Legit-RAG

A modular Retrieval-Augmented Generation (RAG) system built with FastAPI, Qdrant, and OpenAI.

Workflow Components

The system follows a 5-step RAG workflow:

1. Query Routing (router.py)

   • Determines if a query can be answered (ANSWER), needs clarification (CLARIFY), or should be rejected (REJECT)
   • Uses LLM to make intelligent routing decisions
   • Extensible through BaseRequestRouter interface

2. Query Reformulation (reformulator.py)

   • Refines the original query for better retrieval
   • Extracts keywords for hybrid search
   • Implements BaseQueryReformulator for custom reformulation strategies

3. Context Retrieval (retriever.py)

   • Performs hybrid search combining:
     • Semantic search using embeddings
     • Keyword-based search
   • Currently uses Qdrant for vector storage
   • Extensible through BaseRetriever interface

4. Completion Check (completion_checker.py)

   • Evaluates if retrieved context is sufficient to answer the query
   • Returns confidence score
   • Customizable threshold through configuration
   • Implements BaseCompletionChecker interface

5. Answer Generation (answer_generator.py)

   • Generates final response using retrieved context
   • Includes relevant citations
   • Provides confidence scoring
   • Extensible through BaseAnswerGenerator interface

Extensibility

The system is designed for easy extension and modification:

1. LLM Providers

   • Currently uses OpenAI
   • Can be extended to support other providers (Anthropic, Bedrock, etc.)
   • Each component uses abstract base classes for provider independence

2. Vector Databases

   • Currently implements Qdrant
   • Can be extended to support other vector DBs (Pinecone, Weaviate, etc.)
   • Abstract BaseRetriever interface for new implementations

3. Document Management

   • Flexible document model with metadata support
   • Extensible for different document types and sources

4. Search Strategies

   • Hybrid search combining semantic and keyword approaches
   • Customizable result merging strategies
   • Extensible for additional search methods

Setup and Installation

Prerequisites

• Python 3.10+
• Docker and Docker Compose
• OpenAI API key

Setup Steps

1. Clone the repository:

git clone https://github.com/yourusername/legit-rag.git
cd legit-rag

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Create .env file:

cp .env.example .env

5. Edit .env and add your OpenAI API key:

OPENAI_API_KEY=your-key-here

Running the System

1. Start Qdrant vector database:

docker-compose up -d

2. Start the API server:

python -m src.api

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

API Endpoints

Add Documents

POST /documents
{
    "documents": [
        {
            "text": "Your document text here",
            "metadata": {"source": "wiki", "topic": "example"}
        }
    ]
}

Query

POST /query
{
    "query": "Your question here"
}

Example Usage

import requests

# Add documents
docs = {
    "documents": [
        {
            "text": "Example document text",
            "metadata": {"source": "example"}
        }
    ]
}
response = requests.post("http://localhost:8000/documents", json=docs)

# Query
query = {
    "query": "What does the document say?"
}
response = requests.post("http://localhost:8000/query", json=query)
print(response.json())

API Documentation

Once the server is running, you can access the API documentation at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Configuration

Key configuration options in config.py:

• LLM models for each component
• Vector DB settings
• Completion threshold
• API endpoints and ports

Future Enhancements

1. Provider-agnostic LLM interface
2. Support for streaming responses
3. Additional vector database implementations
4. Enhanced document preprocessing
5. Caching layer for frequent queries
6. Batch document processing
7. Advanced result ranking strategies