ToheedAsghar/DocForge
📦 Open Source ProjectToheedAsghar
A multi-agent RAG pipeline that uses collaborative verification to eliminate hallucinations and ensure answer accuracy.
DocForge represents a shift from simple RAG implementations to a multi-agent verification architecture. At its core, the project utilizes LangGraph to manage a stateful workflow where four distinct agents collaborate to process information. The pipeline begins with a routing agent that determines the optimal retrieval strategy, followed by a retrieval agent that interacts with Pinecone vector databases. Once documents are fetched, a synthesis agent generates the response, and a final 'critic' agent performs an automated hallucination check to verify the factual grounding of the generated content against the retrieved context.
Technically, the project is built on Python and integrates seamlessly with the LangChain ecosystem. It is designed for modularity, allowing developers to swap out retrieval strategies or LLM backends while maintaining the core verification loop. By treating the RAG process as a multi-step collaborative task rather than a linear chain, DocForge significantly reduces the risk of 'lazy' or inaccurate AI responses, making it a powerful tool for enterprise-grade documentation systems.
💡Highlights
- ├─4-agent collaborative workflow
- ├─Automated hallucination detection
- └─Built with LangGraph and Pinecone
🎯For
- ├─AI Engineers
- └─RAG Developers