Document RAG System

A cloud-based document processing and query system using RAG (Retrieval-Augmented Generation) with chain-of-thought reasoning.

Step-by-Step Project Plan

1. Set Up LangChain Integration

• Research and familiarize yourself with LangChain documentation.
• Create a LangChain account (if required for their API or services).
• Install the LangChain library in your backend environment using pip install langchain.
• Explore LangChain's vector embeddings documentation to understand supported models and embeddings.
• Implement vector embedding generation for uploaded documents.
• Test embedding generation with sample documents.

2. Backend Development (Python)

Document Handling

• Parse uploaded documents into structured formats (e.g., extracting text from PDFs or Word documents).
• Handle errors for unsupported file types or corrupt files.

Vector Storage

• Set up a vector database (e.g., Azure Cosmos DB or FAISS) for storing embeddings.
• Integrate LangChain-generated embeddings into the vector database.
• Test vector storage and retrieval with simple queries.

Retrieval-Augmented Generation (RAG)

• Implement RAG pipeline to fetch relevant documents or passages based on a query.
• Add chain-of-thought reasoning to decompose complex queries.
• Implement language detection to support multilingual queries.
• Build intent classification for better query handling.
• Ensure response generation includes citations.
• Add error handling and logging.

3. Frontend Development (React)

Query Submission Interface

• Create a chat-like interface for users to submit queries.
• Display responses with citations in a user-friendly format.
• Add loading states and error notifications.

Integration

• Connect the frontend with the backend API.
• Validate data formats between frontend and backend.

4. Performance Optimization

• Optimize vector search performance using FAISS or LangChain utilities.
• Implement batch processing for document uploads.
• Reduce latency for embedding generation and query responses.

5. Testing and QA

Backend

• Write unit tests for each backend component.
• Test document parsing, embedding generation, and RAG pipeline.

End-to-End

• Test system with various document types and sizes.
• Conduct load testing for concurrent user scenarios.
• Evaluate accuracy and relevance of RAG responses.

6. Documentation

• Write API documentation to describe available endpoints and usage.
• Provide usage examples and a quick start guide.
• Include performance benchmarks to showcase capabilities.