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To use pgvector, install the extension on the PostgreSQL server, enable it in the database where you need it, then create a vector column and an index whose operator class matches your distance metric. The steps below take you from installation to a working nearest-neighbor query.
1. Install pgvector on the PostgreSQL server
Installing pgvector makes the extension available to PostgreSQL; it does not enable it in every database. The project’s README documents source builds and package-manager installation routes including Docker, Homebrew, PGXN, APT, and Yum. Package names and PostgreSQL-version support vary by platform, so choose instructions that match the operating system and PostgreSQL server version you actually run.
The current project README’s source-build example checks out the v0.8.7 branch and runs make followed by make install. The documented source-build support is for Linux and Mac with PostgreSQL 13 or later; installation may require elevated privileges. Check the project instructions for the exact prerequisites and commands for your environment.
If PostgreSQL is managed by a cloud or hosting provider, check that provider’s current documentation for pgvector availability, supported versions, and required permissions. Those details differ by service; the SQL steps below assume the server has the extension installed.
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2. Enable pgvector in the database
Connect to the specific database that will store vectors and run:
CREATE EXTENSION vector;This is database-specific: run the command once in each database where pgvector is needed. Your database role must have enough privilege to create the extension; the required role or grant depends on the PostgreSQL service.
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3. Create a vector column and insert sample data
A vector column declares how many dimensions each vector contains. This small example uses three dimensions only to demonstrate the SQL syntax:
CREATE TABLE items (
id bigserial PRIMARY KEY,
embedding vector(3)
);
INSERT INTO items (embedding)
VALUES ('[1,2,3]'), ('[4,5,6]');Replace 3 with the dimensionality produced by your embedding model or other vector source. Stored vectors and query vectors must match the declared dimension. The sample values are not meaningful production embeddings.
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4. Run an exact nearest-neighbor query
Before adding an approximate index, try a query against the table. This example orders rows by L2 distance from the supplied vector and returns at most five:
SELECT *
FROM items
ORDER BY embedding <-> '[3,1,2]'
LIMIT 5;pgvector provides several distance operators. Choose the one that matches the comparison your application needs:
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<->: L2 (Euclidean) distance.<#>: negative inner product. It is negative so PostgreSQL can use ascending-order index scans; multiply the returned value by-1if you need the positive inner product.<=>: cosine distance.<+>: L1 (Manhattan) distance.
By default, pgvector performs exact nearest-neighbor search, which provides perfect recall. Exact search is a useful baseline: an approximate index can change which rows appear among the nearest results.
5. Create an index for your distance metric
For L2 distance, create an HNSW index with the L2 operator class:
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CREATE INDEX ON items USING hnsw (embedding vector_l2_ops);Use an operator class that matches the distance operator in your query. For cosine distance use vector_cosine_ops; for inner product use vector_ip_ops. For example, a cosine-distance query should order by embedding <=> query_vector and use an index on embedding vector_cosine_ops.
For production, the project documentation recommends adding indexes after initial bulk loading for best performance and creating them concurrently to avoid blocking writes. Concurrent index creation uses PostgreSQL’s CREATE INDEX CONCURRENTLY form; follow PostgreSQL’s restrictions for that command when choosing where and how to run it.
Choose between HNSW and IVFFlat
Both HNSW and IVFFlat are approximate nearest-neighbor indexes: they can improve query speed but trade away some recall compared with exact search. The following are qualitative tradeoffs described by the pgvector project documentation, not independent benchmark results.
| Consideration | HNSW | IVFFlat |
|---|---|---|
| Speed and recall tradeoff | Better query performance in the project’s speed-recall comparison. | Lower query performance in the project’s speed-recall comparison. |
| Build and memory | Slower index build and more memory. | Faster index build and less memory. |
| When to build | Can be created before the table contains data. | Build after the table has some data for good recall. |
| Index form | CREATE INDEX ON items USING hnsw (embedding vector_l2_ops); |
CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = ...); |
IVFFlat starting points
The project README suggests these initial values for the IVFFlat lists setting: use approximately rows / 1000 lists for tables up to one million rows, and sqrt(rows) for larger tables. Start with approximately sqrt(lists) probes. These are tuning starting points, not universal optimal settings. More probes favor recall over speed; measure the tradeoff with your own data and workload.
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Account for filters in approximate searches
With an approximate index, a selective WHERE filter is applied after the index scan. As a result, the query may return fewer matching rows than the requested LIMIT. The project documentation describes iterative index scans as one way to address this, and also points to ordinary indexes on filter columns, partial indexes, or partitioning as alternatives depending on the workload. Choose based on your filter patterns and validate the result count and recall you need.
Quick Recap
Quick setup checklist
- Install pgvector for the PostgreSQL server and version you use.
- Run
CREATE EXTENSION vector;in every database that needs the extension. - Set
vector(n)to the actual vector dimension, and use vectors of that same dimension in inserts and queries. - Test the distance-ordered query before adding an approximate index.
- Match the index operator class to the query’s distance operator.
- Evaluate approximate-search recall, filtering behavior, and tuning against your workload.
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