Menghasilkan vector embedding 768 dimensi dari teks untuk semantic search.
VECTOR(FLOAT, 768)Diperbarui: 6 Jan 2026SNOWFLAKE.CORTEX.EMBED_TEXT_768(model, text)Model embedding: 'e5-base-v2', 'snowflake-arctic-embed-m'
Teks untuk di-embed
1 SELECT 2 document_id, 3 SNOWFLAKE.CORTEX.EMBED_TEXT_768('e5-base-v2', document_text) as embedding 4 FROM documents;
Generate embeddings untuk semantic search.
1 WITH query_embedding AS ( 2 SELECT SNOWFLAKE.CORTEX.EMBED_TEXT_768('e5-base-v2', 'machine learning tutorial') as vec 3 ) 4 SELECT 5 d.title, 6 VECTOR_COSINE_SIMILARITY(d.embedding, q.vec) as similarity 7 FROM documents d, query_embedding q 8 ORDER BY similarity DESC 9 LIMIT 10;
Semantic search menggunakan embeddings.
Sudah paham AI_EMBED_TEXT_768? Latih langsung di browser
Latihan interaktif, langsung di browser.