PostgresML is an excellent AI extension to your pSQL database for training, using, and connecting AI 40x faster.

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: ift.tt/ysPAOro

🏁RAG Retrieval Speed Showdown We conducted a hands-on speed comparison of the most popular #rag retrieval systems: 1⃣Pinecone + Hugging Face 2⃣ Qdrant + HuggingFace 3⃣ Weaviate AI Database + HuggingFace 4⃣ Zilliz + HuggingFace 5⃣ PostgresML via Korvus The

Want to see how devs are using PostgresML? 🦉 Check out this great rundown from Krish Naik ⬇️ dlvr.it/TBX8HX

Cody Blakeney PostgresML / Korvus

Too many enterprise AI/ML apps are out of date before they launch, and unmaintainable long term.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. ⬇️ dlvr.it/TBd1P9

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 👇

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 👉 [postgresml.org/blog/sudowrite…]

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:

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: [dlvr.it/TDlprd dlvr.it/TDlprh

How to build a better search engine for Y Combinator 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

Guillermo Rauch PostgresML has been there before 😎

🚀 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 👇 dlvr.it/TFZyVt #AIsearch #databasedev