🚀 Implement semantic search in #Postgres in just 15 minutes. Pure SQL, no ML expertise needed.
🐘 Use pgml and pgvector extensions
🔢 Convert text to embeddings
⚖️ Measure vector similarity
📈 Optimize with HNSW indexes
👇
https://t.co/OQYRWFrrKd
#AIsearch#databasedev
How to build a better search engine for @ycombinator job listings 🧠💻
Our guide shows you how to:
1⃣ Extract structured data from job listings
2⃣Create semantic embeddings for intelligent matching
3⃣Perform context-aware searches beyond simple
keywords
Powered by @runtrellis & the @postgresml SDK, Korvus
👇Tutorial: https://t.co/zyybs2RKZg
PostgresML now supports Llama 3.2 🦙
🦉 Free for all Serverless Database users
🐘 Bring models to your data, not vice versa
✨ Smaller models, bigger capabilities
Read on in the blog: [https://t.co/HnB0OjNHzX https://t.co/DiZRmazvYG
Ever wondered how RAG works inside a database?
Check out our latest chatbot ➡️
It performs RAG over all of Wikipedia, using open-source models directly in Postgres.
✨Full RAG workflow in one SQL query
🐘In-database processing
🌎100% open-source
Try it: https://t.co/xhUR3bZeSQ
What do you think about in-database ML?
#chatbots #RAG #OpenSourceAI
🚀 postgresml-django
Seamlessly integrate ML/AI with #django ORM
✨ Automatic in-database embeddings
🔍 Vector similarity search
🧠 ML-powered Django projects
Try it now, let us know what you think.
https://t.co/OXjQNEwBci
Check out what @sudowrite pulled off with their #RAG stack using PostgresML:
⚡️ Whipped up a prototype in just 3 hours
📈Now crunching 1M+ calls/hour like it's nothing
🐘 Doing all this fancy AI stuff inside their database
Get the details here 👉 [https://t.co/9VdAndhD4n]
Explore the groundbreaking Korvus project, a game-changer for building RAG workflows! This open-source tool leverages PostgresML for in-database machine learning, offering a unified, efficient approach. Learn more: https://t.co/H8VVQi57oK
Want to do RAG on websites all with one DB call?
The team at @postgresml posted an amazing blog on how to use Firecrawl with their Korvus SDK which unifies the entire RAG pipeline in a single database query.
Check it out below 👇
Too many enterprise AI/ML apps are out of date before they launch, and unmaintainable long term. 🙃
Why?
Complex data pipelines. X-functional teams work across multiple quarters to get it right. Isn't there a better way?
Read on in the blog.
⬇️
https://t.co/Igr2fHrahh