Implement Efficient Similarity Search

Provides four production vector-database implementations with matching Python client classes: Pinecone (create-index-if-missing, batched upsert, top-k search with metadata filtering, and a search-with-rerank method that over-fetches then reorders results with a sentence-transformers cross-encoder), Qdrant (collection creation with scalar quantization for memory efficiency, upsert, filtered search with must/should/must-not conditions, and a named-vector hybrid search combining dense and sparse vectors), pgvector on PostgreSQL (async pool setup that enables the vector extension and an HNSW index tuned with m and ef_construction, cosine-distance search via the <=> operator, and a hybrid search that blends vector similarity with Postgres full-text ts_rank scoring in one weighted query), and Weaviate (schema creation with the vectorizer disabled so embeddings are supplied externally, batched upsert, near-vector search, and a with_hybrid search that blends BM25 and vector scores via an alpha parameter). Explains the underlying distance metrics - cosine, Euclidean or L2, dot product, and Manhattan or L1 - and their best-fit use cases, and the three index-type tradeoffs: flat exact search at O(n) with 100% recall for small data, HNSW graph-based search at roughly O(log n) with 95-99% recall for medium-to-large data, and IVF+PQ quantized search at roughly O(sqrt n) with 90-95% recall for very large data.

Use it when building semantic search systems, implementing RAG retrieval, creating recommendation engines, optimizing search latency, scaling to millions of vectors, or combining semantic and keyword search. Skip it for anything unrelated to similarity search or outside this scope.

Input is the target vector store - Pinecone, Qdrant, pgvector, or Weaviate - and the operation needed: index setup, upsert, filtered search, or hybrid search; output is the matching client class method, ready to adapt into a retrieval pipeline, plus the distance-metric and index-type guidance to choose the right configuration for the data size and recall target.

Pinecone via the pinecone Python SDK with ServerlessSpec; Qdrant via qdrant-client with scalar quantization; PostgreSQL via asyncpg and the pgvector extension; Weaviate via the weaviate-client. Reranking uses sentence-transformers' CrossEncoder (cross-encoder/ms-marco-MiniLM-L-6-v2). Links to the Pinecone, Qdrant, pgvector, and Weaviate documentation.

Engineers building semantic search, RAG retrieval, or recommendation systems who need working, store-specific upsert and search code rather than an abstract vector-database comparison. Also useful when deciding between stores: the pgvector implementation shows how far a Postgres-native approach with a tuned HNSW index and combined full-text ranking can go without adding a dedicated vector-database service, while the Pinecone and Qdrant implementations show what a managed or self-hosted dedicated store adds - quantization, richer filter DSLs, and built-in reranking hooks.