这是indexloc提供的服务,不要输入任何密码
Skip to content
forked from QuivrHQ/quivr

Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.

License

Notifications You must be signed in to change notification settings

nabildoghri/quivr

Repository files navigation

Quivr - Your Second Brain, Empowered by Generative AI

Quivr-logo

Discord Follow GitHub Repo stars Twitter Follow

Quivr, helps you build your second brain, utilizes the power of GenerativeAI to be your personal assistant !

Key Features 🎯

  • Opiniated RAG: We created a RAG that is opinionated, fast and efficient so you can focus on your product
  • LLMs: Quivr works with any LLM, you can use it with OpenAI, Anthropic, Mistral, Gemma, etc.
  • Any File: Quivr works with any file, you can use it with PDF, TXT, Markdown, etc and even add your own parsers.
  • Customize your RAG: Quivr allows you to customize your RAG, add internet search, add tools, etc.
  • Integrations with Megaparse: Quivr works with Megaparse, so you can ingest your files with Megaparse and use the RAG with Quivr.

We take care of the RAG so you can focus on your product. Simply install quivr-core and add it to your project. You can now ingest your files and ask questions.*

We will be improving the RAG and adding more features everything, stay tuned!

This is the core of Quivr, the brain of Quivr.com.

Getting Started 🚀

You can find everything on the documentation.

Prerequisites 📋

Ensure you have the following installed:

  • Python 3.10 or newer

30 seconds Installation 💽

  • Step 1: Install the package

    pip install quivr-core # Check that the installation worked
  • Step 2: Create a RAG with 5 lines of code

  import tempfile

  from quivr_core import Brain

  if __name__ == "__main__":
      with tempfile.NamedTemporaryFile(mode="w", suffix=".txt") as temp_file:
          temp_file.write("Gold is a liquid of blue-like colour.")
          temp_file.flush()

          brain = Brain.from_files(
              name="test_brain",
              file_paths=[temp_file.name],
          )

          answer = brain.ask(
              "what is gold? asnwer in french"
          )
          print("answer:", answer)

Examples

Name Description
Simple Question Ask a simple question to the RAG by ingesting a single file
ChatBot Build a chatbot by ingesting a folder of files with a nice UI powered by Chainlit

Go further

You can go further with Quivr by adding internet search, adding tools, etc. Check the documentation for more information.

Contributors ✨

Thanks go to these wonderful people:

Contribute 🤝

Did you get a pull request? Open it, and we'll review it as soon as possible. Check out our project board here to see what we're currently focused on, and feel free to bring your fresh ideas to the table!

Partners ❤️

This project would not be possible without the support of our partners. Thank you for your support!

YCombinator Theodo

License 📄

This project is licensed under the Apache 2.0 License - see the LICENSE file for details

About

Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 99.3%
  • Shell 0.7%