Quivr

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Quivr is your second brain in the cloud, 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 stored securely in the cloud and is always under your control.
• Compatible Files:
  • Text
  • Markdown
  • PDF
  • Audio
  • Video
• Open Source: Quivr is open source and free to use.

Demo

Demo with GPT3.5

Quiver.mp4

Demo with Claude 100k context

Quivr.webm

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

What things you need to install the software and how to install them.

• Python 3.10 or higher
• Pip
• Virtualenv
• Supabase account
• Supabase API key
• Supabase URL

Installing

• Clone the repository

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

• Create a virtual environment

virtualenv venv

• Activate the virtual environment

source venv/bin/activate

• Install the dependencies

pip install -r requirements.txt

• Copy the streamlit secrets.toml example file

cp .streamlit/secrets.toml.example .streamlit/secrets.toml

• Add your credentials to .streamlit/secrets.toml file

supabase_url = "SUPABASE_URL"
supabase_service_key = "SUPABASE_SERVICE_KEY"
openai_api_key = "OPENAI_API_KEY"
anthropic_api_key = "ANTHROPIC_API_KEY" # Optional

• Run the migration script on the Supabase database via the web interface

-- 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 $$
           # variable_conflict use_column
       BEGIN
           RETURN query
           SELECT
               id,
               content,
               metadata,
               embedding,
               1 -(documents.embedding <=> query_embedding) AS similarity
           FROM
               documents
           ORDER BY
               documents.embedding <=> query_embedding
           LIMIT match_count;
       END;
       $$;

• Run the app

streamlit run main.py

Built With

• Python - The programming language used.
• Streamlit - The web framework used.
• Supabase - The open source Firebase alternative.

Contributing

Open a pull request and we'll review it as soon as possible.

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