Skip to main content

​
What is AI Search?

AI Search transforms how you find information by understanding the meaning and context of your queries, not just matching keywords. Instead of searching for exact text matches, AI Search uses embeddings to find semantically similar content, enabling more intuitive and capable search experiences.
Snowflake Cortex Requirement: AI Search is exclusively powered by Snowflake Cortex. You must have a Snowflake Cortex provider configured with embedding services. OpenAI and Gemini providers cannot be used for AI Search functionality.
Prerequisites: AI Search requires a Snowflake Cortex embedding service. See Snowflake Cortex Setup for configuration instructions.
Table Type Requirement: AI Search works exclusively with Standard Snowflake tables. Other table types are not supported for search functionality.

​
How AI Search Works

AI Search uses machine learning embeddings to understand the semantic meaning of text content:
1

Content Processing

Your text data is converted into high-dimensional vectors (embeddings) that represent semantic meaning
2

Query Understanding

When you search, your query is also converted into an embedding using the same model
3

Similarity Matching

The system finds content with embeddings most similar to your query, regardless of exact word matches
4

Results Ranking

Results are ranked by semantic similarity and relevance to your query

​
Prerequisites

Before configuring AI Search, ensure you have:

AI Infrastructure

Snowflake Cortex Provider: Must be configured with embedding servicesStandard Tables: Data stored in Standard Snowflake tablesText Content: Fields containing searchable text dataPermissions: Appropriate access to configure search

Data Preparation

Clean Data: Well-formatted text contentUnique Identifiers: Primary keys for each recordSearchable Fields: Identified fields for search indexingReasonable Size: Tables sized appropriately for search performance

​
Configuration Process

1

Navigate to AI Search

In the Intelligence tab, select AI Search from the menu
2

Add Search Table

Click ”+ Search Table” to configure a new searchable table
3

Select Table

Field to Search: Choose the primary field containing searchable textStandard Table: Select a Standard Snowflake table (other types not supported)Unique Identifier: Specify the field that uniquely identifies each record
4

Configure Search Parameters

Attribute Fields: Select additional fields to include in search resultsService: Choose your configured Snowflake Cortex embedding serviceArchival Service: Select Snowflake Cortex service to use for search queriesTarget Lag: Set how often to update search index (1 = daily updates)
5

Create Search Configuration

Click “Create” to set up the search indexInitial indexing may take time depending on data volume

​
Search Interface

Once configured, AI Search provides an intuitive search experience:

​
Search Results

AI Search returns enriched results with:

Result Content

Matched Text: The content that matched your querySimilarity Score: How closely the content matches your queryAttribute Fields: Additional data fields you configuredRecord Links: Direct links to full records

Search Insights

Query Understanding: How the AI interpreted your searchMatch Reasoning: Why specific results were returnedRelevance Ranking: How results are ordered by relevanceAlternative Suggestions: Related searches you might try

​
AI Search in Automation

​
AI Search Records Action

Use AI Search in your automation workflows with the AI Search Records action:
Purpose: Automatically find relevant records based on trigger contentSetup:
  • Select your configured AI Search table
  • Choose Snowflake Cortex embedding service for queries
  • Set result limits (1-100 records)
  • Configure attribute fields to return
Query Description: Provide guidance on what to search for (e.g., “Search for tickets by issue type”)Best Practices:
  • Use clear, specific search descriptions
  • Set appropriate result limits
  • Choose relevant attribute fields
  • Test search queries before deployment
Customer Support: Find similar support tickets for faster resolutionKnowledge Base: Locate relevant documentation and proceduresContent Discovery: Find related content and resourcesDuplicate Detection: Identify potentially duplicate recordsContext Gathering: Collect relevant information for decision-makingExample Automation:
Email Received → AI Search Records (Knowledge Base)
  → Find: Articles related to email content
  → Return: Title, URL, Category, Last Updated
  → Send Email (with relevant articles)

​
Integration with Agents

AI Search powers conversational agents by enabling them to find relevant information:

Agent Tools

Knowledge Access: Agents can search your knowledge baseContext Gathering: Find relevant information for responsesDynamic Responses: Provide current, accurate informationSelf-Service: Enable users to find answers independently

Configuration

Search Tool: Configure AI Search as an agent toolResult Limits: Set appropriate limits for agent responsesAttribute Fields: Choose fields for agent contextFiltering: Configure search filters for relevant results

​
Performance Optimization

​
Indexing Strategy

  • Content Optimization
  • Index Management
Text Quality: Ensure high-quality, well-formatted text contentContent Length: Optimal content length for embedding models (512-1024 tokens)Language Consistency: Use consistent language and terminologyContent Structure: Organize content logically for better understanding

​
Search Performance

Query Optimization

Query Clarity: Use clear, specific queries for better resultsResult Limits: Set appropriate limits (10-50 results typically)Caching: Cache frequent queries for faster responsesBatch Queries: Process multiple queries together when possible

Data Optimization

Table Size: Optimize table size for search performanceField Selection: Index only necessary fieldsData Quality: Maintain high-quality, relevant contentCleanup: Remove outdated or irrelevant content regularly

​
Best Practices

​
Content Preparation

1

Data Quality

Clean Text: Remove formatting artifacts and ensure readable contentConsistent Format: Use consistent formatting across all contentRelevant Content: Include only content that should be searchableComplete Information: Ensure content provides complete context
2

Structure Optimization

Logical Organization: Structure content in logical, searchable chunksAppropriate Length: Keep content chunks at optimal length for embeddingsClear Language: Use clear, professional languageAvoid Duplication: Remove or consolidate duplicate content

​
Search Configuration

Embedding Selection

Model Choice: Use high-quality embedding models for best resultsConsistency: Use the same embedding model throughout your systemPerformance: Balance model quality with performance requirementsCost Management: Consider embedding costs for large datasets

Attribute Configuration

Relevant Fields: Select fields that provide useful contextField Limits: Don’t overwhelm users with too many fieldsData Types: Ensure attribute fields contain meaningful dataPerformance Impact: Consider performance impact of many attributes

​
User Experience

  • Search Interface
  • Query Guidance
Intuitive Design: Make search interface easy to useClear Instructions: Provide guidance on effective searchingResult Presentation: Present results in a clear, useful formatFeedback Mechanism: Allow users to provide feedback on results

​
Troubleshooting

Symptoms: Cannot configure AI Search on a tableCommon Causes:
  • Table is not a Standard Snowflake table
  • Snowflake Cortex embedding service not configured
  • Insufficient permissions
  • Invalid field selections
Solutions:
  1. Verify table is a Standard Snowflake table
  2. Ensure Snowflake Cortex embedding service is properly configured
  3. Check permissions for search configuration
  4. Review field selections and requirements
Symptoms: Search returns irrelevant or poor-quality resultsCommon Causes:
  • Poor content quality
  • Inappropriate embedding model
  • Incorrect field configuration
  • Insufficient content volume
Solutions:
  1. Improve content quality and formatting
  2. Try different embedding models
  3. Review and adjust field configurations
  4. Ensure sufficient content volume for training
Symptoms: Slow search responses or timeoutsCommon Causes:
  • Large dataset size
  • Inefficient queries
  • Outdated index
  • Resource constraints
Solutions:
  1. Optimize dataset size and content
  2. Improve query efficiency
  3. Update search index regularly
  4. Consider resource scaling

​
Advanced Features

​
Custom Search Filters

Attribute Filtering

Field-Based Filters: Filter results by specific field valuesDate Ranges: Filter by date ranges for time-sensitive contentCategory Filters: Filter by content categories or typesCustom Criteria: Create custom filtering logic

Dynamic Filtering

Context-Aware: Filters that adapt to user contextRole-Based: Different filters for different user rolesWorkflow Integration: Filters that integrate with business workflowsAutomated Filters: Filters applied automatically based on conditions

​
Analytics and Monitoring

  • Search Analytics
  • Content Analytics
Query Patterns: Analyze common search patternsResult Quality: Monitor result relevance and qualityUser Behavior: Track how users interact with searchPerformance Metrics: Monitor search performance over time

​
Integration Examples

​
Support Ticket Resolution

Support Ticket Created → AI Search Records (Knowledge Base)
  → Query: Ticket description and category
  → Results: Related articles and solutions
  → Update Record (add suggested solutions)
  → Notify Agent (with relevant resources)

​
Document Classification

Document Uploaded → AI Search Records (Document Categories)
  → Query: Document content summary
  → Results: Similar documents and categories
  → AI Classification (based on similar documents)
  → Update Record (assign category)

​
Customer Service Enhancement

Customer Email → AI Search Records (Previous Interactions)
  → Query: Customer issue description
  → Results: Similar past issues and resolutions
  → Generate Response (informed by past solutions)
  → Send Email (with contextual response)

​
Next Steps

With AI Search configured:

Build Agents

Create agents that can search your knowledge base

Use in Automation

Add AI Search to your automation workflows

Optimize Performance

Monitor and optimize search performance

Expand Coverage

Add more tables and content to your search system
AI Search transforms how you find and use information by understanding meaning and context. With proper configuration, it becomes a valuable tool for knowledge discovery and workflow enhancement.
⌘I