Want easy mode for RAG? Try the Korvus SDK arrow_forward
You're managing a multitude of microservices - a vector database, embedding model, LLMs, and frameworks to glue them all together.
Production outages that won't stop, high-latency UX, ever-increasing dev time, and data-hungry compute with costly vendors.
Your data is sent through multiple systems. You can't be sure if it's secure, stable, compliant or private.
than HuggingFace + Pinecone for a RAG chatbot
than OpenAI for embedding generation
On vector database cost compared to Pinecone
Explore the SDK and test open source models in our hosted database.
Our pricing is based on the models you use. It’s designed to minimize costs and operations. You’ll also save because you can replace many existing tools.
This is why I’m bullish on @postgresml - devs will always prefer to do things in data stores they already use in production
James yu
@jamesyu
Great article by PostgresML, running @huggingface models INSIDE @PostgreSQL nice tidbit on scalability: "Our example data is based on 5 million DVD reviews from Amazon ... that's more data than fits in a Pinecone Pod at the time of writing"
Paul Copplestone
@kiwicopple
Love the fact that @postgresml can run various algorithms to find the optimum one for model creation
RebataurAI
@rebataur
You can look at PostgresML. Its based on Postgres, not specifically a vector database but they've got a pleasantly full featured eco-system for the whole training process, fetching datasets, huggingface integration, training etc. of course they also have vector related functions
Dushyant (e/acc)
@DevDminGod
If you want to seamlessly integrate machine learning models into your #PostgreSQL database, use PostgresML.
Khuyen Tran
@KhuyenTran16
💯 there's also PostgresML if you wanna get a little more full featured - supports embedding in-database as well as CUBE / pgvector
Martin McFly
@martinmark
Tons of capability in that Postgres extension. It's an important part of the ML Stack at cloud.tembo.io as well.
Adam Hendel
@adamhendel
A game-changer indeed! By integrating ML and AI directly at the database level with @postgresml, we're not just streamlining processes but revolutionizing data handling and insights generation in one fell swoop.
Pranay Suyash
@pranaysuyash
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