quivr/README.md

# Quivr - Your GenerativeAI Second Brain

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<img src="./logo.png" alt="Quivr-logo" width="30%">
<p align="center">

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Quivr is your GenerativeAI second brain, designed to easily store and retrieve unstructured information. It's like Obsidian but powered by generative AI.


## Features

- **Store Anything**: Quivr can handle almost any type of data you throw at it. Text, images, code snippets, you name it.
- **Generative AI**: Quivr uses advanced AI to help you generate and retrieve information.
- **Fast and Efficient**: Designed with speed and efficiency in mind. Quivr makes sure you can access your data as quickly as possible.
- **Secure**: Your data is always under your control.
- **Compatible Files**: 
  - **Text**
  - **Markdown**
  - **PDF**
  - **Powerpoint**
  - **Excel**
  - **Word**
  - **Audio**
  - **Video**
- **Open Source**: Quivr is open source and free to use.




### DEMO WITH STREAMLIT IS USING OLD VERSION 
New version is using a new UI and is not yet deployed as it doesn't have all the features of the old version.
Should be up and live before 25/05/23

### Demo with GPT3.5
https://github.com/StanGirard/quivr/assets/19614572/80721777-2313-468f-b75e-09379f694653


### Demo with Claude 100k context
https://github.com/StanGirard/quivr/assets/5101573/9dba918c-9032-4c8d-9eea-94336d2c8bd4

### Demo of new version - WIP


https://github.com/StanGirard/quivr/assets/19614572/a6463b73-76c7-4bc0-978d-70562dca71f5



## Getting Started with the new version
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
*Old version readme is in the streamlit-demo folder* [here](streamlit-demo/README.md)

### Prerequisites

Make sure you have the following installed before continuing:

- Docker
- Docker Compose

You'll also need a [Supabase](https://supabase.com/) account for:

- A new Supabase project
- Supabase Project API key
- Supabase Project URL

### Installing

- Clone the repository by running **one** of these commands:

  1. If you don't have an SSH key set up:
  ```bash
  git clone https://github.com/StanGirard/Quivr.git && cd Quivr
  ```

  2. If you have an SSH key set up or want to add it (https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account)

  ```bash
  git clone git@github.com:StanGirard/Quivr.git && cd Quivr
  ```

- Copy the `.XXXXX_env` files

```bash
cp .backend_env.example .backend_env
cp .frontend_env.example .frontend_env
```

- Update the `.backend_env` file 

_Note that the `supabase_service_key` is found in your Supabase dashboard under Project Settings -> API. Use the `anon` `public` key found in the `Project API keys` section._


- Run the following migration scripts on the Supabase database via the web interface (SQL Editor -> `New query`)

```sql
-- Enable the pgvector extension to work with embedding vectors
       create extension vector;

       -- Create a table to store your documents
       create table documents (
       id bigserial primary key,
       content text, -- corresponds to Document.pageContent
       metadata jsonb, -- corresponds to Document.metadata
       embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
       );

       CREATE FUNCTION match_documents(query_embedding vector(1536), match_count int)
           RETURNS TABLE(
               id bigint,
               content text,
               metadata jsonb,
               -- we return matched vectors to enable maximal marginal relevance searches
               embedding vector(1536),
               similarity float)
           LANGUAGE plpgsql
           AS $$
           # variable_conflict use_column
       BEGIN
           RETURN query
           SELECT
               id,
               content,
               metadata,
               embedding,
               1 -(documents.embedding <=> query_embedding) AS similarity
           FROM
               documents
           ORDER BY
               documents.embedding <=> query_embedding
           LIMIT match_count;
       END;
       $$;
```

and 

```sql
create table
  stats (
    -- A column called "time" with data type "timestamp"
    time timestamp,
    -- A column called "details" with data type "text"
    chat boolean,
    embedding boolean,
    details text,
    metadata jsonb,
    -- An "integer" primary key column called "id" that is generated always as identity
    id integer primary key generated always as identity
  );
```

- Run the app

```bash
docker compose build && docker compose up
```

## Built With

* [Python](https://www.python.org/) - The programming language used.
* [Supabase](https://supabase.io/) - The open source Firebase alternative.

## Contributing

Open a pull request and we'll review it as soon as possible.
You can find all the subject we would like to tackle here -> https://github.com/users/StanGirard/projects/5

Don't hesitate to come with new ones too :) 

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=StanGirard/quivr&type=Date)](https://star-history.com/#StanGirard/quivr&Date)