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AI, RAG and Vector Databases

If you want to use AI with RAG then go through these resources:

A great langchain RAG tutorial video from @pixegami

The front end for Vector Databases. Works with most of the vector databases such as Pinecone, Chroma, Q
https://vectoradmin.com/

https://www.pinecone.io/

Build knowledgeable AI
With its vector database at the core, Pinecone is the leading knowledge platform for building accurate, secure, and scalable AI applications.

https://www.trychroma.com/

Chroma is the open-source AI application database. Batteries included.

https://qdrant.tech/

High-Performance Vector Search at Scale
Powering the next generation of AI applications with advanced, open-source vector similarity search technology.

https://weaviate.io/

The AI-native database for a new generation of software
Bring intuitive AI-native applications to life with less hallucination, data leakage, and vendor lock-in with the open source vector database developers love.

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🚀 pgai Vectorizer: SQLAlchemy and LiteLLM Make Vector Search Simple

We built pgai Vectorizer to simplify embedding management for AI applications—without needing a separate database or complex infrastructure. Since launch, developers have created over 3,000 vectorizers on Timescale Cloud, with many more self-hosted.

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Top comments (1)

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tejas_kumar_83c520d6bef27 profile image
Tejas Kumar •

Great resource compilation! For those exploring vector databases, it's also worth checking out Astra DB's vector capabilities - they have some interesting RAG examples and offer a generous free tier for testing. The serverless aspect makes it particularly convenient for prototyping RAG applications.

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