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AI Business Assistant — RAG Prototype

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Architected a Retrieval-Augmented Generation (RAG) system using Django and LangChain to enable semantic search over business documents.

PythonDjangoLangChainRAG

Overview


This project involved building a production-grade RAG system that enables semantic search over business documents using natural language queries.


Key Features


  • **Semantic Search**: Leveraged LangChain chains and retrievers for intelligent document retrieval
  • **Vector Storage**: Implemented Chroma vector store for efficient similarity search
  • **Human-in-the-Loop**: Designed approval workflows for AI-generated actions
  • **Safety First**: Implemented output validation, confidence scoring, and prompt versioning to mitigate hallucinations

    • Tech Stack


  • Django / Python
  • LangChain (Chains, Retrievers)
  • Chroma Vector Database
  • OpenAI API
  • Prompt Engineering & Guardrails