README.md

metadata

title: RAG Research Assistant API
emoji: 📚
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
pinned: false

RAG Research Assistant API

This backend provides a sophisticated FastAPI-based service for a Retrieval Augmented Generation (RAG) system designed to assist with research paper searches and information retrieval.

Features

• ArXiv paper search with customizable filtering
• Document chunking and processing
• Embedding generation using Sentence Transformers
• Vector search using FAISS
• LLM response generation with multiple model options
• Markdown-formatted research results

Requirements

• Python 3.8+
• FastAPI
• Sentence Transformers
• FAISS
• Hugging Face API access

Installation

1. Clone the repository
2. Navigate to the backend directory
3. Install dependencies:

pip install -r requirements.txt

4. Create a .env file based on .env.example and add your Hugging Face API key

Running the Application

To run the development server:

python run.py

Or with uvicorn directly:

uvicorn app.main:app --reload

The API will be available at http://localhost:8000, and the API documentation at http://localhost:8000/docs.

API Endpoints

• /rag/query: Process a query through the RAG pipeline
• /rag/search: Search for papers without LLM processing
• /rag/models: Get available LLM models
• /rag/stats: Get system statistics
• /rag/clear/cache: Clear the paper cache
• /rag/clear/database: Clear the vector database
• /health: Simple health check endpoint

Docker

You can also run the application using Docker:

docker build -t rag-backend .
docker run -p 8000:8000 -e HF_API_KEY=your_key_here rag-backend

Architecture

The application follows a service-oriented architecture:

• ArxivService: Interface to ArXiv API
• DocumentService: Process papers into chunks
• EmbeddingService: Generate embeddings
• VectorService: Store and search vectors
• LlmService: Generate responses using LLMs
• FormatterService: Format results
• RagService: Orchestrate the entire RAG pipeline