> ## Documentation Index
> Fetch the complete documentation index at: https://koreai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# GenAI Features - Search AI

<Badge icon="arrow-left" color="gray">[Back to Generative AI Features](/ai-for-service/generative-ai-tools/genai-features)</Badge>

LLM-powered features for Search AI that enable answer generation, vector search, document enrichment, and query processing.

<Note>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](/ai-for-service/generative-ai-tools/llm-integration#add-a-new-model-to-a-pre-built-integration).</Note>

## Model Feature Matrix

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

### Answer Generation and Enrichment

<Note>For Enrich Chunks with LLM and Transform Documents with LLM, use templates from the prompt library to write custom prompts.</Note>

| Model | Answer Generation | Enrich Chunks with LLM | Transform Documents with LLM |
| - | :-: | :-: | :-: |
| Azure OpenAI - GPT-5.4, GPT-5.3 Instant, GPT-5.2, GPT-5.1, GPT-5.4 Mini, GPT-5.4 Nano, GPT-5-mini | ✅ | ✅\* | ✅\* |
| Azure OpenAI - GPT-4 Turbo | ✅ | ✅\* | ✅\* |
| Azure OpenAI - GPT-4o | ✅ | ✅\* | ✅\* |
| Azure OpenAI - GPT-4o mini | ✅\* | ✅\* | ✅\* |
| OpenAI - GPT-5.4, GPT-5.3 Instant, GPT-5.2, GPT-5.1, GPT-5.4 Mini, GPT-5.4 Nano | ✅ | ✅\* | ✅\* |
| OpenAI - GPT-3.5 Turbo, GPT-4, GPT-4 Turbo | ✅ | ✅\* | ✅\* |
| OpenAI - GPT-4o | ✅ | ✅\* | ✅\* |
| OpenAI - GPT-4o mini | ✅\* | ✅\* | ✅\* |
| Custom LLM | ✅\* | ✅\* | ✅\* |
| XO GPT | ✅ | ❌ | ❌ |
| Amazon Bedrock | ✅\* | ✅\* | ✅\* |
| Claude - Opus 4.6, Sonnet 4.6 / 4.5, Haiku 4.5 | ✅ | ✅\* | ✅\* |

### Query Processing

| Model | Metadata Extractor Agent | Query Rephrase (Adv Search) | Query Transformation | Rephrase User Query | Result Type Classification |
| - | :-: | :-: | :-: | :-: | :-: |
| Azure OpenAI - GPT-5.4, GPT-5.3 Instant, GPT-5.2, GPT-5.1, GPT-5.4 Mini, GPT-5.4 Nano, GPT-5-mini | ✅ | ✅ | ✅ | ✅ | ✅ |
| Azure OpenAI - GPT-4 Turbo | ❌ | ❌ | ❌ | ✅\* | ❌ |
| Azure OpenAI - GPT-4o | ✅ | ✅ | ✅ | ✅ | ✅ |
| Azure OpenAI - GPT-4o mini | ✅\* | ✅\* | ✅\* | ✅\* | ✅\* |
| OpenAI - GPT-5.4, GPT-5.3 Instant, GPT-5.2, GPT-5.1, GPT-5.4 Mini, GPT-5.4 Nano | ✅ | ✅ | ✅ | ✅ | ✅ |
| 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 | ✅\* | ✅\* | ✅\* | ✅\* | ✅\* |
| Claude - Opus 4.6, Sonnet 4.6 / 4.5, Haiku 4.5 | ✅ | ✅ | ✅ | ✅ | ✅ |

### Vector Generation

| Model | Vector Generation - Text | Vector Generation - Image |
| - | :-: | :-: |
| Azure OpenAI (all models) | NA | NA |
| OpenAI (all models) | NA | NA |
| Custom LLM | ✅\* | ✅\* |
| XO GPT | ✅ | ✅ |
| Amazon Bedrock | ✅\* | ✅\* |

**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](/ai-for-service/searchai/howto/custom-embedding-model).

## Features

### 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](/ai-for-service/searchai/retrieval-answer-configuration#answer-generation).

### Enrich Chunks with LLM

Uses an external LLM to refine, update, or enrich chunks extracted from ingested content. [Learn more](/ai-for-service/searchai/index-configuration#llm-stage).

<Note>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.</Note>

### Transform Documents with LLM

Uses an external LLM to enhance or update documents during the extraction process. [Learn more](/ai-for-service/searchai/index-configuration#transformation-stages).

<Note>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.</Note>

### Vector Generation - Text

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

### Vector Generation - Image

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

### 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](/ai-for-service/searchai/agentic-rag-and-business-rules).

If using a custom prompt, the LLM output must follow this structure:

```json theme={null}
{
  "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.

### Query Rephrase for Advanced Search API

Adds contextual information to user queries to enhance their relevance. [Learn more](/ai-for-service/searchai/agentic-rag-and-business-rules#query-rephrase-agent).

If using a custom prompt, the LLM output must follow this structure:

```json theme={null}
{
  "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.

### Query Transformation

Identifies key terms within a query, removes noise, and prioritizes relevant documents. [Learn more](/ai-for-service/searchai/agentic-rag-and-business-rules#query-transformation-agent).

If using a custom prompt, the LLM output must follow this structure:

```json theme={null}
{
  "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"]
  }
}
```

| Field | Description |
| - | - |
| `query_processing.original_query` | The input query provided by the user. |
| `query_processing.keyword_search_query` | Optimized for keyword-based search. |
| `query_processing.vector_search_query` | Adapted for vector-based semantic search. |
| `core_terms` | Key terms extracted from the query. Reserved for future use. |
| `semantic_expansions` | Related or semantically similar terms. Reserved for future use. |
| `search_priority.must_include` | Critical terms; strongest boost. Single-word terms allowed. |
| `search_priority.should_include` | Moderate relevance boost. Must contain at least two words. |
| `search_priority.context_terms` | Light contextual boosting. Must contain at least two words. |

**Example:**

```json theme={null}
{
  "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"]
  }
}
```

### Result Type Classification

Used in Agentic RAG to determine whether the user seeks a specific answer or a list of search results. [Learn more](/ai-for-service/searchai/agentic-rag-and-business-rules#result-type-classification-agent).

If using a custom prompt, the LLM output must follow this structure:

```json theme={null}
{
  "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.

### Rephrase User Query

Reconstructs incomplete or ambiguous user inputs using conversation history, improving intent detection and entity extraction accuracy. Handles three scenarios:

| Scenario | Description | Example |
| - | - | - |
| **Completeness** | Completes an incomplete query using conversation context. | "How about Orlando?" → "What's the weather forecast for Orlando tomorrow?" |
| **Co-referencing** | Resolves pronouns or vague references using prior context. | "Every six hours." → "I take ibuprofen every six hours." |
| **Completeness + Co-referencing** | Handles both issues together. | "What about interest rates?" → "What are the interest rates for personal and home loans?" |

#### 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**.

***


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