AI Automation Project
A retrieval-augmented document Q&A system with vector embeddings and semantic search over a custom knowledge base.
Production-ready RAG pipeline letting users query a custom document knowledge base in natural language. The system chunks, embeds, and stores documents in a vector database, then uses semantic retrieval to feed context to an LLM for accurate, grounded answers.
LLMs hallucinate when answering about proprietary or recent documents. Fine-tuning is expensive and slow to update. The goal was a low-cost, fast-to-update system that keeps the LLM grounded in source documents.
n8n orchestrates the ingestion pipeline — documents are chunked, embedded via Hugging Face sentence transformers, and stored in Supabase pgvector. At query time, top-k relevant chunks are retrieved by cosine similarity, passed as context to the LLM, and source citations are returned with the answer.
I'm available for full-time startup roles and select freelance projects.