Pgai Tutorial - Vector Embeddings in PostgreSQL Made Easy
Want to start freelancing? Let me help: https://academy.datalumina.com/freelance Want to learn real AI Engineering? Go here: https://academy.datalumina.com/accelerator 💼 Need help with a project? Work with me: https://www.datalumina.com/ 🔗 GitHub Repository https://github.com/daveebbelaar/pgvectorscale-rag-solution/tree/pgai 📚 More Resources https://github.com/timescale/pgai https://tsdb.co/dave-vectorizer-blog 🔗 Check out Timescale https://tsdb.co/dave-signup 🛠️ My Development Workflow https://youtu.be/3sIzCFuLgIQ ⏱️ Timestamps 0:05 Introduction to Vector Databases 0:18 Timescale's Argument Against Current Abstractions 0:22 Setting Up the Pgai Environment 1:31 Configuring Docker and PostgreSQL 2:47 Understanding the Vectorizer Worker 5:01 Database Initialization and SQL Queries 6:21 Creating Tables and Extensions 7:29 Inserting Data into PostgreSQL 8:49 Adding Context and Summaries 10:05 Managing Data and Embeddings 12:27 Viewing and Analyzing Embeddings 14:06 Performing Semantic Search 15:16 Comparison of Embedding Models 17:01 Running Experiments with Different Models 19:59 Automating Vector Embeddings in Postgres 20:46 Conclusion and Future Prospects 👋🏻 About Me Hi! I'm Dave, a Data Scientist turned AI Engineer and founder of Datalumina®. On this channel, I share practical tutorials to help you become better at building data + AI applications. If you want to know how I help data professionals beyond these videos, then check out the links on my channel. 📌 Description In this video, I discuss the challenges of traditional vector databases and introduce Timescale's Pgai solution, which simplifies embedding creation and synchronization within PostgreSQL. I walk through the setup process using tools like Docker and the OpenAI API, and explain how to structure your database for both source and embedding data. Viewers will learn to automate embeddings, perform semantic searches with SQL, and utilize multiple embedding models. This integrated approach streamlines AI workflows and optimizes the use of databases like Postgres for future AI projects. #pgai #timescale #rag
