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VCell Backend

A FastAPI-based backend service for the VCell AI platform. This service provides RESTful APIs for biomodel retrieval, AI-powered analysis, knowledge base management, and vector database operations.

Architecture

The backend follows a clean architecture pattern with the following structure:

backend/
├── app/
│   ├── main.py                 # FastAPI application entry point
│   ├── core/                   # Core configurations and utilities
│   │   ├── config.py          # Application settings
│   │   └── logger.py          # Logging configuration
│   ├── routes/                # API route definitions
│   │   ├── vcelldb_router.py  # VCellDB API wrapper routes
│   │   ├── llms_router.py     # LLM and AI analysis routes
│   │   ├── knowledge_base_router.py  # Knowledge base management
│   │   └── qdrant_router.py   # Vector database operations
│   ├── controllers/           # Business logic layer
│   ├── services/              # External service integrations
│   ├── schemas/               # Pydantic data models
│   └── utils/                 # Utility functions
├── tests/                     # Test suite
├── pyproject.toml            # Project configuration
└── Dockerfile                # Container configuration

Features

Core Functionality

  • VCellDB Integration: Wrapper for VCell biomodel database API
  • AI-Powered Analysis: LLM integration with tool calling capabilities
  • Knowledge Base: Vector-based document storage and retrieval
  • File Processing: Support for PDF, text, and markdown files
  • RESTful APIs: Comprehensive API endpoints with automatic documentation

API Endpoints

VCellDB Routes (/vcelldb)

  • GET /biomodel - Retrieve biomodels with filtering and sorting
  • GET /biomodel/{id}/simulations - Get simulations for a biomodel
  • GET /biomodel/{id}/biomodel.vcml - Retrieve VCML file content
  • GET /biomodel/{id}/biomodel.sbml - Retrieve SBML file content
  • GET /biomodel/{id}/diagram - Get diagram URL
  • GET /biomodel/{id}/diagram/image - Get diagram image
  • GET /biomodel/{id}/applications/files - Get application files

LLM Routes (/llm)

  • POST /query - General LLM query with tool calling
  • POST /analyse/{biomodel_id} - Analyze specific biomodel
  • POST /analyse/{biomodel_id}/vcml - Analyze VCML content
  • POST /analyse/{biomodel_id}/diagram - Analyze diagram

Knowledge Base Routes (/kb)

  • POST /create-collection - Create knowledge base collection
  • GET /files - List all files in knowledge base
  • POST /upload-pdf - Upload PDF file
  • POST /upload-text - Upload text file
  • DELETE /files/{file_name} - Delete file
  • GET /similar - Find similar documents
  • GET /files/{file_name}/chunks - Get file chunks

Qdrant Routes (/qdrant)

  • Direct vector database operations for advanced use cases

Tech Stack

  • Framework: FastAPI 0.115+
  • Language: Python 3.12+
  • Dependency Management: uv
  • Database: Qdrant Vector Database
  • AI/ML: OpenAI API, LangChain
  • File Processing: PyPDF, Markitdown
  • Testing: Pytest with async support
  • Documentation: Auto-generated OpenAPI/Swagger docs

🚀 Quick Start

Prerequisites

  • Python 3.12+
  • uv
  • Docker (for Qdrant)

Installation

  1. Clone and navigate to backend

    cd backend

  2. Install dependencies

    uv sync

  3. Set up environment variables

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

  4. Start the development server

    uv run uvicorn app.main:app --reload

Using Docker

  1. Build the container

    docker build -t vcell-backend .

  2. Run with Docker Compose

    docker-compose up backend

📚 API Documentation

Once the server is running, you can access:

Logging

The application uses structured logging with different levels:

  • DEBUG: Detailed debugging information
  • INFO: General application information
  • WARNING: Warning messages
  • ERROR: Error messages
  • CRITICAL: Critical errors Logs are configured in app/core/logger.py and can be customized via environment variables.