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README.md
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README.md
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# Quivr - Your GenerativeAI Second Brain
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# Quivr - Your Second Brain, Empowered by Generative AI
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<p align="center">
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<img src="./logo.png" alt="Quivr-logo" width="30%">
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<p align="center">
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<div align="center">
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<img src="./logo.png" alt="Quivr-logo" width="30%" style="border-radius: 50%; padding-bottom: 20px"/>
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</div>
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<div align="center">
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<a href="https://discord.gg/HUpRgp2HG8">
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<img src="https://img.shields.io/badge/discord-join%20chat-blue.svg" alt="Join our Discord" height="40">
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</a>
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</div>
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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.
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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.
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## Key Features 🎯
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## Features
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- **Universal Data Acceptance**: Quivr can handle almost any type of data you throw at it. Text, images, code snippets, we've got you covered.
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- **Generative AI**: Quivr employs advanced AI to assist you in generating and retrieving information.
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- **Fast and Efficient**: Designed with speed and efficiency at its core. Quivr ensures rapid access to your data.
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- **Secure**: Your data, your control. Always.
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- **File Compatibility**:
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- Text
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- Markdown
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- PDF
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- Powerpoint
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- Excel
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- Word
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- Audio
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- Video
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- **Open Source**: Freedom is beautiful, so is Quivr. Open source and free to use.
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- **Store Anything**: Quivr can handle almost any type of data you throw at it. Text, images, code snippets, you name it.
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- **Generative AI**: Quivr uses advanced AI to help you generate and retrieve information.
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- **Fast and Efficient**: Designed with speed and efficiency in mind. Quivr makes sure you can access your data as quickly as possible.
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- **Secure**: Your data is always under your control.
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- **Compatible Files**:
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- **Text**
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- **Markdown**
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- **PDF**
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- **Powerpoint**
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- **Excel**
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- **Word**
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- **Audio**
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- **Video**
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- **Open Source**: Quivr is open source and free to use.
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## Demo Highlights 🎥
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> **Please note: The DEMO WITH STREAMLIT IS USING AN OLD VERSION**
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> The new version showcases a fresh UI, however, it is not yet deployed as it lacks some features of the old version. Expected to go live before 25/05/23
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### DEMO WITH STREAMLIT IS USING OLD VERSION
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New version is using a new UI and is not yet deployed as it doesn't have all the features of the old version.
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Should be up and live before 25/05/23
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### Demo with GPT3.5
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### **Demo with GPT3.5**:
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https://github.com/StanGirard/quivr/assets/19614572/80721777-2313-468f-b75e-09379f694653
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### **Demo with Claude 100k context**: https://github.com/StanGirard/quivr/assets/5101573/9dba918c-9032-4c8d-9eea-94336d2c8bd4
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### Demo with Claude 100k context
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https://github.com/StanGirard/quivr/assets/5101573/9dba918c-9032-4c8d-9eea-94336d2c8bd4
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### Demo of new version - WIP
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### **Demo of the new version (Work in progress)**:
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https://github.com/StanGirard/quivr/assets/19614572/a6463b73-76c7-4bc0-978d-70562dca71f5
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## Getting Started: New Version 🚀
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Follow these instructions to get a copy of the project up and running on your local machine for development and testing purposes.
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## Getting Started with the new version
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These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
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*Old version readme is in the streamlit-demo folder* [here](streamlit-demo/README.md)
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**Old version's readme** is located in the streamlit-demo folder [here](streamlit-demo/README.md)
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### Prerequisites
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### Prerequisites 📋
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Make sure you have the following installed before continuing:
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Before you proceed, ensure you have the following installed:
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- Docker
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- Docker Compose
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You'll also need a [Supabase](https://supabase.com/) account for:
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Additionally, you'll need a [Supabase](https://supabase.com/) account for:
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- A new Supabase project
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- Creating a new Supabase project
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- Supabase Project API key
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- Supabase Project URL
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### Installing
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### Installation Steps 💽
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- Clone the repository by running **one** of these commands:
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- **Step 1**: Clone the repository using **one** of these commands:
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1. If you don't have an SSH key set up:
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- If you don't have an SSH key set up:
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```bash
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git clone https://github.com/StanGirard/Quivr.git && cd Quivr
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```
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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)
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- If you have an SSH key set up or want to add it ([guide here](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account))
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```bash
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git clone git@github.com:StanGirard/Quivr.git && cd Quivr
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```
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- Copy the `.XXXXX_env` files
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- **Step 2**: Copy the `.XXXXX_env` files
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```bash
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cp .backend_env.example .backend_env
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cp .frontend_env.example .frontend_env
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```
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- Update the `.backend_env` file
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- **Step 3**: Update the `.backend_env` file
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_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._
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> _Your `supabase_service_key` can be found in your Supabase dashboard under Project Settings -> API. Use the `anon` `public` key found in the `Project API keys` section._
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- **Step 4**: Run the following migration scripts on the Supabase database via the web interface (SQL Editor -> `New query`)
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- Run the following migration scripts on the Supabase database via the web interface (SQL Editor -> `New query`)
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[Migration Script 1](scripts/supabase_vector_store_document.sql)
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```sql
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-- Enable the pgvector extension to work with embedding vectors
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create extension vector;
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[Migration Script 2](scripts/supabase_usage_table.sql)
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-- Create a table to store your documents
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create table documents (
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id bigserial primary key,
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content text, -- corresponds to Document.pageContent
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metadata jsonb, -- corresponds to Document.metadata
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embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
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);
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CREATE FUNCTION match_documents(query_embedding vector(1536), match_count int)
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RETURNS TABLE(
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id bigint,
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content text,
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metadata jsonb,
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-- we return matched vectors to enable maximal marginal relevance searches
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embedding vector(1536),
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similarity float)
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LANGUAGE plpgsql
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AS $$
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# variable_conflict use_column
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BEGIN
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RETURN query
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SELECT
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id,
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content,
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metadata,
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embedding,
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1 -(documents.embedding <=> query_embedding) AS similarity
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FROM
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documents
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ORDER BY
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documents.embedding <=> query_embedding
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LIMIT match_count;
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END;
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$$;
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```
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and
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```sql
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create table
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stats (
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-- A column called "time" with data type "timestamp"
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time timestamp,
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-- A column called "details" with data type "text"
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chat boolean,
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embedding boolean,
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details text,
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metadata jsonb,
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-- An "integer" primary key column called "id" that is generated always as identity
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id integer primary key generated always as identity
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);
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```
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- Run the app
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- **Step 5**: Launch the app
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```bash
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docker compose build && docker compose up
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```
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## Built With
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- **Step 6**: Navigate to `localhost:3000` in your browser
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* [Python](https://www.python.org/) - The programming language used.
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* [Supabase](https://supabase.io/) - The open source Firebase alternative.
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## Contributing
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Open a pull request and we'll review it as soon as possible.
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You can find all the subject we would like to tackle here -> https://github.com/users/StanGirard/projects/5
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## Contributors ✨
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Don't hesitate to come with new ones too :)
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Thanks goes to these wonderful people:
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<a href="https://github.com/stangirard/quivr/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=stangirard/quivr" />
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</a>
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## Contribute 🤝
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Got 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!
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- [Roadmap](https://github.com/users/StanGirard/projects/5)
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- [Open Issues](https://github.com/StanGirard/quivr/issues)
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- [Open Pull Requests](https://github.com/StanGirard/quivr/pulls)
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- [Good First Issues](https://github.com/StanGirard/quivr/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)
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- [Frontend Issues](https://github.com/StanGirard/quivr/issues?q=is%3Aopen+is%3Aissue+label%3Afrontend)
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- [Backend Issues](https://github.com/StanGirard/quivr/issues?q=is%3Aopen+is%3Aissue+label%3Abackend)
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## Star History 🌟
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## Star History
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[](https://star-history.com/#StanGirard/quivr&Date)
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12
scripts/supabase_usage_table.sql
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12
scripts/supabase_usage_table.sql
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create table
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stats (
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-- A column called "time" with data type "timestamp"
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time timestamp,
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-- A column called "details" with data type "text"
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chat boolean,
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embedding boolean,
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details text,
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metadata jsonb,
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-- An "integer" primary key column called "id" that is generated always as identity
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id integer primary key generated always as identity
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);
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36
scripts/supabase_vector_store_document.sql
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36
scripts/supabase_vector_store_document.sql
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create extension vector;
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-- Create a table to store your documents
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create table documents (
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id bigserial primary key,
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content text, -- corresponds to Document.pageContent
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metadata jsonb, -- corresponds to Document.metadata
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embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed
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);
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CREATE FUNCTION match_documents(query_embedding vector(1536), match_count int)
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RETURNS TABLE(
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id bigint,
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content text,
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metadata jsonb,
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-- we return matched vectors to enable maximal marginal relevance searches
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embedding vector(1536),
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similarity float)
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LANGUAGE plpgsql
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AS $$
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# variable_conflict use_column
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BEGIN
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RETURN query
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SELECT
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id,
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content,
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metadata,
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embedding,
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1 -(documents.embedding <=> query_embedding) AS similarity
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FROM
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documents
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ORDER BY
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documents.embedding <=> query_embedding
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LIMIT match_count;
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END;
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$$;
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