Agent_Course_Eval / README.md
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created vector store in Supabase
dbdcb6d
---
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 $$
select id, content, metadata, embedding
from documents2
order by embedding <#> query_embedding
limit match_count;
$$;