Using Snowflake Cortex for GenAI

Snowflake Cortex enables seamless integration of Generative AI (GenAI) capabilities within the Snowflake Data Cloud. It allows organizations to use pre-trained large language models (LLMs) and create applications for tasks like content generation, text summarization, sentiment analysis, and conversational AI — all without managing external ML infrastructure.

Prerequisites for Snowflake Cortex Setup

Snowflake Environment

Enterprise Edition or higher is required as a baseline for using advanced features like External Functions and Snowpark.

Cortex Licensing

Specific License: Snowflake Cortex requires an additional license or subscription. Ensure you have the Cortex license as part of your Snowflake.

External Integration and Data Preparation

• Set up secure API access to LLMs (e.g., OpenAI or Hugging Face) for embedding and text generation.
• Prepare clean data in Snowflake tables and configure networking for secure external function calls.

Key Features of Snowflake Cortex for GenAI

Pre-Trained LLMs

Access to pre-trained models for text processing and generation, like OpenAI’s GPT models or Snowflake's proprietary embeddings.

Text Embeddings

Generate high-dimensional vector embeddings from textual data for semantic search, clustering, and contextual understanding.

Vector Support

Native VECTOR data type to store embeddings, perform similarity comparisons, and optimize GenAI applications.

Integration With SQL

Leverage Cortex functions (e.g., EMBEDDINGS, MATCH, MATCH_SCORE) directly in SQL queries.

Use Case: Build a Product FAQ Bot With GenAI

Develop a GenAI-powered bot to answer product-related questions using Snowflake Cortex.

Step 1: Create a Knowledge Base Table

Start by storing your FAQs in Snowflake.

CREATE OR REPLACE TABLE product_faq (
    faq_id INT,
    question STRING,
    answer STRING,
    question_embedding VECTOR(768)
);

Step 2: Insert FAQ Data

Populate the table with sample questions and answers.

INSERT INTO product_faq (faq_id, question, answer)
VALUES
(1, 'How do I reset my password?', 'You can reset your password by clicking "Forgot Password" on the login page.'),
(2, 'What is your return policy?', 'You can return products within 30 days of purchase with a receipt.'),
(3, 'How do I track my order?', 'Use the tracking link sent to your email after placing an order.');

Step 3: Generate Question Embeddings

Generate vector embeddings for each question using Snowflake Cortex.

UPDATE product_faq
SET question_embedding = EMBEDDINGS('cortex_default', question);

What this does is: 

• Converts the question into a 768-dimensional vector using Cortex’s default LLM.
• Stores the vector in the question_embedding column.

Step 4: Query for Answers Using Semantic Search

When a user asks a question, match it to the most relevant FAQ in the database.

SELECT
    question,
    answer,
    MATCH_SCORE(question_embedding, EMBEDDINGS('cortex_default', 'How can I reset my password?')) AS relevance
FROM product_faq
ORDER BY relevance DESC
LIMIT 1;

Explanation

• The user’s query ('How can I reset my password?') is converted into a vector.
• MATCH_SCORE calculates the similarity between the query vector and FAQ embeddings.
• Returns the most relevant answer.

Step 5: Automate Text Generation

Use GenAI capabilities to auto-generate answers for uncovered queries.

SELECT
    GENERATE_TEXT('cortex_default', 'How do I update my email address?')
    AS generated_answer;

What this does is:

• Generates a text response for the query using the cortex_default LLM.
• Can be stored back in the FAQ table for future use.

Advanced Use Cases

Document Summarization

Summarize lengthy product manuals or policy documents for quick reference.

SELECT
    GENERATE_TEXT('cortex_default', 'Summarize: Return policy allows refunds within 30 days...')
    AS summary;

Personalized Recommendations

Combine vector embeddings with user preferences to generate personalized product recommendations.

SELECT
    product_name,
    MATCH_SCORE(product_embedding, EMBEDDINGS('cortex_default', 'Looking for lightweight gaming laptops')) AS relevance
FROM product_catalog
ORDER BY relevance DESC
LIMIT 3;

Chatbot Integration

Integrate Cortex-powered GenAI into chat applications using frameworks like Streamlit or API connectors.

Best Practices

Optimize Embedding Generation

• Use cleaned, concise text to improve embedding quality.
• Preprocess input text to remove irrelevant data.

Use VECTOR Indexes

Speed up similarity searches for large datasets:

CREATE VECTOR INDEX faq_index
USING cortex_default
ON product_faq (question_embedding)

Monitor Model Performance

• Track MATCH_SCORE to assess query relevance.
• Fine-tune queries or improve data quality for low-confidence results.

Secure Sensitive Data

Limit access to tables and embeddings containing sensitive or proprietary information.

Batch Processing for Scalability

Process embeddings and queries in batches for high-volume use cases.

Benefits of Snowflake Cortex for GenAI

No Infrastructure Overhead

Use pre-trained LLMs directly within Snowflake without managing external systems.

Seamless Integration

Combine GenAI capabilities with Snowflake’s data analytics features.

Scalability

Handle millions of embeddings or GenAI tasks with Snowflake’s scalable architecture.

Flexibility

Build applications like chatbots, recommendation engines, and content generators.

Cost-Effective

Leverage on-demand GenAI capabilities without investing in separate ML infrastructure.

Next Steps

• Extend: Add advanced use cases like multi-lingual support or real-time chat interfaces.
• Explore: Try other Cortex features like clustering, sentiment analysis, and real-time text generation.
• Integrate: Use external tools like Streamlit or Flask to build user-facing applications. 

Snowflake Cortex makes it easy to bring the power of GenAI into your data workflows. Whether you’re building a chatbot, summarizing text, or creating personalized recommendations, Cortex provides a seamless, scalable platform to achieve your goals.