agent-brain-rag 1.1.0

RAG-based document indexing and semantic search server for AI agents

Doc-Serve Server

RAG-based document indexing and semantic search REST API service.

Installation

pip install doc-serve

Quick Start

1. Set environment variables:

   export OPENAI_API_KEY=your-key
   export ANTHROPIC_API_KEY=your-key
   

2. Start the server:

   doc-serve
   

The server will start at http://127.0.0.1:8000.

Features

• Document Indexing: Load and index documents from folders (PDF, Markdown, TXT, DOCX, HTML)
• Context-Aware Chunking: Smart text splitting with configurable chunk sizes and overlap
• Semantic Search: Query indexed documents using natural language
• OpenAI Embeddings: Uses text-embedding-3-large for high-quality embeddings
• Chroma Vector Store: Persistent, thread-safe vector database
• FastAPI: Modern, high-performance REST API with OpenAPI documentation

Quick Start

Prerequisites

• Python 3.10+
• Poetry
• OpenAI API key

Installation

cd doc-serve-server
poetry install

Configuration

Copy the environment template and configure:

cp ../.env.example .env
# Edit .env with your API keys

Required environment variables:

• OPENAI_API_KEY: Your OpenAI API key for embeddings

Running the Server

# Development mode
poetry run uvicorn doc_serve_server.api.main:app --reload

# Or use the entry point
poetry run doc-serve

The server will start at http://127.0.0.1:8000.

API Documentation

Once running, visit:

• Swagger UI: http://127.0.0.1:8000/docs
• ReDoc: http://127.0.0.1:8000/redoc
• OpenAPI JSON: http://127.0.0.1:8000/openapi.json

API Endpoints

Health

• GET /health - Server health status
• GET /health/status - Detailed indexing status

Indexing

• POST /index - Start indexing documents from a folder
• POST /index/add - Add documents to existing index
• DELETE /index - Reset the index

Querying

• POST /query - Semantic search query
• GET /query/count - Get indexed document count

Example Usage

Index Documents

curl -X POST http://localhost:8000/index \
  -H "Content-Type: application/json" \
  -d '{"folder_path": "/path/to/docs"}'

Query Documents

curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"query": "How do I configure authentication?", "top_k": 5}'

Architecture

doc_serve_server/
├── api/
│   ├── main.py           # FastAPI application
│   └── routers/          # Endpoint handlers
├── config/
│   └── settings.py       # Configuration management
├── models/               # Pydantic request/response models
├── indexing/
│   ├── document_loader.py  # Document loading
│   ├── chunking.py         # Text chunking
│   └── embedding.py        # Embedding generation
├── services/
│   ├── indexing_service.py # Indexing orchestration
│   └── query_service.py    # Query execution
└── storage/
    └── vector_store.py     # Chroma vector store

Development

Running Tests

poetry run pytest

Code Formatting

poetry run black doc_serve_server/
poetry run ruff check doc_serve_server/

Type Checking

poetry run mypy doc_serve_server/

License

MIT