Postgres with GPUs for ML/AI apps 🦉 https://xitter.cf/t.co/vPFyV4vG65 ⭐ https://xitter.cf/t.co/4RcSlBieFt 💬 https://xitter.cf/t.co/MdFYniGsr0

San Francisco, CA
Joined March 2023
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🚀 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
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Replying to @rauchg
@postgresml has been there before 😎
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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: postgresml.org/blog/korvus-t…
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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
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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.org/chatbot What do you think about in-database ML? #chatbots #RAG #OpenSourceAI
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Replying to @consulted_graph
👏 we love to see it. Cheers to more projects that move the models to where the data lives instead of the other way around.
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Get all the details on the blog ⬇️ postgresml.org/blog/announci…
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PostgresML is an excellent AI extension to your pSQL database for training, using, and connecting AI 40x faster.
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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/sudowrit…]
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PostgresML retweeted
Replying to @code_star
PostgresML / Korvus
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PostgresML retweeted
Bony Bean@bonybean14 Jul 2024
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
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PostgresML retweeted
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 👇
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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
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Want to see how devs are using PostgresML? 🦉 Check out this great rundown from @Krishnaik06 ⬇️ dlvr.it/TBX8HX
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🏁RAG Retrieval Speed Showdown We conducted a hands-on speed comparison of the most popular #rag retrieval systems: 1⃣@pinecone + @huggingface 2⃣ @qdrant_engine + HuggingFace 3⃣ @weaviate_io + HuggingFace 4⃣ @zilliz_universe + HuggingFace 5⃣ @postgresml via Korvus The results? A performance gap of nearly 10x between the fastest and slowest system. 👉 Dive into the full post to get the details and analysis. postgresml.org/blog/a-speed-…
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"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 ➡️ dlvr.it/TB2BBw
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In my experience, the accuracy gap between open-source and proprietary is negligible now and open-source is cheaper, faster, more customizable & sustainable for companies! No excuse anymore not to become an AI builder based on open-source AI (vs outsourcing to APIs)!
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Excited to announce support for #Meta #Llama 3.1 in PostgresML Cloud! Get the details on our blog ➡️ dlvr.it/T9zdGm
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Re-ranking retrieved results can be a powerful boost to the relevance of your response. With a managed index like @postgresml that's as easy as passing an extra parameter or two. Check out their guest post on our blog: llamaindex.ai/blog/improving…
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