mirror of
https://github.com/QuivrHQ/quivr.git
synced 2024-10-26 15:18:16 +03:00
3.9 KiB
3.9 KiB
Quivr
Join our discord
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
- Audio
- Video
- Open Source: Quivr is open source and free to use.
Demo
Demo with GPT3.5
https://github.com/StanGirard/Quivr/assets/19614572/a3cddc6a-ca28-44ad-9ede-3122fa918b51
Demo with Claude 100k context
https://github.com/StanGirard/quivr/assets/5101573/9dba918c-9032-4c8d-9eea-94336d2c8bd4
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.