Timescale

🚀 𝗘𝘃𝗲𝗿𝘆 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗶𝘀 𝗡𝗼𝘄 𝗮𝗻 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 - 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗽𝗴𝗮𝗶 𝗩𝗲𝗰𝘁𝗼𝗿𝗶𝘇𝗲𝗿 No need for specialized tools or vector databases—pgai Vectorizer lets you create, sync, and manage embeddings with just one SQL command. 🔹 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗶𝗻 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝘀: Build, store, and sync embeddings alongside your relational data—no extra infrastructure needed. 🔹 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝘀𝘆𝗻𝗰, 𝗿𝗮𝗽𝗶𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴: Keep embeddings fresh as your data changes. Test models instantly. 🔹 𝗔𝗹𝗹 𝗶𝗻 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝘀: Everything you need—embeddings, model access, and AI workflows—all with the SQL you already know. #Postgres #pgaiVectorizer #Postgres #Data #AI #SQL #DevTools #AIDevelopment #PostgresExtensions #AIinSQL

Keeping your embeddings in sync for RAG (retrieval-augmented generation) and other vector search applications just got easier with Timescale's pgai Vectorizer. PGAI Vectorizer automates embedding creation and synchronization directly within Postgres, eliminating the need for complex data pipelines. With a single SQL command, your AI models always have the most up-to-date vector data, enabling seamless and efficient RAG workflows and vector searches! Learn more at pgai vectorizer: https://lnkd.in/ghQ6k5ne

Vector Databases should actually be Vector Indexes Imagine if every time you inserted or updated a row, you had to reach out to an external system to update the associated B-trees. Each call risks failure, rate-limits, and throws in queuing, tracking, staleness handling, and overall complexity. Sounds like some 1984-style dystopia, right? (Well, actually, in 1984 Ingres already managed indexes automatically....) And yet, here in 2024, we’re all too willing to deal with this exact BS for vector indexes. Take a simple example of embedding blog posts. Vector databases treat chunks and embeddings as isolated data atoms, detached from the source data itself. This means each time I publish a new post or edit an old one, I need to manually update embeddings in Pinecone, Qdrant, Weviate, etc. Or I need to set up a complex web-service with monitoring and retry logic to handle it all automatically. Either way, it’s a giant headache, and it shouldn’t have to be this way. That’s why we built pgai Vectorizer — making embedding creation and synchronization as easy as using an index in PostgreSQL. With Vectorizer, you simply have a blog table in your database, and create a vectorizer with a single line of code as seen below. From there, pgai Vectorizer automatically creates embeddings for your blog entries and keeps them in sync with every insert, delete, or update in your blog table. No custom data workflows, infrastructure, or constant monitoring required. There are far more interesting (and fun) challenges in AI than babysitting data infrastructure. Let us take on that burden for you.

🛑 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗔𝗿𝗲 𝘁𝗵𝗲 𝗪𝗿𝗼𝗻𝗴 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻. 𝗛𝗲𝗿𝗲’𝘀 𝗪𝗵𝘆. They treat embeddings as standalone data, disconnected from their source, leading to outdated embeddings, constant sync issues, and endless maintenance. That’s why we built 𝗽𝗴𝗮𝗶 𝗩𝗲𝗰𝘁𝗼𝗿𝗶𝘇𝗲𝗿 𝗳𝗼𝗿 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝘀—so every engineer can build AI applications without the headache of managing embedding pipelines. Creating an embedding pipeline is as easy as building an index. It stays synced with your data automatically—no extra tools, no stale embeddings. 👉 Whether you’re a busy AI engineer or just getting started, check out the blog to see how pgai Vectorizer lets you focus on building killer AI apps. 🔗👇 #Postgres #pgaiVectorizer #Postgres #Data #AI #SQL #DevTools #AIDevelopment #PostgresExtensions #AIinSQL

Building a RAG application? Need to create embeddings and keep them up-to-date as source data changes? Don't want to manage a complex data pipeline? Look no further! pgai Vectorizer is your open-source solution for this automation... and it's built on Postgres! We spent the last few months building this feature, so you don't have to. https://lnkd.in/dww3-q3h https://lnkd.in/grba5PX8 Here is your one-SQL-statement solution:

PostgreSQL just got a supercharged AI makeover - say hello to #pgai and its new Vectorizer tool that creates and syncs embeddings right in the database. Read this morning's VentureBeat article on Timescale's latest launch here: #postgresql #pgai #pgvectorscale #vectordatabase #ai #vectorizer

With Timescale's pgai tool, one can now automatically create embeddings for their text data. It's now possible to implement a full RAG pipeline using only PostgreSQL and the pgai extension. Nothing else required. This is kinda crazy - if you think about how many components you need just a year ago to implement RAG. I'm not even talking about just simple RAG. Many of the 'advanced RAG' techniques can be implemented with just this component. Astonishing!

Timescale Brings PostgreSQL into the GenAI Era with pgai Vectorizer https://ow.ly/Qsce50TVrHb #Technologynews #AI #Technews #cioinfluence #Timescale

Always a pleasure discussing the world of #PostgreSQL with you, Jan Karremans 🙌

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