Vector Search for Docs
Semantic document search with citation tracking for enterprise knowledge bases.
Industry
Enterprise (demo)
Timeline
3 weeks
Stack
Python, Pinecone, OpenAI Embeddings, FastAPI, React
Role
AI Engineering + Backend + Search UX
Problem
Support team couldn't find relevant documentation quickly, leading to inconsistent answers and long resolution times for customer queries.
Approach
Built semantic search with document chunking, vector embeddings, and citation tracking. Added admin feedback loop to improve search quality and analytics to track usage patterns.
Results
Key Features
Document upload with auto-chunking preview
Semantic search interface with filters
Search results with relevance scores and citations
Admin feedback system for result quality
Usage analytics and search patterns
Citation tracking and source verification