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| 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 $$ | |
| select id, content, metadata, embedding | |
| from documents2 | |
| order by embedding <#> query_embedding | |
| limit match_count; | |
| $$; |