Skip to content

GradientFlow-ai/eaas

Repository files navigation

Gradients, Flowing To a Neighborhood Near You

Liberty and Embeddings for All

coyotespike Twitter follower count coyotespike repo star count

Introduction · Tech Stack + Features ·


Introduction

GradientFlow is a community-driven collection of open-source embeddings, ready for you to train your AI, perform multimodal semantic search, and fine-tune.

Tech Stack + Features

The below may change soon as we rebuild on Akash Network.

Frameworks

Platforms

UI

Code Quality

Miscellaneous

Useful tips

npx prisma db push, be aware that this will drop and recreate the tables npx prisma generate then runs to generate the client

This function lives on supabase:

create or replace function match_documents (
  query_embedding vector(1536),
  similarity_threshold float,
  match_count int
)
returns table (
  id int4,
  content text,
  similarity float
)
language plpgsql
as $$
begin
  return query
  select
    "Documents".id,
    "Documents".content,
    (1 - ("Documents".embedding <=> query_embedding)) as similarity
  from "Documents"
  where (1 - ("Documents".embedding <=> query_embedding)) > similarity_threshold
  order by "Documents".embedding <=> query_embedding
  limit match_count;
end;
$$;

-- you can call the function from your browser with supabase-js
-- const { data, error } = await supabase.rpc('match_documents', { query_embedding: [], similarity_threshold: 0.5, match_count: 10 })
-- <->	Euclidean distance
-- <#>	negative inner product
-- <=>	cosine distance
-- OpenAI recommends cosine similarity on their embeddings, so we will use that here.

Issue with docs filed

To Dos

- configure policies for supa
- Convert dashboard [ from Bootstrap to Tailwind ](https://tools.bitfertig.de/bootstrap2tailwind/index.php)
- Add [ Tailwind styled components ](https://github.com/MathiasGilson/tailwind-styled-component)