RASS
Retrieval-Augmented Semantic Search platform for enterprise documents
Retrieval-Augmented Semantic Search (RASS)
| _Medical Informatics Engineering | Summer 2025_ |
Built a containerized RAG platform for semantic, citation-backed search over long-form enterprise documents. The system emphasizes evaluation as a first-class requirement, with automated quality gates for grounding and relevance integrated into the deployment pipeline.
Key Contributions
- Designed and implemented a full RAG pipeline with semantic search capabilities
- Integrated automated evaluation using RAGAS and TruLens frameworks
- Built quality gates for grounding accuracy and answer relevance
- Containerized deployment for production environments
- Connected system to real user workflows in medical informatics context
Technical Stack
Python, LangChain, Vector Databases, Docker, RAGAS, TruLens, Enterprise Search
Impact
- Enabled citation-backed semantic search across thousands of pages of technical documentation
- Established evaluation-first approach to ensure answer quality before deployment
- Provided measurable quality metrics for stakeholder confidence