Semantic Index Configuration form

  • Release version: Zurich
  • Updated July 24, 2026
  • 3 minutes to read
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    Summary of Semantic Index Configuration form

    The Semantic Index Configuration form allows ServiceNow customers to define and manage semantic indexing settings for AI Search indexed sources. This configuration is essential for enhancing AI Search capabilities by enabling semantic vector search on indexed content. The form is available only when the AI Search Semantic Controller plugin is active, which requires at least one ServiceNow Otto® application installed on the instance.

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    Key Features

    • Name: Assign a unique, valid name to the semantic index generated by the configuration. The name must avoid special characters, underscores, or whitespace.
    • Embedding Models: Choose from several embedding models to use for semantic indexing:
      • ServiceNow Embedding (E5): Default fine-tuned embedding with a 512-term encoder limit.
      • Azure OpenAI Embedding: External fine-tuned model option.
      • Google Gemini Embedding: Another external fine-tuned model option.
      • Custom Embedding: Use your own fine-tuned embedding model (BYOM).
    • Active: Toggle to activate or deactivate the semantic index configuration. Inactive configurations are ignored during indexing.
    • Indexed Source: Automatically references the AI Search indexed source to which this configuration applies.
    • Application: Automatically sets the application scope for the configuration record.
    • Chunking Configuration for Embedding: Defines how text is broken down for semantic indexing:
      • Chunking Strategy: Options include:
        • Passage: For longer text, splits content into chunks based on chosen units and sizes.
        • Truncate: For shorter text, concatenates all fields and indexes up to a maximum word limit.
        • Full Text: For shorter text, concatenates all fields and indexes up to the encoder limit.
      • Overlap Sentences: Number of overlapping sentences between chunks to increase recall (only for Passage strategy).
      • Chunk Unit: Defines whether chunks are measured in words or sentences (only for Passage strategy).
      • Chunk Size: Maximum number of words or sentences per chunk (only for Passage strategy).
      • Maximum Total Words: Limits the total number of words indexed when using the Truncate strategy.

    Practical Benefits

    By configuring semantic indexing through this form, ServiceNow customers can optimize AI Search results with precise control over text chunking and embedding models. This leads to improved semantic vector search accuracy and relevance for indexed sources, enhancing overall search experience and AI capabilities within their ServiceNow instance.

    The Semantic Index Configuration form enables you to define semantic indexing settings for an AI Search indexed source.

    For details on defining and modifying semantic indexing settings for an indexed source, see Configure semantic indexing settings for an indexed source.
    Note:
    This form is only available when the AI Search Semantic Controller plugin (com.glide.ais.semantic_search) is active on your instance. To activate this plugin, your instance must have at least one ServiceNow Otto® application installed.
    Table 1. Semantic Index Configuration form
    Field Description
    Name Unique name for the semantic index generated by this semantic index configuration. As an example, if you're creating a semantic index configuration for the Knowledge Table indexed source, you might name it Knowledge-Table-semantic-index.
    Note:
    The semantic index's name can't contain special characters, underscores, or whitespace.
    Embedding Models List of embedding models to use for the semantic index configuration.
    • Default value: ServiceNow Embedding (E5)
    • Supported values:
      • ServiceNow Embedding (E5): Use the E5 fine-tuned embedding model for content in the semantic index. The embedding model's encoder limit is 512 terms.
      • Azure OpenAI Embedding: Use the Azure OpenAI fine-tuned embedding model for content in the semantic index. For more information, see Configuring an external or custom embedding model.
      • Google Gemini Embedding: Use the Google Gemini fine-tuned embedding model for content in the semantic index. For more information, see Configuring an external or custom embedding model.
      • Custom Embedding: Use the custom fine-tuned embedding model for content in the semantic index. For more information, see Configuring bring your own model (BYOM).
    Active Option to make the semantic index configuration active for your instance. AI Search ignores inactive semantic index configurations when indexing content from the specified index source.
    Indexed Source Reference to the AI Search indexed source that you want to apply this semantic index configuration to. This field is automatically set.

    For more details on indexed sources, see Indexed sources in AI Search.
    Application Application scope for the semantic index configuration record. This field is automatically set.
    Chunking Configuration For Embedding
    Chunking Strategy Strategy to use when chunking semantically indexed text for the embedding model.
    • Default value: Passage
    • Supported values:
      • Passage: Chunking strategy for longer text field values. Index text from semantic field values in chunks. Each chunk contains a maximum number of words or sentences determined by your Chunk Unit and Chunk Size selections.
      • Truncate: Chunking strategy for short text field values. Concatenate all semantic index field values, then perform semantic indexing for terms up to the Maximum Total Words limit.
      • Full Text: Chunking strategy for short text field values. Concatenate all semantic index fields, then perform semantic indexing for all terms up to the embedding model's encoder limit.
    • Type: choice list
    Overlap Sentences Number of sentences to overlap between chunks when indexing text from semantic index field values. Higher overlap values increase recall for semantic vector search at the expense of performance.

    This field appears only when Passage is selected from Chunking Strategy.

    • Default value: 5
    • Supported values: Any non-negative integer
    • Type: integer
    Chunk Unit Textual unit to use as the basis for chunk size when indexing semantic field values for semantic vector search.
    This field appears only when Passage is selected from Chunking Strategy.
    • Default value: Words
    • Supported values:
      • Words: Use words as the textual unit by which semantic index field values are chunked. Each chunk can include up to Chunk Size words.
      • Sentences: Use sentences as the textual unit by which semantic index field values are chunked. Each chunk can include up to Chunk Size sentences.
    • Type: choice list
    Chunk Size Maximum number of words or sentences (depending on your Chunk Unit selection) to include in a chunk when indexing semantic field values for semantic vector search.
    This field appears only when Passage is selected from Chunking Strategy.
    • Default value: 250 when Words is selected from Chunk Unit, or 15 when Sentences is selected from Chunk Unit
    • Supported values: Any non-negative integer
    • Type: integer
    Maximum Total Words Maximum number of words to index for semantic vector search from the concatenated values of all semantic index fields.
    This field appears only when Truncate is selected from Chunking Strategy.
    • Default value: 500
    • Supported values: Any non-negative integer
    • Type: integer