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Documentation Index

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Back to Generative AI Features LLM-powered features for Search AI that enable answer generation, vector search, document enrichment, and query processing.
The platform regularly integrates new models from providers like OpenAI, Azure OpenAI, and Anthropic. To use a model not yet available as a pre-built integration, add it using Provider’s New LLM Integration.

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Model Feature Matrix

(✅ Supported | ❌ Not supported | ✅* Supported but no default prompt | NA = Not Applicable)

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Answer Generation and Enrichment

For Enrich Chunks with LLM and Transform Documents with LLM, use templates from the prompt library to write custom prompts.
ModelAnswer GenerationEnrich Chunks with LLMTransform Documents with LLM
Azure OpenAI - GPT-4 Turbo✅✅*✅*
Azure OpenAI - GPT-4o✅✅*✅*
Azure OpenAI - GPT-4o mini✅*✅*✅*
OpenAI - GPT-3.5 Turbo, GPT-4, GPT-4 Turbo✅✅*✅*
OpenAI - GPT-4o✅✅*✅*
OpenAI - GPT-4o mini✅*✅*✅*
Custom LLM✅*✅*✅*
XO GPT✅❌❌
Amazon Bedrock✅*✅*✅*

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Query Processing

ModelMetadata Extractor AgentQuery Rephrase (Adv Search)Query TransformationRephrase User QueryResult Type Classification
Azure OpenAI - GPT-4 Turbo❌❌❌✅*❌
Azure OpenAI - GPT-4o✅✅✅✅✅
Azure OpenAI - GPT-4o mini✅*✅*✅*✅*✅*
OpenAI - GPT-3.5 Turbo, GPT-4, GPT-4 Turbo❌❌❌✅*❌
OpenAI - GPT-4o✅✅✅✅✅
OpenAI - GPT-4o mini✅*✅*✅*✅*✅*
Custom LLM (GPT-4o / GPT-4o mini underlying)✅*✅*✅*✅*✅*
XO GPT❌❌❌✅❌
Amazon Bedrock✅*✅*✅*✅*✅*

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Vector Generation

ModelVector Generation - TextVector Generation - Image
Azure OpenAI (all models)NANA
OpenAI (all models)NANA
Custom LLM✅*✅*
XO GPT✅✅
Amazon BedrockNANA
Supported vector dimensions for custom embedding model integrations: 128, 256, 384, 512, 768, 1024, 1028, 1536, 2048, 3072 See how to integrate a custom embedding model.

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Features

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Answer Generation

Generates an answer to the user’s question based on data ingested into the Search AI application. Relevant data is retrieved and inserted into the prompt; the configured LLM returns a formatted answer. Learn more.

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Enrich Chunks with LLM

Uses an external LLM to refine, update, or enrich chunks extracted from ingested content. Learn more.
You must create a custom prompt to use this feature. All chunk fields are available for use in the prompt — click View Field Details when adding a Workbench Stage to see the full list.

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Transform Documents with LLM

Uses an external LLM to enhance or update documents during the extraction process. Learn more.
You must create a custom prompt to use this feature. All document fields are available for use in the prompt — click View Field Details when adding a Transformation Stage to see the full list.

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Vector Generation - Text

Creates vector embeddings for ingested text data. When a user submits a query, it is converted into an embedding and a vector search retrieves the most relevant data, which is then passed to answer generation.

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Vector Generation - Image

Creates vector embeddings for ingested image data. When a user submits a query, it is converted into an embedding and a vector search retrieves the most relevant images, which are then passed to answer generation.

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Metadata Extractor Agent

Extracts relevant sources and fields from a query, maps them to structured data, and applies filters or boosts for accurate retrieval. Particularly useful for data from third-party applications. Learn more. If using a custom prompt, the LLM output must follow this structure: 
{
  "extractedMetaData": [
    {
      "sourceName": "string",
      "chunkMeta": {
        "metaKey1": ["value1", "value2"],
        "metaKey2": ["value3"]
      }
    }
  ],
  "range": [
    {
      "createdOn": {
        "gte": "YYYY-MM-DDTHH:MM:SSZ",
        "lte": "YYYY-MM-DDTHH:MM:SSZ"
      }
    }
  ],
  "sourceIntent": true
}
  • extractedMetaData: Array of sources and associated metadata.
  • range: Optional date range filter.
  • sourceIntent: Boolean indicating whether the source was explicitly specified.

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Query Rephrase for Advanced Search API

Adds contextual information to user queries to enhance their relevance. Learn more. If using a custom prompt, the LLM output must follow this structure: 
{
  "rephrased_query": "string",
  "confidence": "High",
  "reasoning": "The rephrasing adds clarity and reflects the conversation context."
}
  • rephrased_query: The reworded version of the original query.
  • confidence: Confidence level in the quality of rephrasing.
  • reasoning: Justification for the transformation.

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Query Transformation

Identifies key terms within a query, removes noise, and prioritizes relevant documents. Learn more. If using a custom prompt, the LLM output must follow this structure: 
{
  "query_processing": {
    "original_query": "string",
    "keyword_search_query": "string",
    "vector_search_query": "string"
  },
  "core_terms": ["string"],
  "semantic_expansions": ["string"],
  "search_priority": {
    "must_include": ["string"],
    "should_include": ["string"],
    "context_terms": ["string"]
  }
}
FieldDescription
query_processing.original_queryThe input query provided by the user.
query_processing.keyword_search_queryOptimized for keyword-based search.
query_processing.vector_search_queryAdapted for vector-based semantic search.
core_termsKey terms extracted from the query. Reserved for future use.
semantic_expansionsRelated or semantically similar terms. Reserved for future use.
search_priority.must_includeCritical terms; strongest boost. Single-word terms allowed.
search_priority.should_includeModerate relevance boost. Must contain at least two words.
search_priority.context_termsLight contextual boosting. Must contain at least two words.
Example: 
{
  "query_processing": {
    "original_query": "what are the large language models which have large context window in them",
    "keyword_search_query": "large language models large context window",
    "vector_search_query": "large language models which have large context window"
  },
  "search_priority": {
    "must_include": ["large language models", "large context window"],
    "should_include": ["models"],
    "context_terms": ["LLM"]
  }
}

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Result Type Classification

Used in Agentic RAG to determine whether the user seeks a specific answer or a list of search results. Learn more. If using a custom prompt, the LLM output must follow this structure: 
{
  "query_type": "TYPE_1/TYPE_2",
  "confidence": "High",
  "reasoning": "This query closely matches the definition of TYPE_1 based on its intent and phrasing."
}
  • query_type: TYPE_1 (Search Results) or TYPE_2 (Answers).
  • confidence: Certainty level (for example, High, Medium, Low).
  • reasoning: Brief explanation for the chosen type.

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Rephrase User Query

Reconstructs incomplete or ambiguous user inputs using conversation history, improving intent detection and entity extraction accuracy. Handles three scenarios:
ScenarioDescriptionExample
CompletenessCompletes an incomplete query using conversation context.”How about Orlando?” → “What’s the weather forecast for Orlando tomorrow?”
Co-referencingResolves pronouns or vague references using prior context.”Every six hours.” → “I take ibuprofen every six hours.”
Completeness + Co-referencingHandles both issues together.”What about interest rates?” → “What are the interest rates for personal and home loans?”

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Conversation History Length

Controls how many previous messages are used as rephrasing context. Default: 5. Maximum: 25. Limited to the session’s available history. Access from Rephrase User Query > Advanced Settings.