Semantic Index Configuration form
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.- 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 creating-byom.md. |
| 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.
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| 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.
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| 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.
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| 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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Parent Topic:AI Search reference