AI Research Assistant Backend

This is the backend of the AI Research Assistant project. It is a FastAPI project that provides the API for the Chrome extension and web frontend.

Features

• Paper Processing: Extract and analyze academic papers from various sources
• AI Integration: OpenAI GPT-4 and Anthropic Claude for paper summarization
• Vector Search: Semantic search using embeddings and Pinecone
• Citation Networks: Build and analyze citation relationships
• Background Processing: Async paper processing with Celery
• Authentication: JWT-based user authentication
• Database: PostgreSQL with SQLAlchemy ORM

Getting Started

To get started clone the repository and run the following commands:

git clone <repository-url>
cd ai-research-assistant/backend

This project uses UV to manage the virtual environment, packages and the project itself. To install UV run the following command:

curl -LsSf https://astral.sh/uv/install.sh | sh

After installing UV, you can run the following command to install all the project requirements:

uv sync

This should create a virtual environment and install all the requirements.

Environment Setup

Create a .env file in the backend directory:

cp .env.example .env
# Edit .env with your configuration

Required environment variables:

• DATABASE_URL: PostgreSQL connection string
• REDIS_URL: Redis connection string
• OPENAI_API_KEY: OpenAI API key
• PINECONE_API_KEY: Pinecone API key
• SECRET_KEY: JWT secret key

Database Setup

Run database migrations:

# Create migration
uv run alembic revision --autogenerate -m "Initial migration"

# Apply migrations
uv run alembic upgrade head

Running the Application

To run the project:

# Development mode with auto-reload
uv run fastapi dev app/main.py

# Production mode
uv run fastapi run app/main.py

The API will be available at http://localhost:8000

API Documentation will be available at http://localhost:8000/docs

Background Workers

Start Celery workers for background processing:

# Start worker
uv run celery -A app.services.celery_app worker --loglevel=info

# Start flower for monitoring (optional)
uv run celery -A app.services.celery_app flower

Adding Dependencies

To add a new dependency to the project:

uv add <package-name>

For development dependencies:

uv add --dev <package-name>

Running Tests

Run all tests:

uv run pytest

Run specific test types:

# Unit tests only
uv run pytest tests/unit

# Integration tests only
uv run pytest tests/integration

# Specific test file
uv run pytest tests/unit/test_paper_service.py

Code Quality

Run linting and formatting:

# Install pre-commit hooks
uv run pre-commit install

# Run all checks
uv run pre-commit run --all-files

# Run specific tools
uv run ruff check .
uv run black .
uv run mypy .

Project Structure

app/
├── api/                    # API routes and endpoints
│   ├── decorators.py      # Route decorators
│   └── v1/                # API version 1
├── app_instance.py        # FastAPI app instance
├── core/                  # Core functionality
│   ├── config.py         # Configuration settings
│   ├── security.py       # Authentication & authorization
│   └── app_logging.py    # Logging configuration
├── db/                    # Database layer
│   ├── models.py         # SQLAlchemy models
│   ├── database.py       # Database connection
│   └── queries/          # Database queries
├── schemas/               # Pydantic models
├── services/              # Business logic
│   ├── ai_service.py     # AI integration
│   ├── paper_service.py  # Paper processing
│   └── celery_app.py     # Background tasks
├── utils/                 # Utility functions
└── main.py               # Application entry point

API Endpoints

Authentication

• POST /api/v1/auth/register - User registration
• POST /api/v1/auth/login - User login
• GET /api/v1/auth/me - Get current user

Papers

• POST /api/v1/papers/ - Add paper from URL
• GET /api/v1/papers/{paper_id} - Get paper details
• POST /api/v1/papers/search - Search papers
• GET /api/v1/papers/{paper_id}/summary - Get AI summary

Knowledge Base

• GET /api/v1/knowledge/ - Get user's knowledge entries
• POST /api/v1/knowledge/ - Create knowledge entry
• GET /api/v1/knowledge/search - Semantic search

Contributing

1. Create a feature branch
2. Make your changes
3. Run tests and linting
4. Submit a pull request

License

MIT License