cortex-ai-functions

name	cortex-ai-functions
description	Guide to Snowflake Cortex AI functions — SQL-native LLM inference, text classification, extraction, sentiment analysis, embeddings, and document processing without infrastructure management

Snowflake Cortex AI Functions

SQL-native AI functions that bring LLM capabilities directly into your queries. No API keys, no infrastructure, no data movement — call AI on your data where it lives in Snowflake.

Official docs: Cortex AI Functions

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Quick Reference

Function	Purpose	Returns
AI_COMPLETE	Text generation / prompting	Generated text (VARCHAR)
AI_CLASSIFY	Zero-shot text classification	Label + confidence (OBJECT)
AI_EXTRACT	Entity extraction from text	Structured JSON (OBJECT)
AI_SENTIMENT	Sentiment scoring	FLOAT from -1 to 1
AI_SUMMARIZE	Text summarization	Summary text (VARCHAR)
AI_TRANSLATE	Language translation	Translated text (VARCHAR)
AI_EMBED	Vector embeddings	VECTOR(FLOAT, dim)
AI_FILTER	Boolean content filtering	TRUE / FALSE
AI_REDACT	PII redaction	Redacted text (VARCHAR)
AI_AGG	Aggregated AI analysis	Summary across rows (VARCHAR)
AI_PARSE_DOCUMENT	Document/PDF extraction	Structured content (OBJECT)

All functions live in the SNOWFLAKE.CORTEX schema.

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Text Generation — AI_COMPLETE

Generate text using hosted LLMs. The most versatile function.

Basic usage

SELECT SNOWFLAKE.CORTEX.AI_COMPLETE('claude-3-5-sonnet', 'Explain micro-partitions in one sentence.');

With system message and structured prompt

SELECT SNOWFLAKE.CORTEX.AI_COMPLETE(
    'claude-3-5-sonnet',
    [
        {'role': 'system', 'content': 'You are a concise SQL tutor. Reply in under 50 words.'},
        {'role': 'user', 'content': 'When should I use a clustering key?'}
    ]::ARRAY(OBJECT(role VARCHAR, content VARCHAR)),
    {'temperature': 0.3, 'max_tokens': 200}
);

Batch processing — run AI on every row

SELECT
    ticket_id,
    subject,
    SNOWFLAKE.CORTEX.AI_COMPLETE(
        'mistral-large2',
        'Classify this support ticket as billing, technical, or account. Reply with one word only.\n\n' || body
    ) AS category
FROM support_tickets
WHERE created_at >= CURRENT_DATE - 7;

Available models

Model	Best for
claude-3-5-sonnet	Complex reasoning, analysis, code
mistral-large2	General tasks, good cost/quality balance
llama3.1-70b	Open-source, fast, cost-effective
llama3.1-8b	Lightweight tasks, lowest cost

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Text Classification — AI_CLASSIFY

Zero-shot classification — no training data needed.

Single row

SELECT SNOWFLAKE.CORTEX.AI_CLASSIFY(
    'My invoice is wrong and I was charged twice',
    ['billing', 'technical_issue', 'account_access', 'feature_request']
);
-- Returns: {"label": "billing", "score": 0.92}

Batch — classify customer feedback

SELECT
    feedback_id,
    comment,
    SNOWFLAKE.CORTEX.AI_CLASSIFY(
        comment,
        ['positive', 'negative', 'neutral']
    ):label::VARCHAR AS sentiment_label,
    SNOWFLAKE.CORTEX.AI_CLASSIFY(
        comment,
        ['positive', 'negative', 'neutral']
    ):score::FLOAT AS confidence
FROM customer_feedback;

Content moderation

SELECT
    post_id,
    content,
    SNOWFLAKE.CORTEX.AI_CLASSIFY(
        content,
        ['safe', 'spam', 'offensive', 'off_topic']
    ):label::VARCHAR AS moderation_result
FROM user_posts
WHERE moderation_result != 'safe';

---

Entity Extraction — AI_EXTRACT

Pull structured data out of unstructured text.

Basic extraction

SELECT SNOWFLAKE.CORTEX.AI_EXTRACT(
    'Please contact John Smith at [e-mail] regarding order #12345 placed on March 15, 2025.',
    ['person_name', 'email', 'order_number', 'date']
);
-- Returns: {"person_name": "John Smith", "email": "[e-mail]", "order_number": "12345", "date": "March 15, 2025"}

Batch — extract from support tickets

SELECT
    ticket_id,
    body,
    SNOWFLAKE.CORTEX.AI_EXTRACT(body, ['product_name', 'error_code', 'urgency']):product_name::VARCHAR AS product,
    SNOWFLAKE.CORTEX.AI_EXTRACT(body, ['product_name', 'error_code', 'urgency']):error_code::VARCHAR AS error_code
FROM support_tickets;

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Sentiment Analysis — AI_SENTIMENT

Score text from -1 (negative) to 1 (positive).

Single value

SELECT SNOWFLAKE.CORTEX.AI_SENTIMENT('The product works great, very happy with my purchase!');
-- Returns: 0.89

Batch — analyze reviews with aggregation

SELECT
    product_id,
    COUNT(*) AS review_count,
    AVG(SNOWFLAKE.CORTEX.AI_SENTIMENT(review_text)) AS avg_sentiment,
    COUNT_IF(SNOWFLAKE.CORTEX.AI_SENTIMENT(review_text) < -0.3) AS negative_count
FROM product_reviews
GROUP BY product_id
ORDER BY avg_sentiment ASC;

---

Summarization — AI_SUMMARIZE

Condense long text into concise summaries.

SELECT SNOWFLAKE.CORTEX.AI_SUMMARIZE(article_body) AS summary
FROM news_articles
WHERE published_date = CURRENT_DATE;

Summarize support conversations

SELECT
    conversation_id,
    SNOWFLAKE.CORTEX.AI_SUMMARIZE(full_transcript) AS summary
FROM support_conversations
WHERE resolved_at >= CURRENT_DATE - 1;

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Translation — AI_TRANSLATE

Translate text between languages.

SELECT SNOWFLAKE.CORTEX.AI_TRANSLATE(
    'Snowflake es una plataforma de datos en la nube.',
    'es',   -- source language
    'en'    -- target language
);
-- Returns: "Snowflake is a cloud data platform."

Batch — translate product descriptions

SELECT
    product_id,
    description_es,
    SNOWFLAKE.CORTEX.AI_TRANSLATE(description_es, 'es', 'en') AS description_en,
    SNOWFLAKE.CORTEX.AI_TRANSLATE(description_es, 'es', 'fr') AS description_fr
FROM product_catalog
WHERE market = 'LATAM';

Supported languages include: en, es, fr, de, it, pt, ja, ko, zh, ru, pl, sv, and others. Check docs for the full list.

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Embeddings — AI_EMBED

Generate vector embeddings for semantic search and RAG pipelines.

Generate an embedding

SELECT SNOWFLAKE.CORTEX.AI_EMBED('e5-base-v2', 'Snowflake clustering keys improve query performance');

Semantic similarity search

-- Find documents similar to a query
SET search_query = 'How do I optimize warehouse costs?';

SELECT
    doc_id,
    title,
    VECTOR_COSINE_SIMILARITY(
        embedding,
        SNOWFLAKE.CORTEX.AI_EMBED('e5-base-v2', $search_query)
    ) AS similarity
FROM knowledge_base
ORDER BY similarity DESC
LIMIT 10;

Build an embedding column for RAG

ALTER TABLE knowledge_base ADD COLUMN embedding VECTOR(FLOAT, 768);

UPDATE knowledge_base
SET embedding = SNOWFLAKE.CORTEX.AI_EMBED('e5-base-v2', content);

Available embedding models

Model	Dimensions	Best for
e5-base-v2	768	General-purpose, English text
snowflake-arctic-embed-m-v1.5	768	Multilingual, retrieval-optimized

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Content Filtering — AI_FILTER

Boolean filter — does the text match a condition?

SELECT
    review_id,
    review_text
FROM product_reviews
WHERE SNOWFLAKE.CORTEX.AI_FILTER(review_text, 'mentions a competitor product');

---

PII Redaction — AI_REDACT

Remove personally identifiable information from text.

SELECT SNOWFLAKE.CORTEX.AI_REDACT(
    'Call me at 555-123-4567 or email [e-mail]'
);
-- Returns: "Call me at [PHONE_NUMBER] or email [EMAIL_ADDRESS]"

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Aggregated Analysis — AI_AGG

Summarize or analyze data across multiple rows.

SELECT SNOWFLAKE.CORTEX.AI_AGG(
    review_text,
    'What are the top 3 recurring complaints?'
) AS complaint_summary
FROM product_reviews
WHERE product_id = 'SKU-100';

---

Document Processing — AI_PARSE_DOCUMENT

Extract text and structure from PDFs and images stored in Snowflake stages.

Parse a PDF

SELECT SNOWFLAKE.CORTEX.AI_PARSE_DOCUMENT(
    @my_stage,
    'invoices/invoice_2025_001.pdf',
    {'mode': 'LAYOUT'}
);

Batch — process all PDFs in a stage

SELECT
    relative_path,
    SNOWFLAKE.CORTEX.AI_PARSE_DOCUMENT(
        @my_stage,
        relative_path,
        {'mode': 'LAYOUT'}
    ):content::VARCHAR AS extracted_text
FROM DIRECTORY(@my_stage)
WHERE relative_path LIKE '%.pdf';

Modes

Mode	Use case
LAYOUT	Preserves document structure (headers, tables, paragraphs)
OCR	Optical character recognition for scanned documents and images

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Best Practices

Use batch processing

Process entire tables, not row-by-row. Snowflake parallelizes across rows automatically.

-- Good: batch on a table
SELECT id, SNOWFLAKE.CORTEX.AI_SENTIMENT(text) FROM reviews;

-- Avoid: calling in a loop or procedurally

Cache AI outputs — don't recompute

Store results in columns instead of recomputing on every query.

ALTER TABLE reviews ADD COLUMN sentiment FLOAT;

UPDATE reviews
SET sentiment = SNOWFLAKE.CORTEX.AI_SENTIMENT(review_text)
WHERE sentiment IS NULL;

Choose the right model for the job

• High quality needed (analysis, code, reasoning): claude-3-5-sonnet
• Balanced workloads (classification, extraction): mistral-large2
• High volume / cost-sensitive: llama3.1-70b or llama3.1-8b
• Use the smallest model that produces acceptable results.

Use specialized functions over AI_COMPLETE when available

-- Prefer this (optimized, cheaper):
SELECT SNOWFLAKE.CORTEX.AI_SENTIMENT(text) FROM reviews;

-- Over this (general-purpose, more expensive):
SELECT SNOWFLAKE.CORTEX.AI_COMPLETE('claude-3-5-sonnet', 'Rate sentiment of: ' || text) FROM reviews;

Cost awareness

• AI functions consume credits based on tokens processed.
• Longer inputs = more tokens = higher cost.
• Truncate or pre-filter text when full content is not needed.
• Use WHERE clauses to limit rows processed.
• Monitor usage via SNOWFLAKE.ACCOUNT_USAGE.METERING_DAILY_HISTORY with SERVICE_TYPE = 'AI_SERVICES'.

Rate limits and concurrency

• Cortex functions have per-account rate limits.
• For large batch jobs, use a dedicated warehouse.
• Process in batches with LIMIT/OFFSET if hitting rate limits.
• Smaller models have higher throughput limits than larger models.