base on A complement to pgvector for high performance, cost efficient vector search on large workloads. <p></p> <div align=center> # pgvectorscale <h3>pgvectorscale builds on pgvector with higher performance embedding search and cost-efficient storage for AI applications. </h3> [](https://discord.gg/KRdHVXAmkp) [](https://tsdb.co/gh-pgvector-signup) </div> pgvectorscale complements [pgvector][pgvector], the open-source vector data extension for PostgreSQL, and introduces the following key innovations for pgvector data: - A new index type called StreamingDiskANN, inspired by the [DiskANN](https://github.com/microsoft/DiskANN) algorithm, based on research from Microsoft. - Statistical Binary Quantization: developed by Timescale researchers, This compression method improves on standard Binary Quantization. On a benchmark dataset of 50 million Cohere embeddings with 768 dimensions each, PostgreSQL with `pgvector` and `pgvectorscale` achieves **28x lower p95 latency** and **16x higher query throughput** compared to Pinecone's storage optimized (s1) index for approximate nearest neighbor queries at 99% recall, all at 75% less cost when self-hosted on AWS EC2. <div align=center>  </div> To learn more about the performance impact of pgvectorscale, and details about benchmark methodology and results, see the [pgvector vs Pinecone comparison blog post](http://www.timescale.com/blog/pgvector-vs-pinecone). In contrast to pgvector, which is written in C, pgvectorscale is developed in [Rust][rust-language] using the [PGRX framework](https://github.com/pgcentralfoundation/pgrx), offering the PostgreSQL community a new avenue for contributing to vector support. **Application developers or DBAs** can use pgvectorscale with their PostgreSQL databases. * [Install pgvectorscale](#installation) * [Get started using pgvectorscale](#get-started-with-pgvectorscale) If you **want to contribute** to this extension, see how to [build pgvectorscale from source in a developer environment](./DEVELOPMENT.md). For production vector workloads, get **private beta access to vector-optimized databases** with pgvector and pgvectorscale on Timescale. [Sign up here for priority access](https://timescale.typeform.com/to/H7lQ10eQ). ## Installation The fastest ways to run PostgreSQL with pgvectorscale are: * [Using a pre-built Docker container](#using-a-pre-built-docker-container) * [Installing from source](#installing-from-source) * [Enable pgvectorscale in a Timescale Cloud service](#enable-pgai-in-a-timescale-cloud-service) ### Using a pre-built Docker container 1. [Run the TimescaleDB Docker image](https://docs.timescale.com/self-hosted/latest/install/installation-docker/). 1. Connect to your database: ```bash psql -d "postgres://<username>:<password>@<host>:<port>/<database-name>" ``` 1. Create the pgvectorscale extension: ```sql CREATE EXTENSION IF NOT EXISTS vectorscale CASCADE; ``` The `CASCADE` automatically installs `pgvector`. ### Installing from source You can install pgvectorscale from source and install it in an existing PostgreSQL server 1. Compile and install the extension ```bash # install prerequisites ## rust curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh ## pgrx cargo install --locked cargo-pgrx cargo pgrx init --pg17 pg_config #download, build and install pgvectorscale cd /tmp git clone --branch <version> https://github.com/timescale/pgvectorscale cd pgvectorscale/pgvectorscale cargo pgrx install --release ``` You can also take a look at our [documentation for extension developers](./DEVELOPMENT.md) for more complete instructions. 1. Connect to your database: ```bash psql -d "postgres://<username>:<password>@<host>:<port>/<database-name>" ``` 1. Ensure the pgvector extension is available: ```sql SELECT * FROM pg_available_extensions WHERE name = 'vector'; ``` If pgvector is not available, install it using the [pgvector installation instructions][pgvector-install]. 1. Create the pgvectorscale extension: ```sql CREATE EXTENSION IF NOT EXISTS vectorscale CASCADE; ``` The `CASCADE` automatically installs `pgvector`. ### Enable pgvectorscale in a Timescale Cloud service Note: the instructions below are for Timescale's standard compute instance. For production vector workloads, we’re offering **private beta access to vector-optimized databases** with pgvector and pgvectorscale on Timescale. [Sign up here for priority access](https://timescale.typeform.com/to/H7lQ10eQ). To enable pgvectorscale: 1. Create a new [Timescale Service](https://console.cloud.timescale.com/signup?utm_campaign=vectorlaunch). If you want to use an existing service, pgvectorscale is added as an available extension on the first maintenance window after the pgvectorscale release date. 1. Connect to your Timescale service: ```bash psql -d "postgres://<username>:<password>@<host>:<port>/<database-name>" ``` 1. Create the pgvectorscale extension: ```postgresql CREATE EXTENSION IF NOT EXISTS vectorscale CASCADE; ``` The `CASCADE` automatically installs `pgvector`. ## Get started with pgvectorscale 1. Create a table with an embedding column. For example: ```postgresql CREATE TABLE IF NOT EXISTS document_embedding ( id BIGINT PRIMARY KEY GENERATED BY DEFAULT AS IDENTITY, metadata JSONB, contents TEXT, embedding VECTOR(1536) ) ``` 1. Populate the table. For more information, see the [pgvector instructions](https://github.com/pgvector/pgvector/blob/master/README.md#storing) and [list of clients](https://github.com/pgvector/pgvector/blob/master/README.md#languages). 1. Create a StreamingDiskANN index on the embedding column: ```postgresql CREATE INDEX document_embedding_idx ON document_embedding USING diskann (embedding vector_cosine_ops); ``` 1. Find the 10 closest embeddings using the index. ```postgresql SELECT * FROM document_embedding ORDER BY embedding <=> $1 LIMIT 10 ``` Note: pgvectorscale currently supports: cosine distance (`<=>`) queries, for indices created with `vector_cosine_ops`; L2 distance (`<->`) queries, for indices created with `vector_l2_ops`; and inner product (`<#>`) queries, for indices created with `vector_ip_ops`. This is the same syntax used by `pgvector`. If you would like additional distance types, [create an issue](https://github.com/timescale/pgvectorscale/issues). (Note: inner product indices are not compatible with plain storage.) ## Tuning The StreamingDiskANN index comes with **smart defaults** but also the ability to customize its behavior. There are two types of parameters: index build-time parameters that are specified when an index is created and query-time parameters that can be tuned when querying an index. We suggest setting the index build-time paramers for major changes to index operations while query-time parameters can be used to tune the accuracy/performance tradeoff for individual queries. We expect most people to tune the query-time parameters (if any) and leave the index build time parameters set to default. ### StreamingDiskANN index build-time parameters These parameters can be set when an index is created. | Parameter name | Description | Default value | |------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------| | `storage_layout` | `memory_optimized` which uses SBQ to compress vector data or `plain` which stores data uncompressed | memory_optimized | `num_neighbors` | Sets the maximum number of neighbors per node. Higher values increase accuracy but make the graph traversal slower. | 50 | | `search_list_size` | This is the S parameter used in the greedy search algorithm used during construction. Higher values improve graph quality at the cost of slower index builds. | 100 | | `max_alpha` | Is the alpha parameter in the algorithm. Higher values improve graph quality at the cost of slower index builds. | 1.2 | | `num_dimensions` | The number of dimensions to index. By default, all dimensions are indexed. But you can also index less dimensions to make use of [Matryoshka embeddings](https://huggingface.co/blog/matryoshka) | 0 (all dimensions) | `num_bits_per_dimension` | Number of bits used to encode each dimension when using SBQ | 2 for less than 900 dimensions, 1 otherwise An example of how to set the `num_neighbors` parameter is: ```sql CREATE INDEX document_embedding_idx ON document_embedding USING diskann (embedding) WITH(num_neighbors=50); ``` #### StreamingDiskANN query-time parameters You can also set two parameters to control the accuracy vs. query speed trade-off at query time. We suggest adjusting `diskann.query_rescore` to fine-tune accuracy. | Parameter name | Description | Default value | |------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------| | `diskann.query_search_list_size` | The number of additional candidates considered during the graph search. | 100 | `diskann.query_rescore` | The number of elements rescored (0 to disable rescoring) | 50 You can set the value by using `SET` before executing a query. For example: ```sql SET diskann.query_rescore = 400; ``` Note the [SET command](https://www.postgresql.org/docs/current/sql-set.html) applies to the entire session (database connection) from the point of execution. You can use a transaction-local variant using `LOCAL` which will be reset after the end of the transaction: ```sql BEGIN; SET LOCAL diskann.query_search_list_size= 10; SELECT * FROM document_embedding ORDER BY embedding <=> $1 LIMIT 10 COMMIT; ``` ## Get involved pgvectorscale is still at an early stage. Now is a great time to help shape the direction of this project; we are currently deciding priorities. Have a look at the list of features we're thinking of working on. Feel free to comment, expand the list, or hop on the Discussions forum. ## About Timescale Timescale is a PostgreSQL cloud company. To learn more visit the [timescale.com](https://www.timescale.com). [Timescale Cloud](https://console.cloud.timescale.com/signup?utm_campaign=vectorlaunch) is a high-performance, developer focused, cloud platform that provides PostgreSQL services for the most demanding AI, time-series, analytics, and event workloads. Timescale Cloud is ideal for production applications and provides high availability, streaming backups, upgrades over time, roles and permissions, and great security. [pgvector]: https://github.com/pgvector/pgvector/blob/master/README.md [rust-language]: https://www.rust-lang.org/ [pgvector-install]: https://github.com/pgvector/pgvector?tab=readme-ov-file#installation ", Assign "at most 3 tags" to the expected json: {"id":"10747","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"