RAG & Knowledge
seekdb
seekdb — the AI-native search database unifying vector, text and structured data for agents.
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View guide →Vector similarity search inside Postgres - store embeddings next to your data
pgvector is an open-source rag & knowledge project by pgvector (pgvector/pgvector on GitHub). It has 23,276 GitHub stars and 1,341 forks, is released under the NOASSERTION license, and was last updated on GitHub on 8 October 2026.
Plan a Postgres table with a vector column sized to your embedding model's dimensions.
Clone the repository (or use a Postgres image or managed service that ships it).
Compile the extension, install it into Postgres, and run CREATE EXTENSION vector in your database.
Insert a few embeddings, add an index, and query nearest neighbors with the distance operator.
Run exact nearest neighbor queries or speed them up with approximate indexing as your dataset grows.
Build HNSW indexes for a strong speed-recall tradeoff on large vector collections.
Use IVFFlat indexes when you want faster build times and lower memory use than HNSW.
Choose from L2, inner product, cosine, L1, Hamming and Jaccard distances per query.
Store single-precision, half-precision, binary and sparse vectors to match your memory budget.
Combine vector search with SQL filters, JOINs, transactions and any Postgres client language.
Get the source code from GitHub and enter the project directory.
git clone https://github.com/pgvector/pgvector.git
cd pgvectorCompile pgvector against your Postgres installation (the README pins the current release branch).
make
make install # may need sudoCreate the extension in your database, then store and search vectors with plain SQL.
CREATE EXTENSION vector;
-- then create a table with a vector column and query with the <-> operatorIt is maintained by the pgvector open-source project, led by Andrew Kane, on GitHub.
Yes - pgvector is open source. GitHub lists its license as NOASSERTION, so review the LICENSE file in the repository for the exact terms.
No. pgvector itself needs no API key; you only need a way to generate embeddings, which can be a local model or a provider API.
Unlike standalone vector databases such as Qdrant or Milvus, pgvector keeps vectors inside Postgres, so you get transactions, JOINs and one less system to operate - a good default when you already run Postgres.
Clone the repo, build and install the extension, run CREATE EXTENSION vector, then follow the README's Getting Started section to store and query your first vectors.
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