--- 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 embeddings, you need a table with a column defined as the `vector` data type. You must specify the dimensions of the vector (also known as _dimensionality_), which is determined by the embedding model you plan to use (e.g., OpenAI's `text-embedding-3-small` uses 1536 dimensions). For example, the following command creates a table `embeddings` with a 3-dimensional vector column `data`. ```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 distance (`<=>`), and inner product (`<#>`). - `<->`: Euclidean distance (L2). Measues straight-line distance between two vectors. Good for general similarity tasks when magnitude matters. - `<=>`: Cosine distance. Compares the angle between two vectors. Common for text embeddings, where direction matters more than magnitude. - `<#>`: Negative inner product. Used for maximizing similarity. Often used in ranking or recommendation systems. 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; ``` You should see the following output. ``` id | data ----+-------------- 1 | [0.1,0.2,0.3] (1 row) ``` ## Index using HNSW indexes By default, the query above performs a sequential scan of the `embeddings` table, which can be slow for large datasets. To speed up nearest neighbor searches, you can create an approximate nearest neighbor (ANN) index. `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, content TEXT, data VECTOR(512) ); ``` ### How to generate embeddings In most cases, embeddings are created using external services such as [OpenAI](https://platform.openai.com/docs/guides/embeddings) or [Gemini](https://ai.google.dev/gemini-api/docs/embeddings) etc. Once generated, they can be stored in Postgres for vector search with `pgvector`. The dimensionality of the embedding must match the model you use (for example, 512, 768, or 1536 dimensions), ensuring consistency between the stored vectors and the queries you run. For this example, we will use embeddings generated from the [Nomic API](https://docs.nomic.ai/reference/api/embed-text-v-1-embedding-text-post). ### Insert embeddings You can insert embeddings into the `embeddings` table using the following SQL command. ```sql INSERT INTO embeddings (content, data) VALUES ('i like to eat tacos', 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('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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``` The above SQL command inserts two rows into the `embeddings` table: - Row 1: The text "i like to eat tacos" and its corresponding embedding vector. - Row 2: A longer text about embeddings itself: "An embedding is a numerical representation of data in a high-dimensional space that captures semantic relationships and similarities between entities." and its corresponding embedding vector. The embedding vectors are generated using the Nomic API's `nomic-embed-text-v1.5` model with 512 dimensions. 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 the cosine distance operator `<=>`. ```sql SELECT content, data <=> 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as distance FROM embeddings ORDER BY distance ASC ``` You should see the following output: | content | distance | | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------- | | i like to eat tacos | 0.25041233062525814 | | An embedding is a numerical representation of data in a high-dimensional space that captures semantic relationships and similarities between entities. | 0.4526167402866883 | Unsurprisingly, Postgres finds that the embedding for "i like to eat tacos" is the closest to the embedding for "burgers are tasty". ## Resources - [pgvector GitHub Repository](https://github.com/pgvector/pgvector) - [Nomic API Documentation](https://docs.nomic.ai/)