docs: restructure roadmap into modular three-phase architecture (INITIAL-9/10/11)#47
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Decompose monolithic INITIAL-9 into three specialized technical phases:
- INITIAL-9: RAG Knowledge Base ("The Memory")
- pgvector + OpenAI embeddings
- Markdown/OpenAPI-aware chunking
- Semantic retrieval endpoints
- INITIAL-10: Agentic Layer ("The Brain")
- PydanticAI agents (Experiment Orchestrator, RAG Assistant)
- Tool orchestration with structured outputs
- Human-in-the-loop approval workflow
- INITIAL-11: ForecastLab Dashboard ("The Face")
- React 19 + Vite + shadcn/ui
- TanStack Table/Query for data management
- Recharts for time series visualization
- Agent chat interface with streaming
Update PHASE-index.md and DAILY-FLOW.md to align with new structure.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Comprehensive PRP for INITIAL-9 RAG Knowledge Base feature: - pgvector + SQLAlchemy 2.0 integration patterns - Markdown-aware and OpenAPI-aware chunking - Async OpenAI embeddings with batch processing - HNSW index for cosine similarity search - 15 ordered implementation tasks - 5-level validation loop (syntax → types → unit → integration → smoke) - Full ORM models and Pydantic schemas - Known gotchas and anti-patterns documented Confidence score: 8.5/10 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Comprehensive PRP for INITIAL-10 Agentic Layer feature: - PydanticAI agent framework integration - Experiment Orchestrator Agent (backtest → compare → deploy) - RAG Assistant Agent (query → retrieve → answer with citations) - Human-in-the-loop approval workflow for sensitive actions - WebSocket streaming for real-time token delivery - Session persistence with JSONB message history - 17 ordered implementation tasks - Tool definitions for registry, backtesting, forecasting, RAG - Full Pydantic schemas and ORM models Confidence score: 7.5/10 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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* feat(registry): implement model registry for run tracking and deployments (#36) * docs: expand INITIAL-7 with lifecycle, lineage, and artifact integrity details Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(registry): implement model registry for run tracking and deployments Add model registry feature (PRP-7) with: - ORM models: ModelRun with JSONB columns (model_config, metrics, runtime_info), DeploymentAlias for mutable deployment pointers - Storage: LocalFSProvider with SHA-256 integrity verification and path traversal prevention, abstract interface for future S3/GCS support - Service: RegistryService with state machine validation, duplicate detection, config hashing, and run comparison - API endpoints: CRUD for runs and aliases, artifact verification, run comparison with config/metrics diffs - Database: Alembic migration with GIN indexes for JSONB containment queries - Tests: 103 unit tests (schemas, storage, service) + 24 integration tests - Example: registry_demo.py demonstrating full workflow Run lifecycle: PENDING → RUNNING → SUCCESS/FAILED → ARCHIVED Aliases can only point to SUCCESS runs for deployment safety. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: update documentation for model registry implementation - README.md: Add registry to project structure, API endpoints section, and example reference - docs/ARCHITECTURE.md: Update section 7.6 with full implementation details, add registry endpoints to section 8, mark Phase 1 complete - docs/PHASE-index.md: Mark phases 4-6 as completed, add detailed completion entries for Forecasting, Backtesting, and Registry Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: add PHASE documentation for forecasting, backtesting, and registry Create missing phase documentation files to complete the project's implementation records: - 4-FORECASTING.md: Model zoo with BaseForecaster interface, train/predict endpoints, and joblib persistence - 5-BACKTESTING.md: Time-series CV with expanding/sliding strategies, metrics calculation, and baseline comparisons - 6-MODEL_REGISTRY.md: Run tracking with state machine, deployment aliases, and SHA-256 artifact integrity verification Update PHASE-index.md to link to the new documentation files. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(registry): resolve type checking issues with Pydantic model_config alias - Add pydantic.mypy plugin to pyproject.toml for proper Pydantic type checking - Use model_config_data instead of model_config alias in tests to avoid collision with Pydantic's reserved model_config attribute - Update _model_to_response to use model_validate() for proper alias handling - Change docker-compose postgres port to 5433 to avoid conflicts Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: resolve CI failures for registry PR - Import registry models in alembic/env.py for schema validation - Fix import order and remove extraneous f-strings in registry_demo.py - Add type: ignore comments for frozen model tests with pydantic.mypy plugin Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: prevent db_session fixtures from dropping all tables The data_platform and root conftest.py db_session fixtures were dropping all tables after each test, causing subsequent integration tests to fail when they couldn't find migrated tables. Changes: - Remove Base.metadata.drop_all from db_session fixtures - Tests now rely on migrations for table creation - Each test just rolls back its own changes Also fixes ruff format issue in examples/registry_demo.py. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: add proper test data cleanup to db_session fixtures Update data_platform and ingest test fixtures to clean up test data explicitly instead of dropping all tables or just rolling back. - data_platform: delete test stores, products, calendar entries - ingest: delete test stores, products, sales, calendar entries This ensures test isolation while preserving migrated tables. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: use separate session for test cleanup to avoid transaction issues When tests cause integrity errors, the session enters a failed state. Use a fresh session for cleanup to avoid PendingRollbackError. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: use contextlib.suppress instead of try-except-pass Replace try-except-pass patterns with contextlib.suppress to satisfy ruff S110 linting rule. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * fix: code improvements and documentation fixes - Add date range filter to SalesDaily cleanup in ingest tests - Enforce artifact_hash presence before verification in registry routes - Compute SHA256 from saved file instead of source in storage - Fix override_get_db to mirror production transaction semantics - Filter DeploymentAlias cleanup to only test runs - Update database port to 5433 in config and .env.example - Add language identifiers to fenced code blocks (MD040) - Fix table formatting for markdownlint MD060 - Update PR reference in PHASE/6-MODEL_REGISTRY.md - Convert bare URLs to markdown links in INITIAL-7.md - Wrap __init__.py in backticks in PRP-7 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * sync: update dev from phase-6 (#40) * chore: release v0.2.0 (#37) * feat(registry): implement model registry for run tracking and deployments (#36) * docs: expand INITIAL-7 with lifecycle, lineage, and artifact integrity details Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(registry): implement model registry for run tracking and deployments Add model registry feature (PRP-7) with: - ORM models: ModelRun with JSONB columns (model_config, metrics, runtime_info), DeploymentAlias for mutable deployment pointers - Storage: LocalFSProvider with SHA-256 integrity verification and path traversal prevention, abstract interface for future S3/GCS support - Service: RegistryService with state machine validation, duplicate detection, config hashing, and run comparison - API endpoints: CRUD for runs and aliases, artifact verification, run comparison with config/metrics diffs - Database: Alembic migration with GIN indexes for JSONB containment queries - Tests: 103 unit tests (schemas, storage, service) + 24 integration tests - Example: registry_demo.py demonstrating full workflow Run lifecycle: PENDING → RUNNING → SUCCESS/FAILED → ARCHIVED Aliases can only point to SUCCESS runs for deployment safety. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: update documentation for model registry implementation - README.md: Add registry to project structure, API endpoints section, and example reference - docs/ARCHITECTURE.md: Update section 7.6 with full implementation details, add registry endpoints to section 8, mark Phase 1 complete - docs/PHASE-index.md: Mark phases 4-6 as completed, add detailed completion entries for Forecasting, Backtesting, and Registry Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: add PHASE documentation for forecasting, backtesting, and registry Create missing phase documentation files to complete the project's implementation records: - 4-FORECASTING.md: Model zoo with BaseForecaster interface, train/predict endpoints, and joblib persistence - 5-BACKTESTING.md: Time-series CV with expanding/sliding strategies, metrics calculation, and baseline comparisons - 6-MODEL_REGISTRY.md: Run tracking with state machine, deployment aliases, and SHA-256 artifact integrity verification Update PHASE-index.md to link to the new documentation files. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(registry): resolve type checking issues with Pydantic model_config alias - Add pydantic.mypy plugin to pyproject.toml for proper Pydantic type checking - Use model_config_data instead of model_config alias in tests to avoid collision with Pydantic's reserved model_config attribute - Update _model_to_response to use model_validate() for proper alias handling - Change docker-compose postgres port to 5433 to avoid conflicts Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: resolve CI failures for registry PR - Import registry models in alembic/env.py for schema validation - Fix import order and remove extraneous f-strings in registry_demo.py - Add type: ignore comments for frozen model tests with pydantic.mypy plugin Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: prevent db_session fixtures from dropping all tables The data_platform and root conftest.py db_session fixtures were dropping all tables after each test, causing subsequent integration tests to fail when they couldn't find migrated tables. Changes: - Remove Base.metadata.drop_all from db_session fixtures - Tests now rely on migrations for table creation - Each test just rolls back its own changes Also fixes ruff format issue in examples/registry_demo.py. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: add proper test data cleanup to db_session fixtures Update data_platform and ingest test fixtures to clean up test data explicitly instead of dropping all tables or just rolling back. - data_platform: delete test stores, products, calendar entries - ingest: delete test stores, products, sales, calendar entries This ensures test isolation while preserving migrated tables. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: use separate session for test cleanup to avoid transaction issues When tests cause integrity errors, the session enters a failed state. Use a fresh session for cleanup to avoid PendingRollbackError. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: use contextlib.suppress instead of try-except-pass Replace try-except-pass patterns with contextlib.suppress to satisfy ruff S110 linting rule. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * fix: code improvements and documentation fixes - Add date range filter to SalesDaily cleanup in ingest tests - Enforce artifact_hash presence before verification in registry routes - Compute SHA256 from saved file instead of source in storage - Fix override_get_db to mirror production transaction semantics - Filter DeploymentAlias cleanup to only test runs - Update database port to 5433 in config and .env.example - Add language identifiers to fenced code blocks (MD040) - Fix table formatting for markdownlint MD060 - Update PR reference in PHASE/6-MODEL_REGISTRY.md - Convert bare URLs to markdown links in INITIAL-7.md - Wrap __init__.py in backticks in PRP-7 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * chore(main): release 0.2.0 (#38) Release-As: 0.2.0 Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * chore(main): release 0.2.0 (#39) * chore(main): release 0.2.0 * chore: trigger CI --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Gabe@w7dev <gabor@w7-7.net> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * feat(serving-layer): implement PRP-8 agent-first API design (#42) * docs(initial-8): expand serving layer requirements Add specifications for job-driven orchestration, dimension discovery endpoints, standardized API protocols (filtering/pagination), and agent-first API design patterns for LLM tool-calling optimization. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs(prp-8): add serving layer implementation spec Comprehensive PRP for FastAPI serving layer including: - Dimensions module for store/product discovery endpoints - Analytics module for KPI/drilldown queries - Jobs module for async-ready task orchestration - RFC 7807 problem details for semantic error responses - OpenAPI export optimization for LLM tool-calling 26 tasks with validation gates and 8.5/10 confidence score. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(serving-layer): implement PRP-8 agent-first API design Add RFC 7807 Problem Details for semantic error responses: - ProblemDetail schema with type URIs and error codes - application/problem+json content type - Validation exception handler with field-level errors Add dimensions module for store/product discovery: - GET /dimensions/stores with pagination, filtering, search - GET /dimensions/products with pagination, filtering, search - LLM-optimized Field descriptions for tool-calling Add analytics module for KPI aggregations: - GET /analytics/kpis with date range and dimension filters - GET /analytics/drilldowns for store/product/category/region/date - Revenue share and ranking calculations Add jobs module for async-ready task orchestration: - POST /jobs for train/predict/backtest operations - Job model with JSONB params/results - Status transitions: pending → running → completed/failed Integration: - New settings: analytics_max_rows, jobs_retention_days - Register routers in main.py - Alembic migration for jobs table Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: update documentation for PRP-8 serving layer Update README.md: - Add dimensions, analytics, jobs modules to project structure - Document new API endpoints with examples - Add RFC 7807 error response documentation Update docs/ARCHITECTURE.md: - Mark serving layer section as implemented - Add configuration settings for new modules - Update roadmap with Phase-2 completion Update docs/PHASE-index.md: - Add Phase 7 (Serving Layer) as completed - Update phase overview table - Add version history entry Create docs/PHASE/7-SERVING_LAYER.md: - Comprehensive phase documentation - API endpoint specifications - Database schema and migration details - Usage examples and test coverage Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * style: fix ruff formatting Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * fix(serving-layer): improve analytics validation and jobs run_id handling - Add validate_date_range helper to analytics routes for reusable date validation - Apply date range validation to both get_kpis and get_drilldowns endpoints - Fix total_revenue_all calculation to use full dataset before limiting - Add run_id to train job result for downstream predict jobs - Fix predict job to resolve run_id to model metadata from bundle - Update test fixtures to use 32-char hex IDs per schema requirements Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * style: format jobs service Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: restructure roadmap into modular three-phase architecture (INITIAL-9/10/11) (#47) * docs: restructure INITIAL-9 into modular three-phase roadmap Decompose monolithic INITIAL-9 into three specialized technical phases: - INITIAL-9: RAG Knowledge Base ("The Memory") - pgvector + OpenAI embeddings - Markdown/OpenAPI-aware chunking - Semantic retrieval endpoints - INITIAL-10: Agentic Layer ("The Brain") - PydanticAI agents (Experiment Orchestrator, RAG Assistant) - Tool orchestration with structured outputs - Human-in-the-loop approval workflow - INITIAL-11: ForecastLab Dashboard ("The Face") - React 19 + Vite + shadcn/ui - TanStack Table/Query for data management - Recharts for time series visualization - Agent chat interface with streaming Update PHASE-index.md and DAILY-FLOW.md to align with new structure. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs(prp): add PRP-9 RAG Knowledge Base implementation plan Comprehensive PRP for INITIAL-9 RAG Knowledge Base feature: - pgvector + SQLAlchemy 2.0 integration patterns - Markdown-aware and OpenAPI-aware chunking - Async OpenAI embeddings with batch processing - HNSW index for cosine similarity search - 15 ordered implementation tasks - 5-level validation loop (syntax → types → unit → integration → smoke) - Full ORM models and Pydantic schemas - Known gotchas and anti-patterns documented Confidence score: 8.5/10 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs(prp): add PRP-10 Agentic Layer implementation plan Comprehensive PRP for INITIAL-10 Agentic Layer feature: - PydanticAI agent framework integration - Experiment Orchestrator Agent (backtest → compare → deploy) - RAG Assistant Agent (query → retrieve → answer with citations) - Human-in-the-loop approval workflow for sensitive actions - WebSocket streaming for real-time token delivery - Session persistence with JSONB message history - 17 ordered implementation tasks - Tool definitions for registry, backtesting, forecasting, RAG - Full Pydantic schemas and ORM models Confidence score: 7.5/10 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * docs(prp): add PRP-11 ForecastLab Dashboard implementation plan (#48) Comprehensive PRP for INITIAL-11 (The Face) with: - 24 implementation tasks across 6 phases - React 19 + Vite + shadcn/ui + TanStack Table/Query - TypeScript types matching all backend API schemas - Reusable DataTable with server-side pagination - TimeSeriesChart component with Recharts - WebSocket hook for agent chat streaming - Complete documentation links and gotchas Confidence score: 7.5/10 (chat depends on INITIAL-10) Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * feat(rag): implement PRP-9 RAG Knowledge Base with pgvector (#49) * feat(rag): implement PRP-9 RAG Knowledge Base with pgvector Add RAG (Retrieval-Augmented Generation) knowledge base feature for semantic document indexing and retrieval using PostgreSQL pgvector. Key components: - Document indexing with markdown-aware and OpenAPI-aware chunking - Semantic retrieval using cosine similarity with configurable thresholds - Idempotent re-indexing via SHA-256 content hash comparison - OpenAI text-embedding-3-small for embeddings (1536 dimensions) - HNSW index for fast approximate nearest neighbor search API endpoints: - POST /rag/index - Index documents with automatic chunking - POST /rag/retrieve - Semantic search with relevance scoring - GET /rag/sources - List indexed sources with statistics - DELETE /rag/sources/{source_id} - Remove source and chunks Includes: - ORM models: DocumentSource, DocumentChunk with Vector column - Pydantic v2 schemas with strict validation - 68 unit tests + 14 integration tests - Migration for pgvector extension and RAG tables - Examples and environment configuration Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(rag): add Ollama embedding provider with OpenAI-compatible API - Add EmbeddingProvider abstract base class with provider pattern - Refactor existing OpenAI code to OpenAIEmbeddingProvider - Add OllamaEmbeddingProvider using /v1/embeddings endpoint - Supports configurable dimensions parameter - Uses OpenAI-compatible response format - Add config settings: rag_embedding_provider, ollama_base_url, ollama_embedding_model - Add migration for dynamic embedding dimension support - Update tests for both providers (25 tests) Enables local/LAN embedding generation without OpenAI API dependency. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: add Ollama embedding provider documentation - Update .env.example with Ollama configuration options - Add RAG Knowledge Base section to README with: - Embedding provider options (OpenAI/Ollama) - Example index and retrieve requests - Configuration examples for both providers Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: add Phase 8 RAG Knowledge Base documentation - Create docs/PHASE/8-RAG_KNOWLEDGE_BASE.md with full phase details - Update docs/PHASE-index.md: - Mark Phase 8 as Completed in overview table - Add Phase 8 summary to Completed Phases section - Add entry to Version History Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(ci): add RAG models import to alembic env and format tests - Add rag models import to alembic/env.py for schema validation - Format test_embeddings.py to pass ruff format check Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * fix: address code review issues for RAG module and docs - Make migration deterministic by hardcoding dimension values instead of reading from environment (alembic migration) - Add pyyaml dependency for YAML parsing in OpenAPI chunker - Fix token count logging to capture original count before truncation - Add path traversal protection to RAG service _read_content_from_path (mirrors registry/storage.py pattern) - Fix markdown linting issues: - Add language tags to fenced code blocks (MD040) - Fix table pipe spacing (MD060) - Fix index_docs.py to treat 200 same as 201 for idempotent responses - Add test for path traversal protection Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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* feat: RAG Knowledge Base, Serving Layer, and Model Registry (#50) * feat(registry): implement model registry for run tracking and deployments (#36) * docs: expand INITIAL-7 with lifecycle, lineage, and artifact integrity details Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(registry): implement model registry for run tracking and deployments Add model registry feature (PRP-7) with: - ORM models: ModelRun with JSONB columns (model_config, metrics, runtime_info), DeploymentAlias for mutable deployment pointers - Storage: LocalFSProvider with SHA-256 integrity verification and path traversal prevention, abstract interface for future S3/GCS support - Service: RegistryService with state machine validation, duplicate detection, config hashing, and run comparison - API endpoints: CRUD for runs and aliases, artifact verification, run comparison with config/metrics diffs - Database: Alembic migration with GIN indexes for JSONB containment queries - Tests: 103 unit tests (schemas, storage, service) + 24 integration tests - Example: registry_demo.py demonstrating full workflow Run lifecycle: PENDING → RUNNING → SUCCESS/FAILED → ARCHIVED Aliases can only point to SUCCESS runs for deployment safety. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: update documentation for model registry implementation - README.md: Add registry to project structure, API endpoints section, and example reference - docs/ARCHITECTURE.md: Update section 7.6 with full implementation details, add registry endpoints to section 8, mark Phase 1 complete - docs/PHASE-index.md: Mark phases 4-6 as completed, add detailed completion entries for Forecasting, Backtesting, and Registry Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: add PHASE documentation for forecasting, backtesting, and registry Create missing phase documentation files to complete the project's implementation records: - 4-FORECASTING.md: Model zoo with BaseForecaster interface, train/predict endpoints, and joblib persistence - 5-BACKTESTING.md: Time-series CV with expanding/sliding strategies, metrics calculation, and baseline comparisons - 6-MODEL_REGISTRY.md: Run tracking with state machine, deployment aliases, and SHA-256 artifact integrity verification Update PHASE-index.md to link to the new documentation files. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(registry): resolve type checking issues with Pydantic model_config alias - Add pydantic.mypy plugin to pyproject.toml for proper Pydantic type checking - Use model_config_data instead of model_config alias in tests to avoid collision with Pydantic's reserved model_config attribute - Update _model_to_response to use model_validate() for proper alias handling - Change docker-compose postgres port to 5433 to avoid conflicts Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: resolve CI failures for registry PR - Import registry models in alembic/env.py for schema validation - Fix import order and remove extraneous f-strings in registry_demo.py - Add type: ignore comments for frozen model tests with pydantic.mypy plugin Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: prevent db_session fixtures from dropping all tables The data_platform and root conftest.py db_session fixtures were dropping all tables after each test, causing subsequent integration tests to fail when they couldn't find migrated tables. Changes: - Remove Base.metadata.drop_all from db_session fixtures - Tests now rely on migrations for table creation - Each test just rolls back its own changes Also fixes ruff format issue in examples/registry_demo.py. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: add proper test data cleanup to db_session fixtures Update data_platform and ingest test fixtures to clean up test data explicitly instead of dropping all tables or just rolling back. - data_platform: delete test stores, products, calendar entries - ingest: delete test stores, products, sales, calendar entries This ensures test isolation while preserving migrated tables. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: use separate session for test cleanup to avoid transaction issues When tests cause integrity errors, the session enters a failed state. Use a fresh session for cleanup to avoid PendingRollbackError. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: use contextlib.suppress instead of try-except-pass Replace try-except-pass patterns with contextlib.suppress to satisfy ruff S110 linting rule. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * fix: code improvements and documentation fixes - Add date range filter to SalesDaily cleanup in ingest tests - Enforce artifact_hash presence before verification in registry routes - Compute SHA256 from saved file instead of source in storage - Fix override_get_db to mirror production transaction semantics - Filter DeploymentAlias cleanup to only test runs - Update database port to 5433 in config and .env.example - Add language identifiers to fenced code blocks (MD040) - Fix table formatting for markdownlint MD060 - Update PR reference in PHASE/6-MODEL_REGISTRY.md - Convert bare URLs to markdown links in INITIAL-7.md - Wrap __init__.py in backticks in PRP-7 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * sync: update dev from phase-6 (#40) * chore: release v0.2.0 (#37) * feat(registry): implement model registry for run tracking and deployments (#36) * docs: expand INITIAL-7 with lifecycle, lineage, and artifact integrity details Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(registry): implement model registry for run tracking and deployments Add model registry feature (PRP-7) with: - ORM models: ModelRun with JSONB columns (model_config, metrics, runtime_info), DeploymentAlias for mutable deployment pointers - Storage: LocalFSProvider with SHA-256 integrity verification and path traversal prevention, abstract interface for future S3/GCS support - Service: RegistryService with state machine validation, duplicate detection, config hashing, and run comparison - API endpoints: CRUD for runs and aliases, artifact verification, run comparison with config/metrics diffs - Database: Alembic migration with GIN indexes for JSONB containment queries - Tests: 103 unit tests (schemas, storage, service) + 24 integration tests - Example: registry_demo.py demonstrating full workflow Run lifecycle: PENDING → RUNNING → SUCCESS/FAILED → ARCHIVED Aliases can only point to SUCCESS runs for deployment safety. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: update documentation for model registry implementation - README.md: Add registry to project structure, API endpoints section, and example reference - docs/ARCHITECTURE.md: Update section 7.6 with full implementation details, add registry endpoints to section 8, mark Phase 1 complete - docs/PHASE-index.md: Mark phases 4-6 as completed, add detailed completion entries for Forecasting, Backtesting, and Registry Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: add PHASE documentation for forecasting, backtesting, and registry Create missing phase documentation files to complete the project's implementation records: - 4-FORECASTING.md: Model zoo with BaseForecaster interface, train/predict endpoints, and joblib persistence - 5-BACKTESTING.md: Time-series CV with expanding/sliding strategies, metrics calculation, and baseline comparisons - 6-MODEL_REGISTRY.md: Run tracking with state machine, deployment aliases, and SHA-256 artifact integrity verification Update PHASE-index.md to link to the new documentation files. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(registry): resolve type checking issues with Pydantic model_config alias - Add pydantic.mypy plugin to pyproject.toml for proper Pydantic type checking - Use model_config_data instead of model_config alias in tests to avoid collision with Pydantic's reserved model_config attribute - Update _model_to_response to use model_validate() for proper alias handling - Change docker-compose postgres port to 5433 to avoid conflicts Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: resolve CI failures for registry PR - Import registry models in alembic/env.py for schema validation - Fix import order and remove extraneous f-strings in registry_demo.py - Add type: ignore comments for frozen model tests with pydantic.mypy plugin Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: prevent db_session fixtures from dropping all tables The data_platform and root conftest.py db_session fixtures were dropping all tables after each test, causing subsequent integration tests to fail when they couldn't find migrated tables. Changes: - Remove Base.metadata.drop_all from db_session fixtures - Tests now rely on migrations for table creation - Each test just rolls back its own changes Also fixes ruff format issue in examples/registry_demo.py. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: add proper test data cleanup to db_session fixtures Update data_platform and ingest test fixtures to clean up test data explicitly instead of dropping all tables or just rolling back. - data_platform: delete test stores, products, calendar entries - ingest: delete test stores, products, sales, calendar entries This ensures test isolation while preserving migrated tables. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: use separate session for test cleanup to avoid transaction issues When tests cause integrity errors, the session enters a failed state. Use a fresh session for cleanup to avoid PendingRollbackError. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: use contextlib.suppress instead of try-except-pass Replace try-except-pass patterns with contextlib.suppress to satisfy ruff S110 linting rule. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * fix: code improvements and documentation fixes - Add date range filter to SalesDaily cleanup in ingest tests - Enforce artifact_hash presence before verification in registry routes - Compute SHA256 from saved file instead of source in storage - Fix override_get_db to mirror production transaction semantics - Filter DeploymentAlias cleanup to only test runs - Update database port to 5433 in config and .env.example - Add language identifiers to fenced code blocks (MD040) - Fix table formatting for markdownlint MD060 - Update PR reference in PHASE/6-MODEL_REGISTRY.md - Convert bare URLs to markdown links in INITIAL-7.md - Wrap __init__.py in backticks in PRP-7 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * chore(main): release 0.2.0 (#38) Release-As: 0.2.0 Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * chore(main): release 0.2.0 (#39) * chore(main): release 0.2.0 * chore: trigger CI --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Gabe@w7dev <gabor@w7-7.net> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * feat(serving-layer): implement PRP-8 agent-first API design (#42) * docs(initial-8): expand serving layer requirements Add specifications for job-driven orchestration, dimension discovery endpoints, standardized API protocols (filtering/pagination), and agent-first API design patterns for LLM tool-calling optimization. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs(prp-8): add serving layer implementation spec Comprehensive PRP for FastAPI serving layer including: - Dimensions module for store/product discovery endpoints - Analytics module for KPI/drilldown queries - Jobs module for async-ready task orchestration - RFC 7807 problem details for semantic error responses - OpenAPI export optimization for LLM tool-calling 26 tasks with validation gates and 8.5/10 confidence score. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(serving-layer): implement PRP-8 agent-first API design Add RFC 7807 Problem Details for semantic error responses: - ProblemDetail schema with type URIs and error codes - application/problem+json content type - Validation exception handler with field-level errors Add dimensions module for store/product discovery: - GET /dimensions/stores with pagination, filtering, search - GET /dimensions/products with pagination, filtering, search - LLM-optimized Field descriptions for tool-calling Add analytics module for KPI aggregations: - GET /analytics/kpis with date range and dimension filters - GET /analytics/drilldowns for store/product/category/region/date - Revenue share and ranking calculations Add jobs module for async-ready task orchestration: - POST /jobs for train/predict/backtest operations - Job model with JSONB params/results - Status transitions: pending → running → completed/failed Integration: - New settings: analytics_max_rows, jobs_retention_days - Register routers in main.py - Alembic migration for jobs table Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: update documentation for PRP-8 serving layer Update README.md: - Add dimensions, analytics, jobs modules to project structure - Document new API endpoints with examples - Add RFC 7807 error response documentation Update docs/ARCHITECTURE.md: - Mark serving layer section as implemented - Add configuration settings for new modules - Update roadmap with Phase-2 completion Update docs/PHASE-index.md: - Add Phase 7 (Serving Layer) as completed - Update phase overview table - Add version history entry Create docs/PHASE/7-SERVING_LAYER.md: - Comprehensive phase documentation - API endpoint specifications - Database schema and migration details - Usage examples and test coverage Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * style: fix ruff formatting Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * fix(serving-layer): improve analytics validation and jobs run_id handling - Add validate_date_range helper to analytics routes for reusable date validation - Apply date range validation to both get_kpis and get_drilldowns endpoints - Fix total_revenue_all calculation to use full dataset before limiting - Add run_id to train job result for downstream predict jobs - Fix predict job to resolve run_id to model metadata from bundle - Update test fixtures to use 32-char hex IDs per schema requirements Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * style: format jobs service Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: restructure roadmap into modular three-phase architecture (INITIAL-9/10/11) (#47) * docs: restructure INITIAL-9 into modular three-phase roadmap Decompose monolithic INITIAL-9 into three specialized technical phases: - INITIAL-9: RAG Knowledge Base ("The Memory") - pgvector + OpenAI embeddings - Markdown/OpenAPI-aware chunking - Semantic retrieval endpoints - INITIAL-10: Agentic Layer ("The Brain") - PydanticAI agents (Experiment Orchestrator, RAG Assistant) - Tool orchestration with structured outputs - Human-in-the-loop approval workflow - INITIAL-11: ForecastLab Dashboard ("The Face") - React 19 + Vite + shadcn/ui - TanStack Table/Query for data management - Recharts for time series visualization - Agent chat interface with streaming Update PHASE-index.md and DAILY-FLOW.md to align with new structure. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs(prp): add PRP-9 RAG Knowledge Base implementation plan Comprehensive PRP for INITIAL-9 RAG Knowledge Base feature: - pgvector + SQLAlchemy 2.0 integration patterns - Markdown-aware and OpenAPI-aware chunking - Async OpenAI embeddings with batch processing - HNSW index for cosine similarity search - 15 ordered implementation tasks - 5-level validation loop (syntax → types → unit → integration → smoke) - Full ORM models and Pydantic schemas - Known gotchas and anti-patterns documented Confidence score: 8.5/10 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs(prp): add PRP-10 Agentic Layer implementation plan Comprehensive PRP for INITIAL-10 Agentic Layer feature: - PydanticAI agent framework integration - Experiment Orchestrator Agent (backtest → compare → deploy) - RAG Assistant Agent (query → retrieve → answer with citations) - Human-in-the-loop approval workflow for sensitive actions - WebSocket streaming for real-time token delivery - Session persistence with JSONB message history - 17 ordered implementation tasks - Tool definitions for registry, backtesting, forecasting, RAG - Full Pydantic schemas and ORM models Confidence score: 7.5/10 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * docs(prp): add PRP-11 ForecastLab Dashboard implementation plan (#48) Comprehensive PRP for INITIAL-11 (The Face) with: - 24 implementation tasks across 6 phases - React 19 + Vite + shadcn/ui + TanStack Table/Query - TypeScript types matching all backend API schemas - Reusable DataTable with server-side pagination - TimeSeriesChart component with Recharts - WebSocket hook for agent chat streaming - Complete documentation links and gotchas Confidence score: 7.5/10 (chat depends on INITIAL-10) Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * feat(rag): implement PRP-9 RAG Knowledge Base with pgvector (#49) * feat(rag): implement PRP-9 RAG Knowledge Base with pgvector Add RAG (Retrieval-Augmented Generation) knowledge base feature for semantic document indexing and retrieval using PostgreSQL pgvector. Key components: - Document indexing with markdown-aware and OpenAPI-aware chunking - Semantic retrieval using cosine similarity with configurable thresholds - Idempotent re-indexing via SHA-256 content hash comparison - OpenAI text-embedding-3-small for embeddings (1536 dimensions) - HNSW index for fast approximate nearest neighbor search API endpoints: - POST /rag/index - Index documents with automatic chunking - POST /rag/retrieve - Semantic search with relevance scoring - GET /rag/sources - List indexed sources with statistics - DELETE /rag/sources/{source_id} - Remove source and chunks Includes: - ORM models: DocumentSource, DocumentChunk with Vector column - Pydantic v2 schemas with strict validation - 68 unit tests + 14 integration tests - Migration for pgvector extension and RAG tables - Examples and environment configuration Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(rag): add Ollama embedding provider with OpenAI-compatible API - Add EmbeddingProvider abstract base class with provider pattern - Refactor existing OpenAI code to OpenAIEmbeddingProvider - Add OllamaEmbeddingProvider using /v1/embeddings endpoint - Supports configurable dimensions parameter - Uses OpenAI-compatible response format - Add config settings: rag_embedding_provider, ollama_base_url, ollama_embedding_model - Add migration for dynamic embedding dimension support - Update tests for both providers (25 tests) Enables local/LAN embedding generation without OpenAI API dependency. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: add Ollama embedding provider documentation - Update .env.example with Ollama configuration options - Add RAG Knowledge Base section to README with: - Embedding provider options (OpenAI/Ollama) - Example index and retrieve requests - Configuration examples for both providers Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: add Phase 8 RAG Knowledge Base documentation - Create docs/PHASE/8-RAG_KNOWLEDGE_BASE.md with full phase details - Update docs/PHASE-index.md: - Mark Phase 8 as Completed in overview table - Add Phase 8 summary to Completed Phases section - Add entry to Version History Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(ci): add RAG models import to alembic env and format tests - Add rag models import to alembic/env.py for schema validation - Format test_embeddings.py to pass ruff format check Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * fix: address code review issues for RAG module and docs - Make migration deterministic by hardcoding dimension values instead of reading from environment (alembic migration) - Add pyyaml dependency for YAML parsing in OpenAPI chunker - Fix token count logging to capture original count before truncation - Add path traversal protection to RAG service _read_content_from_path (mirrors registry/storage.py pattern) - Fix markdown linting issues: - Add language tags to fenced code blocks (MD040) - Fix table pipe spacing (MD060) - Fix index_docs.py to treat 200 same as 201 for idempotent responses - Add test for path traversal protection Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * ci: add release-please branch trigger and wire workflow_dispatch ref (#52) - Add 'release-please--branches--**' pattern to match actual release-please branch naming (e.g., release-please--branches--main--components--forecastlabai) - Add 'ref' input to workflow_dispatch with proper type declaration - Wire ref input to all checkout steps via CHECKOUT_REF env var - Use inputs.ref || github.ref for predictable fallback behavior - Update concurrency group to respect manual ref input Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> * chore(main): release 0.2.2 (#51) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Gabor Szabo <168316277+w7-mgfcode@users.noreply.github.com> --------- Co-authored-by: Gabe@w7dev <gabor@w7-7.net> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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Summary
New Three-Phase Architecture
Files Changed
New Files:
INITIAL-10.md- Agentic Layer specification (421 lines)INITIAL-11.md- Dashboard specification (417 lines)PRPs/PRP-9-rag-knowledge-base.md- RAG implementation plan (776 lines)PRPs/PRP-10-agentic-layer.md- Agent implementation plan (920 lines)Modified Files:
INITIAL-9.md- Rewritten for RAG-only focusdocs/PHASE-index.md- Updated phase descriptionsdocs/DAILY-FLOW.md- Updated next phases sectionImplementation Order
Test plan
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