Dynamic RAG

A sophisticated Retrieval-Augmented Generation system with multi-agent architecture for intelligent question-answering over custom knowledge bases.

Features

• Multi-Agent Architecture — Specialized agents working together:
  • QA Agent — Main agent for answering questions using the knowledge base
  • Personal Agent — For personal/general questions not requiring RAG
  • Validation Agent — Classifies queries as on-context, personal, bad, or unknown
  • Prompt Agent — Generates diverse query perspectives for comprehensive retrieval
  • Reranking Agent — Reorders retrieved chunks for better relevance
• Hybrid Search — Combines FAISS vector similarity with keyword matching
• Streaming Responses — Token-by-token streaming of LLM outputs
• Multi-language Support — Uses multilingual embeddings via Cohere
• Document Processing — PDF parsing with OCR support via Mistral

Technical Highlights

• LangChain + LangGraph for orchestrating the RAG pipeline
• FAISS vector store for similarity search
• Cohere embeddings for multilingual support
• SQLite for session persistence and analytics
• Configurable agents via JSON configuration files

Tech Stack

Architecture

ValidationAgent → classifies query type
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PromptAgent → generates specialized prompts
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QA Agent → retrieves chunks, reranks, generates answer
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StreamingResponse