base on Postgres with GPUs for ML/AI apps. <div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://github.com/user-attachments/assets/5d5510da-6014-4cf3-849f-566050e053da"> <source media="(prefers-color-scheme: light)" srcset="https://github.com/user-attachments/assets/aea1c38a-15bf-4270-8365-3d5e6311f5fc"> <img alt="Logo" src="" width="520"> </picture> </div> <p align="center"> <p align="center"><b>Postgres + GPUs for ML/AI applications.</b></p> </p> <p align="center"> | <a href="https://postgresml.org/docs/"><b>Documentation</b></a> | <a href="https://postgresml.org/blog"><b>Blog</b></a> | <a href="https://discord.gg/DmyJP3qJ7U"><b>Discord</b></a> | </p> --- Why do ML/AI in Postgres? Data for ML & AI systems is inherently larger and more dynamic than the models. It's more efficient, manageable and reliable to move models to the database, rather than constantly moving data to the models.</b></p> </p> - [Getting started](#getting-started) - [PostgresML Cloud](#postgresml-cloud) - [Self-hosted](#self-hosted) - [Ecosystem](#ecosystem) - [Large Language Models](#large-language-models) - [Hugging Face](#hugging-face) - [OpenAI and Other Providers](#openai) - [RAG](#rag) - [Chunk](#chunk) - [Embed](#embed) - [Rank](#rank) - [Transform](#transform) - [Machine Learning](#machine-learning) ## Architecture <div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://github.com/user-attachments/assets/e27f8bda-1fe6-49f8-b9d8-ef563e0150e5"> <source media="(prefers-color-scheme: light)" srcset="https://github.com/user-attachments/assets/09bbed94-b73f-447b-95d9-2d4a7727c3aa"> <img alt="Logo" src="" width="784"> </picture> </div> <div align="center"> <b>PostgresML is a powerful Postgres extension that seamlessly combines data storage and machine learning inference within your database</b>. By integrating these functionalities, PostgresML eliminates the need for separate systems and data transfers, enabling you to perform ML operations directly on your data where it resides. </div> ## Features at a glance - **In-Database ML/AI**: Run machine learning and AI operations directly within PostgreSQL - **GPU Acceleration**: Leverage GPU power for faster computations and model inference - **Large Language Models**: Integrate and use state-of-the-art LLMs from Hugging Face - **RAG Pipeline**: Built-in functions for chunking, embedding, ranking, and transforming text - **Vector Search**: Efficient similarity search using pgvector integration - **Diverse ML Algorithms**: 47+ classification and regression algorithms available - **High Performance**: 8-40X faster inference compared to HTTP-based model serving - **Scalability**: Support for millions of transactions per second and horizontal scaling - **NLP Tasks**: Wide range of natural language processing capabilities - **Security**: Enhanced data privacy by keeping models and data together - **Seamless Integration**: Works with existing PostgreSQL tools and client libraries # Getting started The only prerequisites for using PostgresML is a Postgres database with our open-source `pgml` extension installed. ## PostgresML Cloud Our serverless cloud is the easiest and recommend way to get started. [Sign up for a free PostgresML account](https://postgresml.org/signup). You'll get a free database in seconds, with access to GPUs and state of the art LLMs. ## Self-hosted If you don't want to use our cloud you can self host it. ``` docker run \ -it \ -v postgresml_data:/var/lib/postgresql \ -p 5433:5432 \ -p 8000:8000 \ ghcr.io/postgresml/postgresml:2.7.12 \ sudo -u postgresml psql -d postgresml ``` For more details, take a look at our [Quick Start with Docker](https://postgresml.org/docs/open-source/pgml/developers/quick-start-with-docker) documentation. ## Ecosystem We have a number of other tools and libraries that are specifically designed to work with PostgreML. Remeber PostgresML is a postgres extension running inside of Postgres so you can connect with `psql` and use any of your favorite tooling and client libraries like [psycopg](https://www.psycopg.org/psycopg3/) to connect and run queries. <b>PostgresML Specific Client Libraries:</b> - [Korvus](https://github.com/postgresml/korvus) - Korvus is a Python, JavaScript, Rust and C search SDK that unifies the entire RAG pipeline in a single database query. - [postgresml-django](https://github.com/postgresml/postgresml-django) - postgresml-django is a Python module that integrates PostgresML with Django ORM. <b>Recommended Postgres Poolers:</b> - [pgcat](https://github.com/postgresml/pgcat) - pgcat is a PostgreSQL pooler with sharding, load balancing and failover support. # Large language models PostgresML brings models directly to your data, eliminating the need for costly and time-consuming data transfers. This approach significantly enhances performance, security, and scalability for AI-driven applications. By running models within the database, PostgresML enables: - Reduced latency and improved query performance - Enhanced data privacy and security - Simplified infrastructure management - Seamless integration with existing database operations ## Hugging Face PostgresML supports a wide range of state-of-the-art deep learning architectures available on the Hugging Face [model hub](https://huggingface.co/models). This integration allows you to: - Access thousands of pre-trained models - Utilize cutting-edge NLP, computer vision, and other AI models - Easily experiment with different architectures ## OpenAI and other providers While cloud-based LLM providers offer powerful capabilities, making API calls from within the database can introduce latency, security risks, and potential compliance issues. Currently, PostgresML does not directly support integration with remote LLM providers like OpenAI. # RAG PostgresML transforms your PostgreSQL database into a powerful vector database for Retrieval-Augmented Generation (RAG) applications. It leverages pgvector for efficient storage and retrieval of embeddings. Our RAG implementation is built on four key SQL functions: 1. [Chunk](#chunk): Splits text into manageable segments 2. [Embed](#embed): Generates vector embeddings from text using pre-trained models 3. [Rank](#rank): Performs similarity search on embeddings 4. [Transform](#transform): Applies language models for text generation or transformation For more information on using RAG with PostgresML see our guide on [Unified RAG](https://postgresml.org/docs/open-source/pgml/guides/unified-rag). ## Chunk The `pgml.chunk` function chunks documents using the specified splitter. This is typically done before embedding. ```postgresql pgml.chunk( splitter TEXT, -- splitter name text TEXT, -- text to embed kwargs JSON -- optional arguments (see below) ) ``` See [pgml.chunk docs](https://postgresml.org/docs/open-source/pgml/api/pgml.chunk) for more information. ## Embed The `pgml.embed` function generates embeddings from text using in-database models. ```postgresql pgml.embed( transformer TEXT, "text" TEXT, kwargs JSONB ) ``` See [pgml.embed docs](https://postgresml.org/docs/open-source/pgml/api/pgml.embed) for more information. ## Rank The `pgml.rank` function uses [Cross-Encoders](https://www.sbert.net/examples/applications/cross-encoder/README.html) to score sentence pairs. This is typically used as a re-ranking step when performing search. ```postgresl pgml.rank( transformer TEXT, query TEXT, documents TEXT[], kwargs JSONB ) ``` Docs coming soon. ## Transform The `pgml.transform` function can be used to generate text. ```postgresql SELECT pgml.transform( task => TEXT OR JSONB, -- Pipeline initializer arguments inputs => TEXT[] OR BYTEA[], -- inputs for inference args => JSONB -- (optional) arguments to the pipeline. ) ``` See [pgml.transform docs](https://postgresml.org/docs/open-source/pgml/api/pgml.transform) for more information. See our [Text Generation guide](https://postgresml.org/docs/open-source/pgml/guides/llms/text-generation) for a guide on generating text. # Machine learning <b>Some highlights:</b> - [47+ classification and regression algorithms](https://postgresml.org/docs/open-source/pgml/api/pgml.train) - [8 - 40X faster inference than HTTP based model serving](https://postgresml.org/blog/postgresml-is-8x-faster-than-python-http-microservices) - [Millions of transactions per second](https://postgresml.org/blog/scaling-postgresml-to-one-million-requests-per-second) - [Horizontal scalability](https://postgresml.org/docs/open-source/pgcat/) **Training a classification model** *Training* ```postgresql SELECT * FROM pgml.train( 'Handwritten Digit Image Classifier', algorithm => 'xgboost', 'classification', 'pgml.digits', 'target' ); ``` *Inference* ```postgresql SELECT pgml.predict( 'My Classification Project', ARRAY[0.1, 2.0, 5.0] ) AS prediction; ``` ## NLP The `pgml.transform` function exposes a number of available NLP tasks. Available tasks are: - [Text Classification](https://postgresml.org/docs/open-source/pgml/guides/llms/text-classification) - [Zero-Shot Classification](https://postgresml.org/docs/open-source/pgml/guides/llms/zero-shot-classification) - [Token Classification](https://postgresml.org/docs/open-source/pgml/guides/llms/token-classification) - [Translation](https://postgresml.org/docs/open-source/pgml/guides/llms/translation) - [Summarization](https://postgresml.org/docs/open-source/pgml/guides/llms/summarization) - [Question Answering](https://postgresml.org/docs/open-source/pgml/guides/llms/question-answering) - [Text Generation](https://postgresml.org/docs/open-source/pgml/guides/llms/text-generation) - [Text-to-Text Generation](https://postgresml.org/docs/open-source/pgml/guides/llms/text-to-text-generation) - [Fill-Mask](https://postgresml.org/docs/open-source/pgml/guides/llms/fill-mask) ", Assign "at most 3 tags" to the expected json: {"id":"2750","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"