README.md

metadata

title: Agent Course Test
emoji: 🦀
colorFrom: green
colorTo: yellow
sdk: gradio
sdk_version: 5.29.1
app_file: app.py
pinned: false
license: mit
short_description: Agent model to obtain huggingface agent course cert
hf_oauth: true
hf_oauth_expiration_minutes: 480

Script to setting up tables and vector search "CREATE EXTENSION IF NOT EXISTS vector;" """ CREATE TABLE IF NOT EXISTS documents2 ( id BIGINT PRIMARY KEY, content TEXT, metadata JSONB, embedding VECTOR(1536) ); """

create or replace function match_documents_2( query_embedding vector(1536), match_count int default 5 ) returns table ( id bigint, content text, metadata jsonb, embedding vector ) language sql as $$ select id, content, metadata, embedding from documents2 order by embedding <#> query_embedding limit match_count; $$;