* feat(v2): loaders added * feature: Add scroll animations * feature: upload ui * feature: upload multiple files * fix: Same file name and size remove * feat(crawler): added * feat(parsers): v2 added more * feat(v2): audio now working * feat(v2): all loaders * feat(v2): explorer * chore: add links * feat(api): added status in return message * refactor(website): remove old code * feat(upload): return type for messages * feature: redirect to upload if ENV=local * fix(chat): fixed some issues * feature: respect response type * loading state * feature: Loading stat * feat(v2): added explore and chat pages * feature: modal settings * style: Chat UI * feature: scroll to bottom when chatting * feature: smooth scroll in chat * feature(anim): Slide chat in * feature: markdown chat * feat(explorer): list * feat(doc): added document item * feat(explore): added modal * Add clarification on Project API keys and web interface for migration scripts to Readme (#58) * fix(demo): changed link * add support to uploading zip file (#62) * Catch UnicodeEncodeError exception (#64) * feature: fixed chatbar * fix(loaders): missing argument * fix: layout * fix: One whole chatbox * fix: Scroll into view * fix(build): vercel issues * chore(streamlit): moved to own file * refactor(api): moved to backend folder * feat(docker): added docker compose * Fix a bug where langchain memories were not being cleaned (#71) * Update README.md (#70) * chore(streamlit): moved to own file * refactor(api): moved to backend folder * docs(readme): updated for new version * docs(readme): added old readme * docs(readme): update copy dot env file * docs(readme): cleanup --------- Co-authored-by: iMADi-ARCH <nandanaditya985@gmail.com> Co-authored-by: Matt LeBel <github@lebel.io> Co-authored-by: Evan Carlson <45178375+EvanCarlson@users.noreply.github.com> Co-authored-by: Mustafa Hasan Khan <65130881+mustafahasankhan@users.noreply.github.com> Co-authored-by: zhulixi <48713110+zlxxlz1026@users.noreply.github.com> Co-authored-by: Stanisław Tuszyński <stanislaw@tuszynski.me>
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Quivr
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/19614572/80721777-2313-468f-b75e-09379f694653
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
Make sure you have the following installed before continuing:
- Python 3.10 or higher
- Pip
- Virtualenv
You'll also need a Supabase account for:
- A new Supabase project
- Supabase Project API key
- Supabase Project 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
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
)
-- 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
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
streamlit run main.py
Built With
- NextJS - The React framework used.
- FastAPI - The API framework used.
- Supabase - The open source Firebase alternative.
Contributing
Open a pull request and we'll review it as soon as possible.