--- title: Vector Search in Postgres subtitle: A step-by-step guide describing how to use pgvector for vector search in Postgres author: vkarpov15 enableTableOfContents: true createdAt: '2025-02-04T13:24:36.612Z' updatedOn: '2025-02-04T13:24:36.612Z' --- Vector search enables you to perform similarity searches on vectors stored in Postgres. With the [pgvector](https://github.com/pgvector/pgvector) extension, you can store and efficiently query vector embeddings, making Postgres a viable option for AI-driven applications like retrieval-augmented generation (RAG) and semantic search. ## Steps - Install and enable pgvector - Create a table with a vector column - Insert and retrieve vector data - Perform nearest neighbor searches - Index using HNSW indexes - Insert and retrieve embeddings ## Install and enable pgvector Before using vector search, you need to install the [`pgvector` extension](https://github.com/pgvector/pgvector). The `pgvector` extension adds a `vector` data type, operators for similarity search (`<->`, `<#>`, `<=>`) , and support for ANN indexes. In Neon, `pgvector` is already installed, you just need to enable it using the following command. ```sql CREATE EXTENSION IF NOT EXISTS vector; ``` ## Create a table with a vector column To store vector embeddings, create a table with a vector column. You must specify the size (also known as _dimensionality_) of the vectors when defining the column. ```sql CREATE TABLE embeddings ( id SERIAL PRIMARY KEY, data VECTOR(3) -- 3-dimensional vector example ); ``` ## Insert and retrieve vector data You can insert vectors as arrays using the following command. Under the hood, vectors are just fixed-length arrays of floats. ```sql INSERT INTO embeddings (data) VALUES ('[0.1, 0.2, 0.3]'), ('[0.5, 0.1, 0.8]'); ``` You can retrieve all stored vectors using the following command. ```sql SELECT * FROM embeddings; ``` ## Perform nearest neighbor searches Vector search typically means finding the closest vectors in the database to a given vector. There are different distance metrics to calculate which vector is closest, like Euclidean distance (`<->`), cosine similarity (`<#>`), and inner product (`<=>`). For example, the following command runs nearest neighbor search to find the most similar vector to `[0.2, 0.1, 0.3]` using Euclidean distance, which is `[0.1, 0.2, 0.3]`. ```sql SELECT * FROM embeddings ORDER BY data <-> '[0.2, 0.1, 0.3]' LIMIT 1; ``` ## Index using HNSW indexes For large datasets, exact nearest neighbor search can be slow. `pgvector` supports two different indexes for nearest neighbor search: HNSW and IVFFlat. The following command creates a HNSW index. ```sql CREATE INDEX ON embeddings USING hnsw (data); ``` ## Insert and retrieve embeddings Vector databases are typically used to store _embeddings_. An embedding is a numerical representation of data in a high-dimensional space that captures semantic relationships and similarities between entities. First, run the following command to recreate the `embeddings` table to store vectors with dimensionality 512. ```sql DROP TABLE embeddings; CREATE TABLE embeddings ( id SERIAL PRIMARY KEY, data VECTOR(512) ); ``` For example, the following command inserts a pair of 512 dimensionality vectors containing text embeddings pulled from the [Nomic API](https://docs.nomic.ai/reference/api/embed-text-v-1-embedding-text-post). The first embedding represents the string "i like to eat tacos", the second represents the string "An embedding is a numerical representation of data in a high-dimensional space that captures semantic relationships and similarities between entities." ```sql INSERT INTO embeddings (data) VALUES /* Embedding representation of "i like to eat tacos" */ 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/* Embedding representation of "An embedding is a numerical representation of data in a high-dimensional space that captures semantic relationships and similarities between entities." */ 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``` You can then query for which embeddings are closest to a new vector. For example, the following query finds the closest vector to the embedding for "burgers are tasty" using cosine similarity `<#>`. Unsurprisingly, Postgres returns the "i like to eat tacos" vector. ```sql SELECT * FROM embeddings ORDER BY data <#> 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LIMIT 1; ```