RAG-Powered Knowledge Assistant
Retrieval-Augmented Generation system providing instant, accurate answers from company documentation.
Client: Tech Startup
Industry: Software Development
Duration: 6 weeks
Team: 2 engineers
Problem
Employees spent hours searching through scattered documentation, reducing productivity and increasing frustration.
Solution
Developed a sophisticated RAG pipeline that ingests documents from multiple sources, creates semantic embeddings, implements hybrid search, and provides citations with confidence scores.
Approach
- Built document ingestion pipeline for multiple file formats
- Implemented hybrid retrieval using semantic and keyword search
- Added citation tracking and confidence scoring
- Created feedback loop for continuous improvement
Stack
PythonFastAPISentence TransformersPineconeLangChainStreamlit
Results
- 80% search time reduction
- 95% accuracy rate
- 4.8/5 satisfaction