🚀 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://postgresml.org/blog/semantic-search-in-postgres-in-15-minutes?utm_medium=mastodon

#AIsearch #databasedev

How to build a better search engine for @ycombinator job listings 🧠 💻
Our guide shows you how to:

1️⃣ Extract structured data from job listings
2️⃣ Create semantic embeddings for intelligent matching
3️⃣ Perform context-aware searches beyond simple
keywords

Powered by @runtrellis & the @PostgresML SDK, Korvus
👇 Tutorial: https://postgresml.org/blog/korvus-trellis-semantic-search-over-yc-jobs?utm_source=dlvr.it&utm_medium=mastodon 💻 💻 💻

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://postgresml.org/blog/meta-llama-3.2-now-available-in-postgresml-serverless%5D?utm_source=dlvr.it&utm_medium=mastodon https://postgresml.org/blog/meta-llama-3.2-now-available-in-postgresml-serverless?utm_source=dlvr.it&utm_medium=mastodon

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: http://postgresml.org/chatbot?utm_source=dlvr.it&utm_medium=mastodon
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://github.com/postgresml/postgresml-django?utm_source=dlvr.it&utm_medium=mastodon

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 Postgres DB

Their founder's take? "PostgresML just makes a lot of sense"

Get the details here 👉 [https://postgresml.org/blog/sudowrite-postgresml%5D?utm_source=dlvr.it&utm_medium=mastodon https://postgresml.org/blog/sudowrite-postgresml?utm_source=dlvr.it&utm_medium=mastodon

Too many enterprise AI/ML apps are out of date before they launch, and unmaintainable long term.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://postgresml.org/blog/llms-are-commoditized-data-is-the-differentiator?utm_source=dlvr.it&utm_medium=mastodon

Want to see how devs are using PostgresML? 🦉

Check out this great rundown from @Krishnaik06 ⬇️

https://www.youtube.com/watch?v=zT-cc1IblsQ&utm_source=dlvr.it&utm_medium=mastodon

RAG Retrieval Speed Showdown 🏁

We conducted a hands-on speed comparison of the most popular RAG retrieval systems:

1️⃣ Pinecone + HuggingFace
2️⃣ Qdrant + HuggingFace
3️⃣ Weaviate + HuggingFace
4️⃣ Zilliz + HuggingFace
5️⃣ PostgresML via Korvus

The results? A performance gap of nearly 10x between the fastest and slowest system.

👉 Dive into our full blog post to get the details and analysis.

https://postgresml.org/blog/a-speed-comparison-of-the-most-popular-retrieval-systems-for-rag?utm_source=dlvr.it&utm_medium=mastodon

"Open source AI is important, not because of some pedantic definition by some pseudo-official body like OSI — it’s important because of the power and incentive structures that pervade our society."

Read on in our blog ➡️

https://postgresml.org/blog/whats-hacker-news-problem-with-open-source-ai?utm_source=dlvr.it&utm_medium=mastodon