Anwendung um nahtlos mit Ihrem Einbettungsmodell zu arbeiten, müssen Sie vor- und Nachverarbeitungsskripts erstellen. Diese Skripts helfen dem
Anwendung Verstehen Sie das Format der Daten, die Ihr Modell erwartet und erstellt.
Das folgende Beispiel zeigt das Präprozessorskript für
Azure OpenAI Anforderungsstruktur.
(function(inputs) { //// inputs is the capability inputs which contains request.
/* write code here to transform capability input to definition input.
*{
"request": { //request contains rawInput which will be system-provided.
"rawInput": { //rawInput contains passages(Array of text segments), type(Context of the input) and modelId(Name of the embedding model)
"passages": ["Sample text"],
"type": "ingest/query",
"model": "text-embedding-3-large"
},
"providerInput": { //providerInput should be populated using the relevant data from rawInput.passages.
"input": ["Sample text"] //The field name "input" inside providerInput is provider-specific and rawInput.passages should be mapped to the appropriate field expected by your provider
}
}
}
try {
var parsedInputs = JSON.parse(inputs);
var request = parsedInputs.request;
var rawInput = request.rawInput;
var passagesArray = rawInput.passages;
var type = rawInput.type;
if (passagesArray && passagesArray.length > 0) {
parsedInputs.request.providerInput = {};
parsedInputs.request.providerInput.input = passagesArray;
}
} catch (ex) {
gs.error("Exception occured while preprocessing of the inputs." + ex);
throw new Error("Exception occured while OE preprocessing of the inputs : " + ex);
}
inputs = parsedInputs;
return inputs;
})(inputs);
Das folgende Beispiel zeigt das Antwortpostprozessorskript für
Azure OpenAI Anforderungsstruktur.
(function(outputs) {
/* write code here to transform definition output to capability output.
* outputs contains result from the provider definition implementation.
* Expected outputs format from provider definition implementation in success scenario:
"result": {
"provider": "custom_embedding_model",
"logId": "0ee6f4f4ff112210b6e1ffffffffff03",
"status": "success",
"response": {"object":"list","data":[{"object":"embedding","index":0,"embedding":[0.008695375,0.0011188902]},{"object":"embedding","index":1,"embedding":[0.012572239,0.017366657]}],"model":"text-embedding-3-large","usage":{"prompt_tokens":4,"total_tokens":4}}
}
* Expected outputs format from provider definition implementation in error scenario:
"result": {
"responseHeaders": "[Content-Length: 224\r\n, Content-Type: application/json\r\n, apim-request-id: 72b1519e-a367-4f01-b249-8f37d35d50d3\r\n, Date: Thu, 01 May 2025 12:03:44 GMT\r\n]",
"responseBody": "{\"error\":{\"code\":\"401\",\"message\":\"Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.\"}}",
"provider": "custom_embedding_model",
"errorCode": "401",
"logId": "0ee6f4f4ff112210b6e1ffffffffff03",
"error": "PermissionDenied",
"status": "error"
}
* Extract the embeddings from the response key of the provider result (in a successful scenario) and elevate it in the final output. Sample format for the same:
"result": {
"provider": "custom_embedding_model",
"logId": "0ee6f4f4ff112210b6e1ffffffffff03",
"status": "success",
"response": {"object":"list","data":[{"object":"embedding","index":0,"embedding":[0.008695375,0.0011188902]},{"object":"embedding","index":1,"embedding":[0.012572239,0.017366657]}],"model":"text-embedding-3-large","usage":{"prompt_tokens":4,"total_tokens":4}}
"embeddings": [[0.008695375,0.0011188902], [0.012572239,0.017366657]]
}
*/
try {
outputs = JSON.parse(outputs);
var embeddingsArray = [];
if (!gs.nil(outputs.result) && !gs.nil(outputs.result.response)) {
var responseBody = outputs.result.response;
if (!gs.nil(responseBody)) {
var data = JSON.parse(responseBody).data;
for (var i = 0; i < data.length; i++) {
var embeddingResponse = data[i];
if (!gs.nil(embeddingResponse) && !gs.nil(embeddingResponse.embedding))
embeddingsArray.push(embeddingResponse.embedding);
}
}
outputs.result.embeddings = embeddingsArray;
}
} catch (ex) {
gs.error("Exception occured while OE postprocessing of the outputs = " + ex);
outputs.result.status = "error";
outputs.result.error = gs.getMessage("Exception occured while OE postprocessing of the outputs : " + ex);
}
return outputs;
})(outputs);
), um das Dokument auszuwählen.