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AI Search lets you find information by meaning and context rather than exact keyword matches. It uses embeddings to convert text into vector representations and then compares semantic similarity, so a search for “delivery delays” can surface records about “shipping bottleneck” even when no words overlap. AI Search is available on Elements and Tables. You can also use it as an action step in automations or as a tool that agents can call at runtime.

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Requirements

Snowflake Cortex only. AI Search is exclusively powered by Snowflake Cortex embedding services. OpenAI and Gemini providers cannot be used for AI Search.
Before configuring AI Search, confirm the following:
  • Snowflake Cortex provider configured with an embedding service — see Snowflake Cortex Setup
  • Standard Snowflake table — other table types are not supported
  • Change tracking enabled on all underlying Snowflake tables — this can be enabled at any time, even after connecting an Element to Elementum. Run ALTER TABLE <table_name> SET CHANGE_TRACKING = TRUE; if it is not already on.
  • A text field containing the content you want to search
  • A unique identifier field (primary key) on each record
For automation triggers like “Record is updated” on Snowflake tables, change tracking must be enabled before configuring the trigger.

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How It Works

  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 converted into an embedding using the same model.
  3. Similarity matching — The system finds content whose embeddings are closest to your query, regardless of exact word overlap.
  4. Results ranking — Results are ordered by semantic similarity so the most relevant records appear first.
1

Navigate to AI Search

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

Add a search table

Click + Search Table to begin configuring a new searchable table.
3

Select your table and fields

  • Field to Search — Choose the primary text field containing searchable content
  • Standard Table — Select a Standard Snowflake table
  • Unique Identifier — Specify the field that uniquely identifies each record
4

Configure search parameters

  • Attribute Fields — Select additional fields to return alongside search results (e.g., category, status, date)
  • Service — Choose your configured Snowflake Cortex embedding service
  • Archival Service — Select the Snowflake Cortex service to use when running search queries
  • Target Lag — Set how frequently the search index updates (1 = daily)
5

Create the search configuration

Click Create to build the search index. Initial indexing may take time depending on data volume.

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Using the Search Interface

Once a search table is configured, you can query it directly from the AI Search interface. Natural language queries — Type questions in plain language. AI Search understands meaning, so queries like “issues with payment processing” or “customer complaints about delivery” return semantically relevant results even when records use different phrasing. Filtering — Combine semantic search with attribute filters to narrow results by field values, date ranges, or categories. Results — Each result includes the matched text, a similarity score indicating how closely it relates to your query, and any attribute fields you configured during setup.

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AI Search in Other Features

AI Search integrates with automations, agents, and Tables. Rather than configuring AI Search differently in each context, you set it up once (as described above) and then reference the search table where needed.

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Automations

Use the AI Search action in an automation to run a semantic query as part of a workflow. Select a configured AI Search table as the source and pass a dynamic query using value references from earlier in the automation (e.g., {{trigger.description}}). For full configuration details and output variables, see AI Search in the Automation Actions Reference. For broader guidance on using AI within automations, see AI in Automations.

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Agents

AI Search can be configured as a tool that agents call at runtime to look up relevant records during a conversation. When adding AI Search as an agent tool, you can define input value references with descriptions and use them in filters so the agent passes values dynamically for context-aware search. For details on configuring agent tools, see Agent Skills. For building and managing agents, see Building Agents.

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Tables

Table admins can enable AI Search on any Table. The setup follows the same process described above. For details on enabling AI Search within the Tables interface, see the AI Search section on the Tables page.

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Tips for Better Results

  • Choose a descriptive text field. The field you index should contain meaningful, readable content — not codes or IDs. Fields with complete sentences or descriptions produce better semantic matches.
  • Keep content at a reasonable length. Embedding models work best with content in the 512–1,024 token range. Very short or very long text can reduce match quality.
  • Use consistent language. Consistent terminology and formatting across records improves how well the embedding model captures your domain’s semantics.
  • Set appropriate result limits. For most use cases, 10–50 results strikes the right balance between coverage and relevance. In automation workflows, start with a smaller limit and increase if needed.
  • Update your index regularly. The Target Lag setting controls how often new or changed records are indexed. Daily updates (Target Lag = 1) work well for most cases; adjust if your data changes more frequently.

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Troubleshooting

Symptoms: AI Search fails to create or index properly, or you receive errors about change tracking.Solutions:
  1. Enable change tracking on the source Snowflake table: 
    ALTER TABLE <table_name> SET CHANGE_TRACKING = TRUE;
  2. Verify change tracking is active: 
    SHOW TABLES LIKE '<table_name>';
    Check the change_tracking column in the output.
  3. For “Record is updated” automation triggers, change tracking must be enabled before configuring the trigger.
Symptoms: The setup dialog does not allow you to create a search configuration.Solutions:
  1. Confirm the table is a Standard Snowflake table — other types are not supported.
  2. Verify that a Snowflake Cortex embedding service is configured under AI Services.
  3. Check that change tracking is enabled on the underlying table.
  4. Ensure you have the necessary permissions to configure search.
Symptoms: Searches return results that don’t match the intent of your query.Solutions:
  1. Review the content in your indexed text field — short, vague, or code-like values produce weaker embeddings.
  2. Try rephrasing your query to be more specific.
  3. Check that the correct embedding service is selected in the search configuration.
  4. If the indexed content has changed significantly since the last index update, wait for the next index refresh or reduce the Target Lag setting.
Symptoms: Search queries take longer than expected to return results.Solutions:
  1. Reduce the number of attribute fields returned with results.
  2. Lower the result limit if it is set high.
  3. Ensure your Snowflake warehouse is appropriately sized for the data volume.
  4. Check Snowflake Cortex service status for any provider-side latency.

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Next Steps

AI Services

Create and manage the embedding services that power AI Search

AI in Automations

Learn how AI actions — including AI Search — fit into automation workflows

Building Agents

Create agents that use AI Search as a runtime tool

Automation Actions Reference

Full configuration details for the AI Search automation action