Use Ollama Embeddings with PostgreSQL (Tutorial)

🔥 Build AI Apps with PostgreSQL, pgvector, and Ollama: Complete Tutorial Learn how to build powerful AI applications using PostgreSQL, pgVector, and Ollama! In this tutorial, we'll show you how to: - Set up Ollama for local LLM deployment - Use pgVector to turn Postgres into a vector database - Automate embedding creation with pgai Vectorizer - Perform semantic search on text data - Build a RAG (Retrieval Augmented Generation) application We'll use Sam Altman's blog posts as example data to demonstrate how to create a complete RAG pipeline using 100% open source tools. All the code runs locally on your machine - no API costs or third-party dependencies required! 🛠️ Technologies covered: - PostgreSQL - Ollama - pgvector extension - Pgai Extension & Vectorizer - Nomic Embed (embedding model) - TinyLlama (1.1B parameter LLM) All resources and code are available in the PGAI GitHub repo. Start building your AI apps today! 📌 pgai Github repo ⇒ https://github.com/timescale/pgai 📌 Ollama ⇒ https://ollama.com 📌 pgai Vectorizer Ollama Quickstart ⇒ https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md 📌 The Emerging Open-Source AI Stack ⇒ https://www.timescale.com/blog/the-emerging-open-source-ai-stack 📚 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀 00:00 Introduction: Ollama and PostgreSQL 01:19 Set up 01:38 Setup with Docker Compose 02:43 Dataset overview 03:29 Download embedding models and LLM from Ollama 04:35 Using pgai Vectorizer to auto-embed data 07:02 pgai Vectorizer Architecture overview 09:12 Vectorizer Status Check 10:45 Example: Similarity Search with pgvector and pgai 11:51 Example: RAG with pgvector and pgai 14:18 Learn more on pgai github