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/blo...
📌 The Emerging Open-Source AI Stack ⇒ https://www.timescale.com/blog/the-em...
📚 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