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Back to API List This API retrieves answers and search results for a specific query. By default, the API returns answers. Along with search results and answers, the API also provides details about the chunks used and their associated metadata.
MethodPOST
Endpointhttps://{{host}}/api/public/bot/<AppId>/search/v2/advanced-search
Content-Typeapplication/json
Authorizationauth: <JWT Token>
API ScopeAnswer Generation

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Query Parameters

ParameterDescriptionMandatory
AppIDYour application ID. To view your App ID, go to Dev Tools under App Settings. The AppID is listed under API scopes.Yes

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Request Parameters

ParameterDescriptionMandatory
queryQuery string for search.Yes
answerSearchSet to true to include an answer in the response.No
searchResultsSet to true to include search results in the response.No
metaFiltersArray of rules to filter results from the Answer Index before generating the answer. For example, to use only web pages: "metaFilters": [{"condition": "AND", "rules": [{"fieldName": "sourceType", "fieldValue": ["web"], "operator": "contains"}]}]No
isFacetsEnableSet to true to enable facets configured in the Search AI application.No
customDataCustom data to send with the request. Use this to pass previous conversations as context, set user context (identity, location, etc.), or filter search results.No
raclEntityIdsArray of RACL values specifying Role-Based Access Control List values to determine accessible content. Can include user identities (email addresses) and permission entity IDs (user groups). When provided, Search AI uses only these values and ignores RACL Resolver API configurations.No
dynamicPromptSelectionSpecifies the prompt and model to use for answer generation. Overrides the default model and prompt set at the application level. Accepts integrationName, model, and promptName. Supported integrationName values: "openai", "azure", "korexo", or a custom integration name. All values are case-sensitive. For Kore XO GPT, set model to "XO-GPT" and promptName to "Default".No
includeMetaDataAnswersArray of specific chunk metadata fields to include in the response. Fields are returned as part of graph_answer. For metadata fields, use the root name prefix (for example, "chunkMeta.author"). Missing fields are returned as null.No
includeChunksInResponseSet to true to include the list of qualified chunks in the response. Chunk data is stored in the chunk_result field.No
Sample Request 
{
    "query": "History_of_Data_Science",
    "answerSearch": true,
    "searchResults": true,
    "isFacetsEnable": true
}

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Response

The response is in JSON format. Key fields within the template field:
  • results: Search results grouped by source type. Chunks from the same document are grouped together.
"results": {
  "googleDrive": {
    "data": [
      {
        "docId": "fc-4f2ef8b5-e05d-555a-8fb9-dc8a1ceb514b",
        "recordTitle": "Copy of Microsoft.pdf",
        "recordUrl": "https://drive.google.com/file/d/1vEZTjr9VtQxxxxxxxxxxxxxxxxxxxxxxTGP/view?usp=drivesdk",
        "chunkResults": [
          "chk-f6bf3fb8-5adf-427e-b97a-0893af7be94f",
          "chk-864bf33b-bfad-4c08-9b93-bff1a663ae07",
          "chk-6f949cdb-33c3-446b-8a20-9cdd7cdab9a4"
        ]
      }
    ],
    "doc_count": 1
  },
  "file": {
    "data": [
      {
        "docId": "fc-e1d1181b-4b23-58df-8346-e4c728259c4d",
        "recordTitle": "SampleFile_20230130_0000.pdf",
        "recordUrl": "https://domain.com/api/getMediaStream/...",
        "chunkResults": [
          "chk-84e2c8ca-dc0f-4372-b3ff-36368ab4dea3"
        ]
      }
    ],
    "doc_count": 1
  }
}
  • answer_details: The answer to the query along with metadata.
"answer_details": {
  "query": "Microsoft",
  "requestType": "answer_search",
  "response": {
    "answer": "Microsoft is a multinational technology company...",
    "answer_payload": {
      "center_panel": {
        "type": "citation_snippet",
        "data": [...]
      }
    }
  },
  "resultType": "Answer",
  "indexPipelineId": "fip-8918ab9c-522d-5802-add6-1ee06751490f",
  "searchIndexId": "sidx-6dab30f2-450c-5272-8c5f-33ea7d96cc8f",
  "searchRequestId": "fsh-49d40f77-3a8d-5747-91dd-4d1aa43a293d",
  "queryPipelineId": "fqp-2641bfc8-2b02-5c6b-9335-d3076d7fbaf7"
}
  • chunk_result: Array of all chunks used for answers or search results. Each item contains chunk details and metadata.
"chunk_result": [
  {
    "_index": "answer_index",
    "_id": "2pxA1ZQBY6hZN8s7ElhV",
    "_score": 2.9723904,
    "_source": {
      "chunkId": "chk-f6bf3fb8-5adf-427e-b97a-0893af7be94f",
      "recordTitle": "Copy of Microsoft.pdf",
      "sourceType": "googleDrive",
      "chunkQualified": true,
      "sentToLLM": true,
      "usedInAnswer": false
    }
  }
]
  • facets: Filter information. Each unique value of the selected filter field creates a bucket with a key, chunk count, and document count.
"facets": [
  {
    "fieldName": "createdBy",
    "multiselect": false,
    "name": "Created By",
    "subtype": "value",
    "buckets": [
      {
        "key": "u-fec9538c-xxxx-xxxx-xxxx-679de486e3d9",
        "chunk_count": 240,
        "doc_count": 14
      }
    ]
  }
]
  • tabFacet: Content for the static filter used to create tabs. Each bucket corresponds to a unique value of the tab field.
"tabFacet": {
  "fieldName": "sourceType",
  "buckets": [
    { "key": "file", "name": "Files", "chunk_count": 347, "doc_count": 7 },
    { "key": "googleDrive", "name": "Google Drive", "chunk_count": 168, "doc_count": 3 },
    { "key": "web", "name": "Web Results", "chunk_count": 44, "doc_count": 10 }
  ]
}
Additional response fields (not within template):
  • llmResponseTime: Time (in milliseconds) taken by the language model to generate the response.
  • retrievalResponseTime: Time (in milliseconds) taken by Search AI to retrieve and process relevant chunks.

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Custom Data Examples

Example 1: Pass user information 
"customData": {
  "userContext": {
    "userName": "John",
    "userId": "john.smith@example.com",
    "emailId": "john.smith@example.com"
  }
}
Example 2: Pass user location 
"customData": {
  "userContext": {
    "location": "Germany"
  }
}
Example 3: Pass previous conversation as context to the Query Rephrasing Agent 
"customData": {
  "previousConversation": [
    {
      "query": "What is the leave policy for America?",
      "answer": "The leave policy in the U.S. varies by employer..."
    },
    {
      "query": "How do I reset my company email password?",
      "answer": "You can reset your company email password by visiting the IT support portal..."
    }
  ]
}