Content Comparison

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To expose Reporting services to our system, we need an API to be exposed for the MediaHaven installation. When creating new Module Plugins having of the following https://mediahaven.atlassian.net/wiki/spaces/CS/pages/edit-v2/4438884395#Categories Plugin Category:

• Business Intelligence

then MediaHaven will connect to this plugin using REST API calls, which must implement the following methods depending on the category.

Methods

GET /

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info

Status
colour Blue title

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Business Intelligence

It gives the possible configuration parameters for this connector

Ex.

{
  "Rules": [
    {
      "Name": "TextModel",
      "Type": "String|Integer|Boolean",
      "Required": false,
      "Default" : "ada2"
    }
  ]
}

POST /text-embedding

Status
colour Blue title EMBEDDING

Expected parameters

Text as JSON body in Textproperty and Config based on config rules

Ex.

{
  "Text": "Given me pictures of lamas",
  "Dimensions": 1536,
  "Config": {
    "TextModel" : "ada2"
  }
}

basic info about this connector

Expected parameters

• None

Expected headers

x-api-key: <optional<reporting engine api key>

Output

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Ex.

{
  "EmbeddingReportingEngine": [ 
    -0.006929283495992422,
    -0.005336422007530928,
    ... (omitted for spacing)
    -4.547132266452536e-05,
    -0.024047505110502243]
}

POST /embedding

Status
colour Blue title EMBEDDING

Expected parameters

Record as JSON body in Metadata property and Config based on config rules

Ex.

{ 
 "Config": {     "TextModelName" : "ada2metabase"
  },
  "Metadata": {     "RightsManagementVersion": {
        "Permissions": {...},
        "RecordPhase": "Concept",
        "ExpiryDate": null,
        "ExpiryStatus": null,
        "Zone": {}
    },
    "Dimensions": 1536
  }
}

Expected headers

x-api-key: <optional api key>

Output

Array of float (vector) with size equal to dimensions. The vector MUST BE normalized. When the provided dimensions are 0, the embedding should be in the natural dimensions used by the embedding. If the embeddings cannot be provided in the requested dimensions, then a 400 HTTP error code should be returned.

Ex.

{
  "Embedding": [ 
    -0.006929283495992422,
    -0.005336422007530928,
    ... (omitted for spacing)
    -4.547132266452536e-05,
    -0.024047505110502243]
}

...

0.51", 
  }
}

GET /reports

Status
colour Blue title

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Business Intelligence

Expected parameters

Record as JSON body in Metadata property and Config based on config rules

Ex.

...

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• None

Expected headers

x-api-key: <optional<reporting engine api key>

Output

Textual string

Ex.

{
  "DescriptionReportingEngine": "This is an image of a cat"
}

POST /generative-metadata

Status
colour Blue title GENERATIVE

Record as JSON body in Metadata property, Fields with as items dotted keys and AI hint as value, Locale as the requested target locale for the output and finally Config based on config rules

Ex.

{ 
 "Fields": {      "Descriptive.Description": {
      "Hint": "Give a description for the image"
     },
 "Name": "metabase",
   "Descriptive.Keywords.Keyword": {         "Hint"Version": "Give a list of keywords. Generate around 10.",
        "Type": "array"
     }
  0.51", 
  }, 
 "Locale": "en_US",
  "Config"Reports": {
    "TextModelEmbedUri" : "ada2"
  },
  "Metadata": {
    "RightsManagement": {
        "Permissions": {...},
   https://metabase/embedded/report/x/y/z"
    "RecordPhaseLastDataRefresh": "Concept",
        "ExpiryDate": null,
        "ExpiryStatus": null,
        "Zone": {}
    }
  }
}

Expected headers

x-api-key: <optional api key>

Output

A JSON object of generated fields with as keys dotted keys and as value the AI's generated value.

Ex:

{ 
  "GeneratedFields": {
    "Descriptive.Description": "This is an image of a cat",
    "Descriptive.Keywords.Keyword": ["Animal", "Cat", ...]  2024-10-24T15:26:59Z"
  }
}