AI-Powered Knowledge Base System
RAG-based knowledge retrieval system using LangChain, OpenAI, and Pinecone. Ingests enterprise documents and serves accurate, cited answers at scale — with sub-500ms response times.
The Challenge
What needed to be solved.
An enterprise client had thousands of internal documents — SOPs, policies, technical manuals — scattered across SharePoint, Google Drive, and legacy systems. Employees spent an average of 45 minutes per query finding the right information, leading to productivity losses and inconsistent answers across teams.
The Approach
How I built the solution.
Designed a RAG (Retrieval-Augmented Generation) pipeline using LangChain for document ingestion and chunking, OpenAI embeddings for semantic search, and Pinecone as the vector store. Built a FastAPI backend with intelligent caching and a React frontend with a conversational interface. Implemented citation tracking so every answer links back to the source document and page.
Technology Stack
Tools chosen with intent.
Results
Measurable outcomes delivered.
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