application fonctionne de manière transparente avec votre modèle d’incorporation, vous devez créer des scripts de prétraitement et de post-traitement. Ces scripts aident l’application
à comprendre le format des données attendues et produites par votre modèle.
L’exemple suivant montre le script de prétraitement pour la structure de la
Azure OpenAI demande.
(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);
L’exemple suivant montre le script de post-traitement de réponse pour la structure de demande
Azure OpenAI .
(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);
) pour sélectionner le document.