README.md

metadata

title: Template Final Assignment
emoji: 🕵🏻‍♂️
colorFrom: indigo
colorTo: indigo
sdk: gradio
sdk_version: 5.25.2
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_expiration_minutes: 480

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

1. Setup Supabase for Vector Store, following run this SQL Editor

• enable vector extension;
• create documents table;
• create match_documents_langchain function.

-- 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 works for OpenAI embeddings, change if needed
);

-- Create a function to search for documents
create function match_documents_langchain (
  query_embedding vector,
  match_count int default null,
  filter jsonb DEFAULT '{}'
) returns table (
  id bigint,
  content text,
  metadata jsonb,
  similarity float
)
language plpgsql
as $$
#variable_conflict use_column
begin
  return query
  select
    id,
    content,
    metadata,
    1 - (documents.embedding <=> query_embedding) as similarity
  from documents
  where metadata @> filter
  order by documents.embedding <=> query_embedding
  limit match_count;
end;
$$;

2. Setup Supabase API Key

export SUPABASE_URL=https://upkfycxsetvrlochkrti.supabase.co
export SUPABASE_SERVICE_KEY=

3. Setup Google Gemini API Key, or Groq Cloud

# Google Gemini
export GOOGLE_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxx_xxxxxxxx
# Groq Cloud
export GROQ_API_KEY=gsk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx