Cortex Search Tools

​

Overview

​

Tool Name

_cortex_search

​

Purpose

​

Functions Available

1. _cortex_search: Manages Cortex Search services for creating AI-powered semantic search over table data, executing natural language queries, and maintaining search indexes. Controlled by the action parameter.

​

Key Features

​

Input Parameters for Each Function

​

_cortex_search

​

Use Cases

1. Product Discovery Enable customers to find products using natural descriptions like “eco-friendly kitchen appliances” or “budget laptops for students.”
2. Document Retrieval Search company documentation, policies, or knowledge bases with queries like “remote work policies” or “data retention guidelines.”
3. Customer Support Find relevant help articles, FAQs, or past tickets based on problem descriptions rather than exact keywords.
4. Content Recommendation Match user interests to relevant articles, research papers, or media content based on semantic similarity.
5. Data Exploration Help analysts discover datasets or tables by searching descriptions and metadata with natural language.

​

Workflow/How It Works

1. Step 1: Identify Source Data Choose a Snowflake table with a text column rich enough for semantic search (descriptions, documents, summaries).
2. Step 2: Create Search Service Run create_service specifying the table_name, search_column, and metadata_columns to index.
   Copy

   Ask AI

   _cortex_search(
       action="create_service",
       service_name="product_catalog",
       table_name="products",
       search_column="description",
       metadata_columns=["product_id", "name", "category", "price"],
       warehouse="COMPUTE_WH"
   )
   

3. Step 3: Wait for Indexing Snowflake builds the vector index in the background. Use get_status to monitor progress.
4. Step 4: Query with Natural Language Execute searches using conversational queries:
   Copy

   Ask AI

   _cortex_search(
       action="query",
       service_name="product_catalog",
       query="wireless headphones with noise cancellation",
       limit=5
   )
   

5. Step 5: Refresh as Needed When source data changes, run refresh_service to update the index.
6. Step 6: Monitor and Maintain Use list_services and describe_service to track all search services and their configurations.

​

Integration Relevance

• data_connector_tools for exploring tables and identifying good search candidates.
• _query_database to prepare and clean source data before indexing.
• file_manager_tools to log search queries and results for analytics.
• project_manager_tools to track search service creation and maintenance tasks.
• image_tools to build search UIs with visual result previews.

​

Configuration Details

• Search Column Selection: Choose columns with substantive text (100+ characters ideal). Avoid short codes or categorical values.
• Metadata Columns: Include fields users need to see or filter on (IDs, names, categories, dates).
• Warehouse Sizing: Larger warehouses speed up initial indexing; small warehouses suffice for queries.
• Table Requirements: Source table must be in Snowflake (not external tables or views with complex logic).
• Refresh Strategy: Schedule refresh_service after ETL runs to keep index current.

​

Limitations or Notes

1. Snowflake Cortex Availability: Requires Snowflake account with Cortex features enabled.
2. Language Support: Optimized for English; other languages may have reduced accuracy.
3. Column Type Constraints: search_column must contain text (VARCHAR, STRING); cannot index binary or complex types.
4. Table Size: Very large tables (100M+ rows) may have longer indexing times and higher costs.
5. Query Complexity: Simple natural language works best; complex boolean logic or regex not supported.
6. No Partial Updates: Changes to source data require full refresh_service; incremental updates not available.
7. Result Ranking: Uses AI-based relevance; exact ranking algorithm not customizable.

​

Supported Actions

​

Not Supported

​

Output

• create_service: Confirmation with service name, indexed columns, and status.
• query: List of matching results with metadata, ranked by semantic relevance.
• list_services: Table of all services with names, source tables, and creation dates.
• describe_service: Detailed configuration including search column, metadata columns, and index statistics.
• refresh_service: Status update with refresh timestamp and row count.
• get_status: Health check with indexing progress, last refresh, and error messages if any.
• drop_service: Confirmation of service deletion.
• Errors: Structured messages with resolution hints (e.g., invalid column names, warehouse unavailable).

​

Best Practices

​

Example: Complete Product Search Setup

# 1. Create search service over product catalog
_cortex_search(
    action="create_service",
    service_name="product_search",
    table_name="RETAIL_DB.PRODUCTS.CATALOG",
    search_column="description",
    metadata_columns=["product_id", "name", "category", "price", "rating"],
    warehouse="COMPUTE_WH"
)

# 2. Check indexing status
_cortex_search(
    action="get_status",
    service_name="product_search"
)

# 3. Execute semantic search
results = _cortex_search(
    action="query",
    service_name="product_search",
    query="affordable wireless earbuds with long battery life",
    limit=10
)

# 4. Refresh after daily ETL
_cortex_search(
    action="refresh_service",
    service_name="product_search"
)