(''); // We'll load original on demand + } else { + // Load original content + const response = await apiService.getPromptContent(promptName); + setOriginalContent(response.content); + setCurrentContent(response.content); + } + } catch (err) { + setError(err instanceof Error ? err.message : 'Failed to load prompt content'); + } finally { + setLoading(false); + } + }, + [customPrompts], + ); + + // Load content when selected prompt changes + useEffect(() => { + if (selectedPrompt) { + loadPromptContent(selectedPrompt); + } + }, [selectedPrompt, loadPromptContent]); + + const handleSave = useCallback(() => { + if (!selectedPrompt || !currentContent.trim()) { + setError('Please select a prompt and provide content'); + return; + } + + saveCustomPrompt(selectedPrompt, currentContent); + setCustomPrompts({ ...customPrompts, [selectedPrompt]: currentContent }); + setHasUnsavedChanges(false); + setError(null); + }, [selectedPrompt, currentContent, customPrompts]); + + const handleResetToDefault = useCallback(async () => { + if (!selectedPrompt) return; + + try { + setLoading(true); + setError(null); + + // Delete custom version + deleteCustomPrompt(selectedPrompt); + const newCustomPrompts = { ...customPrompts }; + delete newCustomPrompts[selectedPrompt]; + setCustomPrompts(newCustomPrompts); + + // Load original content + const response = await apiService.getPromptContent(selectedPrompt); + setOriginalContent(response.content); + setCurrentContent(response.content); + setHasUnsavedChanges(false); + } catch (err) { + setError(err instanceof Error ? err.message : 'Failed to reset prompt'); + } finally { + setLoading(false); + } + }, [selectedPrompt, customPrompts]); + + const handleExportToFile = useCallback(() => { + try { + exportCustomPromptsToFile(); + } catch (err) { + setError(err instanceof Error ? err.message : 'Failed to export prompts'); + } + }, []); + + const handleImportFromFile = useCallback( + (event: React.ChangeEvent) => { + const file = event.target.files?.[0]; + if (!file) return; + + importCustomPromptsFromFile(file) + .then((imported) => { + setCustomPrompts({ ...customPrompts, ...imported }); + setError(null); + // Reload current prompt if it was imported + if (selectedPrompt && imported[selectedPrompt]) { + setCurrentContent(imported[selectedPrompt]); + } + }) + .catch((err) => { + setError(err instanceof Error ? err.message : 'Failed to import prompts'); + }); + + // Reset file input + event.target.value = ''; + }, + [customPrompts, selectedPrompt], + ); + + const handleOpenInGitHub = useCallback(() => { + if (!selectedPrompt) return; + const url = `${GITHUB_BASE_URL}/${selectedPrompt}.md`; + window.open(url, '_blank'); + }, [selectedPrompt]); + + const isCustomVersion = useMemo(() => { + return customPrompts[selectedPrompt] !== undefined; + }, [customPrompts, selectedPrompt]); + + const dropdownItems = useMemo(() => { + return availablePrompts.map((prompt) => ( + setSelectedPrompt(prompt)}> + + {prompt.replace(/_/g, ' ')} + {customPrompts[prompt] && ( + + (Custom) + + )} + + + )); + }, [availablePrompts, customPrompts]); + + return ( + + + + <Flex alignItems={{ default: 'alignItemsCenter' }} spaceItems={{ default: 'spaceItemsSm' }}> + <FlexItem> + <CogIcon /> + </FlexItem> + <FlexItem>Prompt Settings</FlexItem> + </Flex> + + + + + + {error && ( + + + {error} + + + )} + + + + setIsDropdownOpen(false)} + toggle={(toggleRef: React.Ref) => ( + setIsDropdownOpen(!isDropdownOpen)} + isExpanded={isDropdownOpen} + style={{ width: '300px' }} + > + {selectedPrompt ? ( + + {selectedPrompt.replace(/_/g, ' ')} + {isCustomVersion && ( + + + (Custom) + + + )} + + ) : ( + 'Select a prompt...' + )} + + )} + > + {dropdownItems} + + + + + + +