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AI RAG Pinecone LangChain

MEDBOT RAG: CLINICAL COMPLIANCE AGENT

Contextual Q&A agent for medical practitioners built using LangChain and pinecone vector databases.

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MedBot RAG queries complex healthcare documents, providing doctors with answers backed by medical policy citations.

"By utilizing semantic vector lookups, MedBot avoids LLM hallucination, linking answers directly to indexed pages of guidelines."

RAG Architecture

Text files are chunked using recursive token splitters, embedded using OpenAI text-embedding-3-large, and stored in Pinecone. The search API processes query inputs to retrieve relevant chunks, feeding them into GPT-4o with strict reference formatting directives.

System Architecture

[PRACTITIONER WEB APP]
React.js Chat view / Document PDF Citation panel overlays
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[LANGCHAIN RETRIEVAL CONTROLLER]
Python FastAPI / Semantic Hybrid Search Router / OpenAI Embeddings
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[VECTOR RESOURCE INDEX]
Pinecone Database (Clinical guidelines metadata & sparse-dense vectors)