About This Project
An intelligent Multi-Agent Retrieval-Augmented Generation (RAG) system that combines autonomous AI agents, semantic search, and large language models to deliver accurate, context-aware, and explainable responses from enterprise documents. The platform supports multi-document knowledge retrieval, agent orchestration, intelligent tool usage, citation-based answers, and page-level source verification for reliable AI-assisted decision-making. ✨ Features: Multi-Agent Architecture, Agentic AI Workflow, Retrieval-Augmented Generation (RAG), Semantic Search, Pinecone Vector Database, Namespace-based Knowledge Isolation, Cross-Encoder Re-ranking, Citation-Based Responses, PDF Page Viewer, Multi-PDF Knowledge Base, Context-Aware Q&A, Intelligent Tool Calling, MongoDB Integration, Streamlit Dashboard, Enterprise Document Search, Scalable Vector Indexing, Fast Retrieval, Explainable AI Responses.
Key Highlights
An intelligent Multi-Agent Retrieval-Augmented Generation (RAG) system that combines autonomous AI agents, semantic search, and large language models to deliver accurate, context-aware, and explainable responses from enterprise documents
The platform supports multi-document knowledge retrieval, agent orchestration, intelligent tool usage, citation-based answers, and page-level source verification for reliable AI-assisted decision-making
✨ Features: Multi-Agent Architecture, Agentic AI Workflow, Retrieval-Augmented Generation (RAG), Semantic Search, Pinecone Vector Database, Namespace-based Knowledge Isolation, Cross-Encoder Re-ranking, Citation-Based Responses, PDF Page Viewer, Multi-PDF Knowledge Base, Context-Aware Q&A, Intelligent Tool Calling, MongoDB Integration, Streamlit Dashboard, Enterprise Document Search, Scalable Vector Indexing, Fast Retrieval, Explainable AI Responses.