Quivr - Your Second Brain, Empowered by Generative AI

](https://discord.gg/HUpRgp2HG8) [ [](https://twitter.com/_StanGirard)

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, 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

```bash

pip install quivr-core # Check that the installation worked

```

• Step 2: Create a RAG with 5 lines of code

```python

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)

```

Configuration

Workflows

#### Basic RAG

Creating a basic RAG workflow like the one above is simple, here are the steps:

1. 1.Add your API Keys to your environment variables

import os
os.environ["OPENAI_API_KEY"] = "myopenai_apikey"

Quivr supports APIs from Anthropic, OpenAI, and Mistral. It also supports local models using Ollama.

1. 1.Create the YAML file `basic_rag_workflow.yaml` and copy the following content in it

workflow_config:
  name: "standard RAG"
  nodes:
    - name: "START"
      edges: ["filter_history"]

    - name: "filter_history"
      edges: ["rewrite"]

    - name: "rewrite"
      edges: ["retrieve"]

    - name: "retrieve"
      edges: ["generate_rag"]

    - name: "generate_rag" # the name of the last node, from which we want to stream the answer to the user
      edges: ["END"]