1. 调整请求大小：建议将较大的请求（如大于4MB）拆分成多个较小的请求，以避免超时和缓慢响应。

示例代码：

import requests

url = "https://{endpoint}/formrecognizer/v2.1-preview.2/custom/models/{modelId}/copy" 

payload = {
    "targetResourceId": target_resource_id,
    "targetResourceRegion": target_resource_region,
    "copyAuthorization": copy_authorization_token,
    "modelDisplayName": model_display_name
}

headers = {
    "Content-Type": "application/json",
    "Ocp-Apim-Subscription-Key": subscription_key
}

# Split larger requests into smaller ones (< 4MB)
if len(request_body) > 4 * 1024 * 1024:
    chunks = []
    chunk_size = 2 * 1024 * 1024
    
    for i in range(0, len(request_body), chunk_size):
        chunks.append(request_body[i:i+chunk_size])
    
    responses = []
    for chunk in chunks:
        response = requests.post(url, json=chunk, headers=headers)
        responses.append(response.json())
        
    response = responses[-1] # Use the last response as the final result
else:
    response = requests.post(url, json=payload, headers=headers).json()

2. 使用较小的模型：考虑使用较小的模型，以缩短处理时间和提高性能。

示例代码：

import requests

url = "https://{endpoint}/formrecognizer/v2.1-preview.2/prebuilt/receipt/analyze" 

headers = {
    "Content-Type": "image/jpeg",
    "Ocp-Apim-Subscription-Key": subscription_key
}

# Use a smaller model for faster processing
params = {
    "locale": "en-US",
    "includeFieldElements": True,
    "modelId": "prebuilt-receipt"
}

response = requests.post(url, data=image_data, headers=headers, params=params).json()

3. 使用更快的计算资源：升级到更快的