The Problem
Traditional keyword-based search struggles with semantic meaning and user intent.
The Solution
Implemented a dense retrieval system using state-of-the-art embedding models and Elasticsearch/Weaviate.
Architecture
FastAPI backend, Sentence Transformers, Weaviate vector DB, React frontend with real-time updates.
Technology Stack
PythonFastAPISentence TransformersWeaviateReactTypeScript
Outcomes & Impact
Achieved 95% relevance precision. Reduced search latency by 40% vs traditional methods.
5,000+
Queries/sec
10M documents
Index Size
<100ms
Latency
Lessons Learned
Learned the importance of embedding model selection and the value of proper vector DB indexing.