Repository files navigation

Postgres + GPUs for ML/AI applications.

|Documentation |Blog |Discord |

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.

• Getting started
  • PostgresML Cloud
  • Self-hosted
  • Ecosystem
• Large Language Models
  • Hugging Face
  • OpenAI and Other Providers
• RAG
  • Chunk
  • Embed
  • Rank
  • Transform
• Machine Learning

• 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

The only prerequisites for using PostgresML is a Postgres database with our open-sourcepgml extension installed.

Our serverless cloud is the easiest and recommend way to get started.

Sign up for a free PostgresML account. You'll get a free database in seconds, with access to GPUs and state of the art LLMs.

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:2.10.0 \    sudo -u postgresml psql -d postgresml

For more details, take a look at ourQuick Start with Docker documentation.

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 withpsql and use any of your favorite tooling and client libraries likepsycopg to connect and run queries.

PostgresML Specific Client Libraries:

• Korvus - Korvus is a Python, JavaScript, Rust and C search SDK that unifies the entire RAG pipeline in a single database query.
• postgresml-django - postgresml-django is a Python module that integrates PostgresML with Django ORM.

Recommended Postgres Poolers:

• pgcat - pgcat is a PostgreSQL pooler with sharding, load balancing and failover support.

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

PostgresML supports a wide range of state-of-the-art deep learning architectures available on the Hugging Facemodel hub. 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

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.

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: Splits text into manageable segments
2. Embed: Generates vector embeddings from text using pre-trained models
3. Rank: Performs similarity search on embeddings
4. Transform: Applies language models for text generation or transformation

For more information on using RAG with PostgresML see our guide onUnified RAG.

Thepgml.chunk function chunks documents using the specified splitter. This is typically done before embedding.

pgml.chunk(    splitter TEXT,    -- splitter name    text TEXT,        -- text to embed    kwargs JSON       -- optional arguments (see below))

Seepgml.chunk docs for more information.

Thepgml.embed function generates embeddings from text using in-database models.

pgml.embed(    transformer TEXT,    "text" TEXT,    kwargs JSONB)

Seepgml.embed docs for more information.

Thepgml.rank function usesCross-Encoders to score sentence pairs.

This is typically used as a re-ranking step when performing search.

pgml.rank(    transformer TEXT,    query TEXT,    documents TEXT[],    kwargs JSONB)

Docs coming soon.

Thepgml.transform function can be used to generate text.

SELECT pgml.transform(    task   => TEXT OR JSONB,     -- Pipeline initializer arguments    inputs => TEXT[] OR BYTEA[], -- inputs for inference    args   => JSONB              -- (optional) arguments to the pipeline.)

Seepgml.transform docs for more information.

See ourText Generation guide for a guide on generating text.

Some highlights:

• 47+ classification and regression algorithms
• 8 - 40X faster inference than HTTP based model serving
• Millions of transactions per second
• Horizontal scalability

Training a classification model

Training

SELECT * FROM pgml.train(    'Handwritten Digit Image Classifier',    algorithm => 'xgboost',    'classification',    'pgml.digits',    'target');

Inference

SELECT pgml.predict(    'My Classification Project',    ARRAY[0.1, 2.0, 5.0]) AS prediction;

Thepgml.transform function exposes a number of available NLP tasks.

Available tasks are:

• Text Classification
• Zero-Shot Classification
• Token Classification
• Translation
• Summarization
• Question Answering
• Text Generation
• Text-to-Text Generation
• Fill-Mask