Pinecone
Managed vector database for AI applications
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Discover top open-source software, updated regularly with real-world adoption signals.
Vector similarity search integrated directly into PostgreSQL
Add vector columns to PostgreSQL tables and run exact or approximate nearest‑neighbor queries with L2, cosine, inner product, Hamming and more, using familiar SQL.
pgvector extends PostgreSQL with a native vector data type, letting you store embeddings directly alongside relational data. You can perform exact nearest‑neighbor searches or create HNSW/IVFFlat indexes for fast approximate queries, choosing from L2, inner product, cosine, L1, Hamming, and Jaccard distances—all through standard SQL.
The extension is ideal for developers and data teams already using PostgreSQL who need vector search without managing a separate service. Installation works on Linux, macOS, Windows, Docker, Homebrew, APT, Yum, and conda‑forge, and the extension can be enabled per‑database with . Once installed, you create a column, insert embeddings, and query with operators like for cosine similarity. Indexes are built with familiar syntax, and you can tune HNSW parameters (, , ) to balance recall and latency.
CREATE EXTENSION vectorvectorfor L2 distance orCREATE INDEXmef_constructionhnsw.ef_searchWhen teams consider pgvector, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Semantic product search
Store product embeddings alongside catalog data and retrieve similar items with a single SQL query.
Recommendation engine for media
Join user interaction tables with embedding vectors to compute nearest neighbors for personalized suggestions.
Anomaly detection on time‑series embeddings
Insert vectorized features and query for outliers using distance thresholds directly in PostgreSQL.
Geospatial‑like similarity for text documents
Use cosine distance on sentence embeddings to find related documents without external services.
No, it is a PostgreSQL extension; queries run via standard client libraries.
HNSW for fast approximate search and IVFFlat for a trade‑off between build time and query speed.
Yes, pgvector supports `bit` (binary) and `sparsevec` types with up to 64k dimensions and 1k non‑zero elements respectively.
Adjust HNSW parameters `m`, `ef_construction`, and the session variable `hnsw.ef_search` to balance accuracy and latency.
Many hosted providers pre‑install the extension; otherwise you can add it via Docker, Homebrew, APT, Yum, or conda‑forge.
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