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
Go to file
2023-05-14 21:12:30 +02:00
.streamlit Support for Anthropics Models 2023-05-14 01:30:03 -07:00
.vscode Support for Anthropics Models 2023-05-14 01:30:03 -07:00
loaders feat(metadata): added file size 2023-05-13 01:12:51 +02:00
website feat(website): first iteration 2023-05-14 21:12:30 +02:00
.gitignore feat(website): first iteration 2023-05-14 21:12:30 +02:00
2023-05-13-02-16-02.png feat(demo): added 2023-05-13 02:16:41 +02:00
brain.py feat(forget): now able to forget things 2023-05-13 01:30:00 +02:00
Dockerfile fix(requirements): fixed the issue 2023-05-13 16:37:18 +02:00
files.py feat(pdf): added pdf loader 2023-05-13 00:25:12 +02:00
LICENSE feat(license): added 2023-05-13 18:12:35 +02:00
logo.png feat(readme): first iteration 2023-05-13 02:02:45 +02:00
main.py Merge branch 'main' into support-anthropic-100k 2023-05-14 12:40:37 +02:00
question.py Support for Anthropics Models 2023-05-14 01:30:03 -07:00
README.md Update README.md 2023-05-13 19:56:54 +02:00
requirements.txt Support for Anthropics Models 2023-05-14 01:30:03 -07:00
sidebar.py feat(visual): moved things around 2023-05-12 23:58:19 +02:00
utils.py feat(init): init repository 2023-05-12 23:05:31 +02:00

Quiver

quiver-logo

Quiver 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: Quiver can handle almost any type of data you throw at it. Text, images, code snippets, you name it.
  • Generative AI: Quiver uses advanced AI to help you generate and retrieve information.
  • Fast and Efficient: Designed with speed and efficiency in mind. Quiver 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: Quiver is open source and free to use.

Demo

https://github.com/StanGirard/quiver/assets/19614572/a3cddc6a-ca28-44ad-9ede-3122fa918b51

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/quiver.git & cd quiver
  • 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"
  • 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.