Semantic Index Configuration form
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Summary of Semantic Index Configuration form
The Semantic Index Configuration form in ServiceNow enables you to define settings for semantic indexing of AI Search indexed sources. This configuration is essential for optimizing how AI Search processes and retrieves semantically relevant content. The form becomes available only when theAI Search Semantic Controller plugin (com.glide.ais.semanticsearch)is active, which requires at least one Now Assist application installed on your instance.
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Key Features
- Name: Assign a unique, special-character-free name to the semantic index configuration for clear identification, such as Knowledge-Table-semantic-index.
- Embedding Models: Select from multiple fine-tuned embedding models to generate semantic vectors, including:
- ServiceNow Embedding (E5) – default, with a 512-term encoder limit.
- Azure OpenAI Embedding
- Google Gemini Embedding
- Custom Embedding (Bring Your Own Model)
- Active: Toggle to activate or deactivate the configuration. Inactive configurations are ignored during indexing.
- Indexed Source: Automatically references the AI Search indexed source to which the configuration applies.
- Application: Automatically sets the application scope of the configuration record.
- Chunking Configuration: Controls how text is broken into chunks for embedding, with three strategies:
- Passage (default): Breaks longer text into chunks by words or sentences, with configurable chunk size and overlap to balance recall and performance.
- Truncate: Concatenates all semantic fields and indexes up to a maximum total word limit, suitable for short texts.
- Full Text: Concatenates all semantic fields and indexes all terms up to the embedding model’s encoder limit.
- Overlap Sentences: Number of overlapping sentences between chunks to improve search recall.
- Chunk Unit: Choose whether chunks are measured in words or sentences.
- Chunk Size: Maximum number of words or sentences per chunk.
Practical Benefits for ServiceNow Customers
By configuring semantic indexing settings precisely, you can enhance AI Search’s ability to understand and retrieve content semantically, leading to more relevant search results for end users. Selecting appropriate embedding models and chunking strategies lets you optimize indexing for different content types and sizes, balancing recall, precision, and performance. Activating and associating these configurations with indexed sources ensures your semantic search capabilities are tailored and effective across 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.
| 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.
|
| 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.
|
| 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.
|
| 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.
|
| 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.
|
| 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.
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