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feat(docs): reworked the website (QuivrHQ#1523)
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StanGirard authored Oct 30, 2023
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8 changes: 8 additions & 0 deletions docs/docs/Developers/_category_.json
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{
"label": "🧑‍💻 Developer Docs",
"position": 3,
"link": {
"type": "generated-index",
"description": "How to use Quivr as a Dev ?"
}
}
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{
"label": "Quivr's Brain",
"position": 3,
"label": "API",
"position": 1,
"link": {
"type": "generated-index",
"description": "How does the backend works?"
}
}
}
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Expand Up @@ -43,7 +43,7 @@ Users can create multiple chat sessions, each with its own set of chat messages.
- Description: This endpoint allows adding a new question to a chat. It generates an answer for the question using different models based on the provided model type.

Models like gpt-4-0613 and gpt-3.5-turbo-0613 use a custom OpenAI function-based answer generator.
![Function based answer generator](../../../static/img/answer_schema.png)
![Function based answer generator](../../../../static/img/answer_schema.png)

6. **Get the chat history:**
- HTTP method: GET
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# Introduction to Chains

Quivr uses a framework called [Langchain](https://python.langchain.com/docs/get_started/introduction.html) for the majority of the interaction with the Large Language Models (LLMs).
Quivr uses a framework called [Langchain](https://python.langchain.com/docs/introduction.html) for the majority of the interaction with the Large Language Models (LLMs).

Langchain provides functionality to connect multiple components such as LLMs, document retrievers, and other components together to form a "chain" of components.

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Expand Up @@ -31,5 +31,5 @@ Authorization: Bearer {api_key}

Replace `{api_key}` with the generated API key obtained from the frontend

You can find more information in the [Authentication](/docs/backend/api/getting_started) section of the documentation.
You can find more information in the [Authentication](/docs/Developers/backend/api/getting_started) section of the documentation.

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{
"label": "Quivr's Face",
"position": 4,
"label": "Frontend",
"position": 2,
"link": {
"type": "generated-index"
}
}
}
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{
"label": "LLM",
"position": 2,
"position": 3,
"link": {
"type": "generated-index",
"description": "How does the LLM (Large Language Model Work)?"
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36 changes: 36 additions & 0 deletions docs/docs/Developers/llm/private-llm.md
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---
sidebar_position: 1
title: Hugging Face Integration 🤗
---

# Private Language Models with Quivr

Quivr introduces the groundbreaking feature of integrating private Large Language Models (LLMs) powered by HuggingFace. This enhancement ensures your data's confidentiality, as all processing is performed locally on your server.

## Running Mistral with Huggingface Inference Endpoint

### 1. Deploy the Model
- Navigate to the [Mistral AI model page](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on Huggingface.
- Select the option for 'Inference Endpoints'.
- Please note that we recommend the Mistral 7B Instruct model, especially tailored for chat applications.

### 2. Create the Endpoint
- Feel free to assign a custom name to your endpoint.
- Select a location and adhere to the recommended instance size.
- Click to confirm and create your endpoint.

### 3. Obtain Credentials
- Allow some time for your instance to initialize.
- Securely copy both the API URL and your Bearer Token for future use.

### 4. Install Quivr
- To set up Quivr, kindly follow the concise 3-step installation guide provided in our [readme.md](https://github.com/Quivr/README.md).
- Important: Configure environmental variables in your backend/.env file, including the Huggingface API key for seamless integration.

### 5. Configure Your Supabase Instance
- Within your Supabase instance, locate the user_settings table.
- Here, input the following path: "huggingface/mistralai/Mistral-7B-Instruct-v0.1".

As a result, you'll have Quivr running locally with Mistral, now hosted via Huggingface. For those interested in a hassle-free experience, visit [Quivr.app](https://quivr.app) to leverage Mistral at no cost, all thanks to Huggingface. The source code for this setup is [available here](https://github.com/Quivr/SourceCode).

Experience the enhanced privacy and control with Quivr's Private LLM feature today!
3 changes: 2 additions & 1 deletion docs/docs/Reference/_category_.json
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{
"position": 4
"position": 4,
"label": "📚 Reference"
}
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{
"label": "Getting Started",
"position": 1,
"label": "🕺 User Guide",
"position": 2,
"link": {
"type": "generated-index",
"description": "How to start using Quivr"
}
}
}
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34 changes: 22 additions & 12 deletions docs/docs/contributions.md
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---
sidebar_position: 4
title: 🆘 Contributing
---
# Contributing to Quivr

Thanks for your interest in contributing to Quivr! Here you'll find guidelines for contributing and steps on how you can contribute.

## Table of Contents

- [Community](#community)
- [Roadmap](#roadmap-and-issues)
- [How to Contribute](#how-to-contribute)
- [Reporting Bugs](#reporting-bugs)
- [Feature Requests](#feature-requests)
- [Code Contributions](#code-contributions)
- [Submission Guidelines](#submission-guidelines)
- [Coding Rules](#coding-rules)
- [Frontend Guidelines](#frontend-guidelines)
- [Backend Guidelines](#backend-guidelines)
- [Making a Pull Request](#making-a-pull-request)
- [Contact](#contact)
- [Contributing to Quivr](#contributing-to-quivr)
- [Table of Contents](#table-of-contents)
- [Community](#community)
- [Roadmap and Issues](#roadmap-and-issues)
- [How to Contribute](#how-to-contribute)
- [Reporting Bugs](#reporting-bugs)
- [Feature Requests](#feature-requests)
- [Code Contributions](#code-contributions)
- [Submission Guidelines](#submission-guidelines)
- [Coding Rules](#coding-rules)
- [Frontend Guidelines](#frontend-guidelines)
- [Coding Conventions](#coding-conventions)
- [Testing](#testing)
- [Backend Guidelines](#backend-guidelines)
- [Coding Conventions](#coding-conventions-1)
- [Testing](#testing-1)
- [Making a Pull Request](#making-a-pull-request)
- [Contact](#contact)

## Community

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34 changes: 34 additions & 0 deletions docs/docs/intro.md
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---
sidebar_position: 1
title: 🚀 Welcome to Quivr
---

# Intro

Quivr, your second brain, utilizes the power of GenerativeAI to store and retrieve unstructured information. Think of it as Obsidian, but turbocharged with AI capabilities.

## Key Features 🎯

- **Universal Data Acceptance**: Quivr can handle almost any type of data you throw at it. Text, images, code snippets, we've got you covered.
- **Generative AI**: Quivr employs advanced AI to assist you in generating and retrieving information.
- **Fast and Efficient**: Designed with speed and efficiency at its core. Quivr ensures rapid access to your data.
- **Secure**: Your data, your control. Always.
- **File Compatibility**:
- Text
- Markdown
- PDF
- Powerpoint
- Excel
- Word
- Audio
- Video
- **Open Source**: Freedom is beautiful, so is Quivr. Open source and free to use.

## Demo Highlights 🎥

### **Demo**:

<video width="640" height="480" controls>
<source src="https://github.com/StanGirard/quivr/assets/19614572/a6463b73-76c7-4bc0-978d-70562dca71f5" type="video/mp4"/>
Your browser does not support the video tag.
</video>
2 changes: 1 addition & 1 deletion docs/docs/privacy_policy.md
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---
sidebar_position: 6
title: Privacy Policy
title: 👀 Privacy Policy
---

## Privacy Policy for Quivr
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