quivr

What it is

Quivr is an opinionated Retrieval-Augmented Generation (RAG) framework that simplifies the integration of Generative AI into applications. It supports various LLMs and vectorstores, allowing developers to focus on their product rather than RAG.

Quivr stands out by offering a flexible and customizable RAG framework that supports multiple LLMs and vectorstores, enabling developers to focus on their product rather than the intricacies of RAG.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited documentation for advanced use cases

   Core concepts and detailed configuration options are not well-documented

What it does well

• Supports multiple LLMs including GPT4, Groq, and Llama

• Flexible vectorstore support with options like PGVector and Faiss

• Customizable for various file types and integration methods

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.