Building Enterprise Semantic Search with AI: Beyond Keyword Matching
Hybrid search, reranking, and personalization for intelligent enterprise knowledge systems
Building Enterprise Semantic Search with AI: Beyond Keyword Matching
Hybrid search, reranking, and personalization for intelligent enterprise knowledge systems
Design and implement enterprise semantic search systems that combine vector embeddings, BM25 keyword search, and LLM reranking for accurate, fast, and contextually relevant results.
Enterprise semantic search goes far beyond keyword matching to understand user intent. Architecture: 1) Dual retrieval: BM25 for exact keyword matches (fast, reliable for specific terms) + dense vector search for semantic similarity (finds conceptually related content). Combine with Reciprocal Rank Fusion (RRF) for merged result ranking. 2) Reranking: use CrossEncoder models (ms-marco-MiniLM-L-6-v2) or Cohere Rerank API to reorder top-50 results - improves precision significantly at small latency cost. 3) Query expansion: use LLM to generate alternative query formulations to improve recall. 4) Personalization layer: adjust ranking based on user role, department, and past interactions. 5) Chunking strategy matters: sentence-level chunks for Q&A, section-level for document retrieval, full documents for summarization. Production: index with HNSW for sub-millisecond vector search, implement query result caching for common searches, monitor click-through rates as implicit feedback.
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