21:I[4606,["9139","static/chunks/9139-29aac3297803e837.js","5162","static/chunks/5162-971519dd5e179b15.js","1163","static/chunks/app/%5Blocale%5D/(with-footer)/ai/%5BwebsiteName%5D/page-93c09f9a3f0813c2.js"],""] 23:I[1895,["9139","static/chunks/9139-29aac3297803e837.js","5162","static/chunks/5162-971519dd5e179b15.js","1163","static/chunks/app/%5Blocale%5D/(with-footer)/ai/%5BwebsiteName%5D/page-93c09f9a3f0813c2.js"],""] 22:T668,

What is PostgresML?

PostgresML is a complete MLOps platform implemented as a PostgreSQL extension. It allows users to build fast, simple, and powerful machine learning models directly within their database. It offers GPU-powered Postgres databases for building AI applications, enabling tasks like vector embedding, real-time output generation, and more. PostgresML simplifies the AI stack by integrating various components like vector databases, embedding models, and LLMs into a single system.

How to use PostgresML?

PostgresML can be used through SQL or SDKs in JS and Python. Users can perform tasks like text generation, embedding creation, and vector database operations directly within PostgreSQL. The platform supports various open-source models and allows for fine-tuning LLMs on user data.

PostgresML's Core Features

Build and deploy ML models directly within PostgreSQL

GPU-powered Postgres databases for AI applications

Vector embedding and real-time output generation

Integration with open-source models (Mistral, Llama, etc.)

Support for SQL and SDKs in JS and Python

PostgresML's Use Cases

#1 RAG (Retrieval Augmented Generation)

#2 Search

#3 Chatbot

#4 Text Generation

#5 Embeddings

#6 Vector Database

#7 Supervised Learning

PostgresML

📋Introduction

User Rating