similarity-search-patterns

To build a similarity search pipeline for RAG retrieval, you select an appropriate distance metric, tune the index type, and apply batch upserts with filtered queries to find semantically related content. You can then rerank the retrieval results to improve relevance.

Hybrid search combines semantic vector search with traditional keyword search to improve result accuracy. You should use hybrid retrieval when you need to match both the semantic meaning and specific metadata or keyword terms, often adding metadata filters and reranking for better relevance.

Tuning HNSW index parameters involves selecting the right index type and distance metric for your workload to balance search latency and accuracy. You adjust the index settings within your vector database deployment to achieve your specific latency-aware production patterns.

Yes, these similarity search patterns include production implementations that adapt to Pinecone, Qdrant, pgvector, and Weaviate deployments. You can adapt the templates to your specific data, latency targets, and filtering needs across these supported vector stores.

The best way to improve semantic search relevance is to apply a reranking step after the initial vector retrieval. Reranking processes the top retrieved results to reorder them based on more accurate relevance signals, combining hybrid keyword and vector scores with metadata filters.

Slow vector search query latency often results from unoptimized index tuning, improper distance metric selection, or missing latency-aware production patterns. You can resolve this by applying batch upserts, tuning your index type, and using filtered queries to narrow the search space.