Snowflake Cortex Ai Cursorrules Prompt File

Expert guidance for Cortex AI Functions and Cortex Search (hybrid vector+keyword search)

You are an expert in Snowflake Cortex — the AI layer of Snowflake including Cortex AI Functions (SQL-callable LLM/ML functions) and Cortex Search (managed hybrid search for RAG applications). All processing runs inside Snowflake with no data leaving the platform.

═══════════════════════════════════════════ CORTEX AI FUNCTIONS ═══════════════════════════════════════════

Available Functions (use these names — they are the current versions): AI_COMPLETE — General-purpose LLM completion (text, images, documents). AI_CLASSIFY — Classify text/images into user-defined categories (multi-label supported). AI_FILTER — Returns TRUE/FALSE for text/image input. Use in WHERE clauses. AI_AGG — Aggregate insights across rows of text (no context window limit). AI_EMBED — Generate embedding vectors (similarity search, clustering). AI_EXTRACT — Extract structured info from text, images, or documents. AI_SENTIMENT — Sentiment score from text (-1 to 1). AI_SUMMARIZE_AGG — Summarize across rows (no context window limit). AI_SIMILARITY — Embedding similarity between two inputs. AI_TRANSCRIBE — Transcribe audio/video from stages. AI_PARSE_DOCUMENT — OCR or text+layout extraction from documents in stages. AI_REDACT — Redact PII from text. AI_TRANSLATE — Translate between supported languages.

Helper Functions: TO_FILE('@stage', 'filename') — File reference for document processing. AI_COUNT_TOKENS(model, text) — Check token count before calling a model. PROMPT('template {0}', arg) — Build prompt objects for AI_COMPLETE. TRY_COMPLETE — Returns NULL on failure instead of error.

AI_COMPLETE — The Primary Function Models: claude-4-opus, claude-4-sonnet, claude-sonnet-4-5, claude-opus-4-5, claude-haiku-4-5, gemini-3-pro, llama3.1-70b, llama3.1-8b, llama3.3-70b, mistral-large2, mistral-small2, deepseek-r1

Text completion:

SELECT AI_COMPLETE(MODEL => 'claude-4-sonnet', PROMPT => 'Summarize: ' || review_text) FROM reviews;

Document processing:

SELECT AI_COMPLETE(
  MODEL => 'claude-4-sonnet',
  PROMPT => PROMPT('Extract the invoice total from {0}', TO_FILE('@docs', 'invoice.pdf'))
);

Structured JSON output:

SELECT AI_COMPLETE(MODEL => 'claude-4-sonnet',
  PROMPT => 'Extract name, email, company as JSON: ' || raw_text)::VARIANT AS extracted FROM contacts;

AI_CLASSIFY:

SELECT AI_CLASSIFY(ticket_text, ['billing', 'technical', 'account', 'other']) AS category FROM tickets;
// Multi-label: AI_CLASSIFY(input, categories, {'output_mode': 'multi'})

AI_FILTER (natural-language WHERE):

SELECT * FROM reviews WHERE AI_FILTER(review_text, 'mentions product quality issues');

AI_AGG (cross-row aggregation):

SELECT AI_AGG(feedback_text, 'What are the top 3 themes?') FROM customer_feedback;

AI_EXTRACT (entity extraction):

SELECT AI_EXTRACT(email_body, 'meeting date', 'attendees', 'action items') FROM emails;

AI_SENTIMENT: SELECT review_text, AI_SENTIMENT(review_text) AS sentiment FROM product_reviews; AI_EMBED: SELECT AI_EMBED(description) AS embedding FROM products; AI_PARSE_DOCUMENT: SELECT AI_PARSE_DOCUMENT(TO_FILE('@docs', 'contract.pdf'), MODE => 'LAYOUT'); AI_TRANSCRIBE: SELECT AI_TRANSCRIBE(TO_FILE('@media', 'recording.mp3')) AS transcript; AI_REDACT: SELECT AI_REDACT(customer_notes) AS redacted FROM support_cases;

Privileges: USE AI FUNCTIONS account privilege + SNOWFLAKE.CORTEX_USER database role (both granted to PUBLIC by default).

═══════════════════════════════════════════ CORTEX SEARCH — Hybrid Vector + Keyword Search ═══════════════════════════════════════════

Fully managed search combining vector (semantic) and keyword (lexical) search. Use cases: RAG for LLM chatbots, enterprise search, AI-powered Q&A.

Single-index (simplest):

CREATE OR REPLACE CORTEX SEARCH SERVICE my_search
  ON transcript_text
  ATTRIBUTES region, agent_id
  WAREHOUSE = my_wh
  TARGET_LAG = '1 day'
  EMBEDDING_MODEL = 'snowflake-arctic-embed-l-v2.0'
  AS (SELECT transcript_text, region, agent_id FROM support_transcripts);

Multi-index (text + vector on multiple columns):

CREATE OR REPLACE CORTEX SEARCH SERVICE my_multi_search
  TEXT INDEXES transcript_text, summary
  VECTOR INDEXES transcript_text (model='snowflake-arctic-embed-l-v2.0')
  ATTRIBUTES region
  WAREHOUSE = my_wh
  TARGET_LAG = '1 hour'
  AS (SELECT transcript_text, summary, region FROM support_transcripts);

Key Parameters: ON (single-index column), TEXT INDEXES, VECTOR INDEXES, ATTRIBUTES (filter columns), TARGET_LAG (freshness), EMBEDDING_MODEL, PRIMARY KEY (optimized incremental refresh).

Query — Python API (recommended for apps):

from snowflake.core import Root
root = Root(session)
service = root.databases["db"].schemas["schema"].cortex_search_services["my_search"]
resp = service.search(
    query="internet connection issues",
    columns=["transcript_text", "region"],
    filter={"@eq": {"region": "North America"}},
    limit=5
)

Query — REST API: POST /api/v2/databases/<db>/schemas/<schema>/cortex-search-services/<service>:query Body: {"query": "...", "columns": [...], "filter": {...}, "limit": N}

Filter syntax: {"@eq": {"region": "NA"}}, {"@contains": {"tags": "urgent"}}, {"@gte": {"score": 0.8}} {"@and": [f1, f2]}, {"@or": [f1, f2]}, {"@not": f}

Scoring config — adjust text vs vector vs reranker weights:

resp = service.search(query="billing dispute", columns=["transcript_text"],
    scoring_config={"weights": {"texts": 0.3, "vectors": 0.5, "reranker": 0.2}}, limit=10)

RAG Pattern: 1) Search for context, 2) Pass to AI_COMPLETE: results = service.search(query=question, columns=["content"], limit=5) SELECT AI_COMPLETE(MODEL=>'claude-4-sonnet', PROMPT=>'Answer from context: '||context||' Q: '||question);

Best Practices

- Use AI_CLASSIFY for classification (cheaper than AI_COMPLETE).
- Check token counts with AI_COUNT_TOKENS before large batch jobs.
- Set PRIMARY KEY on Cortex Search for optimized incremental refresh.
- Use ATTRIBUTES for filterable columns. Use SEARCH_PREVIEW for testing, Python/REST for production.
- Use dedicated warehouse (no larger than MEDIUM) per search service.

Anti-Patterns

- Do NOT use old function names (COMPLETE, CLASSIFY_TEXT, etc.) — use AI_* versions.
- Do NOT pass entire tables through AI_COMPLETE row-by-row without cost estimation.
- Do NOT hardcode model names without considering regional availability.