Semantic Search & Vector DB

Keyword search falls apart in technical domains where meaning matters more than string matching. I build semantic retrieval pipelines that leverage embeddings and FAISS-based vector indexes to surface results aligned with intent, not just text overlap.

My Approach

• FAISS Vector Indexing – Configurable with HNSW or IVF for speed at scale, tuned for both latency and recall.

• Hybrid Retrieval – Combining dense embeddings with keyword/BM25 to handle edge cases where exact terms still matter.

• Metadata Filtering – Queries can be constrained by document type, version, or tags for precision.

• Explainability – Results return with scores, highlighted spans, and source links so users know why they ranked.

Advancing Further

I continue to evolve methodology toward:

• High-Throughput Indexing – scaling to millions of documents with PQ/IVF compression and sharding.

• Real-Time Updates – ingestion pipelines that update indexes continuously as new data arrives.

• Domain-Aware Query Expansion – semantic query rewriting to capture related terminology in specialized fields.

Why It Matters

Generic search tools can’t handle the nuance of specialized knowledge. With semantic search and vector DBs, I deliver context-aware retrieval that feels intuitive to users while maintaining speed, scale, and traceability.