Semantic Index Configuration form

  • Release version: Australia
  • Updated June 8, 2026
  • 3 minutes to read
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    Summary of Semantic Index Configuration form

    The Semantic Index Configuration form in ServiceNow allows you to define semantic indexing settings for an AI Search indexed source. This configuration is essential for enabling semantic vector search capabilities, improving search relevance by understanding the meaning of content rather than relying solely on keyword matching. The form is only available when the AI Search Semantic Controller plugin is active, which requires at least one Now Assist application installed on your instance.

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    Key Features

    • Name: Assign a unique name to your semantic index configuration. This name must avoid special characters, underscores, or whitespace for compatibility.
    • Embedding Models: Choose from several embedding models to encode content semantics:
      • ServiceNow Embedding (E5): Default model with a 512-term encoder limit.
      • Azure OpenAI Embedding: External fine-tuned model option.
      • Google Gemini Embedding: External fine-tuned model option.
      • Custom Embedding: Use your own fine-tuned embedding model.
    • Active: Toggle to activate or deactivate the semantic index configuration. Only active configurations are used during AI Search indexing.
    • Indexed Source: Automatically references the AI Search indexed source to which the configuration applies.
    • Application: Automatically sets the application scope for the configuration record.
    • Chunking Configuration: Defines how text is divided into chunks for embedding processing, critical for optimizing semantic search performance:
      • Chunking Strategy: Choose from Passage, Truncate, or Full Text based on text length and indexing needs.
        • Passage: Suitable for longer text, breaks content into manageable chunks with overlap options.
        • Truncate: Concatenates short text fields and indexes up to a maximum total words limit.
        • Full Text: Concatenates short text fields and indexes all terms up to the embedding model’s encoder limit.
      • Overlap Sentences: Number of overlapping sentences between chunks (only for Passage strategy), balancing between recall and performance.
      • 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: Limit on the total number of words indexed when using the Truncate strategy.

    Practical Implications for ServiceNow Customers

    By configuring semantic indexing settings appropriately, you can tailor AI Search to better understand and retrieve relevant content from your indexed sources. Selecting the right embedding model and chunking strategy enables optimized search performance and result accuracy based on your data characteristics. Activating and managing these configurations properly ensures your AI Search indexes include meaningful semantic vectors, enhancing knowledge discovery and user experience within your ServiceNow environment.

    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 Now Assist 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 ../concept/creating-byom.html.
    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