Agentic RAG Conversational AI System

An agentic, retrieval-augmented conversational AI system designed for context-aware, multi-turn interactions over domain-specific (medical) documents. The system combines persistent conversational memory, vector-based retrieval, and LLM reasoning to deliver grounded, reliable responses.

Overview

This project implements an end-to-end Retrieval-Augmented Generation (RAG) architecture with agentic orchestration. It supports stateful conversations, retrieves relevant medical context, and generates accurate responses using large language models.

Architecture

High-level flow:

1. User query ingestion
2. Conversational memory retrieval from persistent storage
3. Context retrieval from FAISS vector index
4. Agentic orchestration via LangChain
5. Response generation using Azure OpenAI
6. Memory update and persistence

Tech Stack

• Azure OpenAI (LLM)
• LangChain (agent orchestration and RAG)
• FAISS (vector database)
• Azure Cosmos DB (conversational memory)
• Python (backend)

Key Features

• Agentic, multi-turn conversational flow
• Retrieval-Augmented Generation (RAG) for grounded responses
• Persistent conversational memory across sessions
• Vector search over medical notes
• Modular, production-oriented architecture

How It Works

• Medical notes are embedded and indexed into FAISS.
• User queries are enriched with past conversation context from Cosmos DB.
• Relevant documents are retrieved and passed to the LLM.
• Azure OpenAI generates grounded responses.
• Conversation state is persisted for future turns.

Use Cases

• Healthcare conversational assistants
• Clinical knowledge retrieval
• Domain-specific RAG applications
• Enterprise AI assistants

Future Enhancements

• Multi-agent planning and tool routing
• Reranking and hybrid retrieval
• Streaming responses
• Evaluation and grounding metrics
• Secure access control