重要
此功能目前为预览版。
本文档演示了如何通过 REST API 在 Fabric 中使用 Azure OpenAI 的示例。
初始化
from synapse.ml.mlflow import get_mlflow_env_config
from trident_token_library_wrapper import PyTridentTokenLibrary
mlflow_env_configs = get_mlflow_env_config()
mwc_token = PyTridentTokenLibrary.get_mwc_token(mlflow_env_configs.workspace_id, mlflow_env_configs.artifact_id, 2)
auth_headers = {
"Authorization" : "MwcToken {}".format(mwc_token)
}
聊天
GPT-4o 和 GPT-4o-mini 是针对聊天界面优化的语言模型。
import requests
def print_chat_result(messages, response_code, response):
print("==========================================================================================")
print("| OpenAI Input |")
for msg in messages:
if msg["role"] == "system":
print("[System] ", msg["content"])
elif msg["role"] == "user":
print("Q: ", msg["content"])
else:
print("A: ", msg["content"])
print("------------------------------------------------------------------------------------------")
print("| Response Status |", response_code)
print("------------------------------------------------------------------------------------------")
print("| OpenAI Output |")
if response.status_code == 200:
print(response.json()["choices"][0]["message"]["content"])
else:
print(response.content)
print("==========================================================================================")
deployment_name = "gpt-4o" # deployment_id could be one of {gpt-4o or gpt-4o-mini}
openai_url = mlflow_env_configs.workload_endpoint + f"cognitive/openai/openai/deployments/{deployment_name}/chat/completions?api-version=2025-04-01-preview"
payload = {
"messages": [
{"role": "system", "content": "You are an AI assistant that helps people find information."},
{"role": "user", "content": "Does Azure OpenAI support customer managed keys?"}
]
}
response = requests.post(openai_url, headers=auth_headers, json=payload)
print_chat_result(payload["messages"], response.status_code, response)
输出
==========================================================================================
| OpenAI Input |
[System] You are an AI assistant that helps people find information.
Q: Does Azure OpenAI support customer managed keys?
------------------------------------------------------------------------------------------
| Response Status | 200
------------------------------------------------------------------------------------------
| OpenAI Output |
As of my last training cut-off in October 2023, Azure OpenAI Service did not natively support customer-managed keys (CMK) for encryption of data at rest. Data within Azure OpenAI is typically encrypted using Microsoft-managed keys.
However, you should verify this information on the official Azure documentation or by contacting Microsoft support, as cloud service features and capabilities are frequently updated.
==========================================================================================
嵌入
嵌入是机器学习模型和算法可以轻松使用的一种特殊数据表示格式。 它包含信息丰富的文本语义，由浮点数向量表示。 向量空间中两个嵌入之间的距离与两个原始输入之间的语义相似性有关。 例如，如果两个文本相似，则它们的向量表示形式也应该相似。
若要在 Fabric 中访问 Azure OpenAI 嵌入终结点，可以使用以下格式发送 API 请求：
POST <url_prefix>/openai/deployments/<deployment_name>/embeddings?api-version=2024-02-01
deployment_name 可以是 text-embedding-ada-002。
import requests
def print_embedding_result(prompts, response_code, response):
print("==========================================================================================")
print("| OpenAI Input |\n\t" + "\n\t".join(prompts))
print("------------------------------------------------------------------------------------------")
print("| Response Status |", response_code)
print("------------------------------------------------------------------------------------------")
print("| OpenAI Output |")
if response_code == 200:
for data in response.json()['data']:
print("\t[" + ", ".join([f"{n:.8f}" for n in data["embedding"][:10]]) + ", ... ]")
else:
print(response.content)
print("==========================================================================================")
deployment_name = "text-embedding-ada-002"
openai_url = mlflow_env_configs.workload_endpoint + f"cognitive/openai/openai/deployments/{deployment_name}/embeddings?api-version=2025-04-01-preview"
payload = {
"input": [
"empty prompt, need to fill in the content before the request",
"Once upon a time"
]
}
response = requests.post(openai_url, headers=auth_headers, json=payload)
print_embedding_result(payload["input"], response.status_code, response)
输出：
==========================================================================================
| OpenAI Input |
empty prompt, need to fill in the content before the request
Once upon a time
------------------------------------------------------------------------------------------
| Response Status | 200
------------------------------------------------------------------------------------------
| OpenAI Output |
[-0.00258819, -0.00449802, -0.01700411, 0.00405622, -0.03064079, 0.01899395, -0.01295485, -0.01426286, -0.03512142, -0.01831212, ... ]
[0.02129045, -0.02013996, -0.00462094, -0.01146069, -0.01123944, 0.00199124, 0.00228992, -0.01370478, 0.00855917, -0.01470356, ... ]
==========================================================================================