Project brief
A document-aware conversational AI system built for privacy-sensitive deployments where data cannot leave owned infrastructure. It combines retrieval-augmented generation, FAISS vector search, and a locally-deployed LLM stack to produce grounded responses from organizational documents without relying on external API services.
Problem
Generic LLM chatbots hallucinate, lack proprietary context, and become untrustworthy in enterprise settings where both accuracy and data sovereignty matter. Cloud-hosted AI APIs are off the table when the documents contain sensitive information — but most self-hosted alternatives sacrifice too much on response quality.
Solution
The system preprocesses documents into a FAISS vector index, retrieves contextually relevant chunks at query time, and conditions LLM responses through a LangChain pipeline. The entire stack runs on self-managed infrastructure using Docker, giving operators control over data, model choice, and retrieval behavior. A Flask API exposes the assistant to consuming applications.
Role
Full system design and implementation: document preprocessing and chunking strategy, vector embedding pipeline, FAISS index management, LangChain retrieval orchestration, LLM parameter tuning for accuracy, Flask API design, Docker containerization, and self-hosted deployment.
Challenge
Retrieval quality across varied document formats is the hardest problem — chunk boundaries, embedding model choice, and query formulation all interact to determine whether the LLM gets useful context. Reducing hallucination while keeping response latency acceptable on local hardware required extensive tuning of both the retrieval and generation stages.