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https://github.com/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.
https://github.com/quivrhq/quivr

ai api chatbot chatgpt database docker framework frontend groq html javascript llm openai postgresql privacy rag react security typescript vector

Last synced: 6 days ago
JSON representation

Host: GitHub
URL: https://github.com/quivrhq/quivr
Owner: QuivrHQ
License: other
Created: 2023-05-12T23:53:30.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-11-22T16:54:08.000Z (29 days ago)
Last Synced: 2024-11-24T20:50:27.881Z (27 days ago)
Topics: ai, api, chatbot, chatgpt, database, docker, framework, frontend, groq, html, javascript, llm, openai, postgresql, privacy, rag, react, security, typescript, vector
Language: Python
Homepage: https://core.quivr.com
Size: 122 MB
Stars: 36,730
Watchers: 283
Forks: 3,591
Open Issues: 138
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: .github/FUNDING.yml
- License: LICENSE

Awesome Lists containing this project

awesome-ChatGPT-repositories - quivr - Your GenAI Second Brain 🧠 A personal productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic, VertexAI, Ollama, LLMs, that you can share with users ! Local & Private alternative to OpenAI GPTs & ChatGPT powered by retrieval-augmented generation. (Chatbots)

README

        # Quivr - Your Second Brain, Empowered by Generative AI



    



[![Discord Follow](https://dcbadge.vercel.app/api/server/HUpRgp2HG8?style=flat)](https://discord.gg/HUpRgp2HG8)

[![GitHub Repo stars](https://img.shields.io/github/stars/quivrhq/quivr?style=social)](https://github.com/quivrhq/quivr)

[![Twitter Follow](https://img.shields.io/twitter/follow/StanGirard?style=social)](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](https://github.com/quivrhq/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](https://core.quivr.com/).

### 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

![](docs/docs/workflows/examples/basic_rag.excalidraw.png)

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

1. Add your API Keys to your environment variables

```python

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

```yaml

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"]

# Maximum number of previous conversation iterations

# to include in the context of the answer

max_history: 10

# Reranker configuration

reranker_config:

  # The reranker supplier to use

  supplier: "cohere"

  # The model to use for the reranker for the given supplier

  model: "rerank-multilingual-v3.0"

  # Number of chunks returned by the reranker

  top_n: 5

# Configuration for the LLM

llm_config:

  # maximum number of tokens passed to the LLM to generate the answer

  max_input_tokens: 4000

  # temperature for the LLM

  temperature: 0.7

```

3. Create a Brain with the default configuration

```python

from quivr_core import Brain

brain = Brain.from_files(name = "my smart brain",

                        file_paths = ["./my_first_doc.pdf", "./my_second_doc.txt"],

                        )

```

4. Launch a Chat

```python

brain.print_info()

from rich.console import Console

from rich.panel import Panel

from rich.prompt import Prompt

from quivr_core.config import RetrievalConfig

config_file_name = "./basic_rag_workflow.yaml"

retrieval_config = RetrievalConfig.from_yaml(config_file_name)

console = Console()

console.print(Panel.fit("Ask your brain !", style="bold magenta"))

while True:

    # Get user input

    question = Prompt.ask("[bold cyan]Question[/bold cyan]")

    # Check if user wants to exit

    if question.lower() == "exit":

        console.print(Panel("Goodbye!", style="bold yellow"))

        break

    answer = brain.ask(question, retrieval_config=retrieval_config)

    # Print the answer with typing effect

    console.print(f"[bold green]Quivr Assistant[/bold green]: {answer.answer}")

    console.print("-" * console.width)

brain.print_info()

```

5. You are now all set up to talk with your brain and test different retrieval strategies by simply changing the configuration file!

## Go further

You can go further with Quivr by adding internet search, adding tools, etc. Check the [documentation](https://core.quivr.com/) 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](https://github.com/users/StanGirard/projects/5) to see what we're currently focused on, and feel free to bring your fresh ideas to the table!

- [Open Issues](https://github.com/quivrhq/quivr/issues)

- [Open Pull Requests](https://github.com/quivrhq/quivr/pulls)

- [Good First Issues](https://github.com/quivrhq/quivr/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)

## Partners ❤️

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



    





  



## License 📄

This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details