重要
この機能はプレビュー段階にあります。
このドキュメントでは、REST API を使用して Fabric で Azure OpenAI を使用する方法の例を示します。
初期化
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)
}
チャット
GPT-4o と GPT-4o-mini は、会話インターフェイス用に最適化された言語モデルです。
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)
アウトプット
==========================================================================================
| 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.
==========================================================================================
埋め込み
埋め込みは、機械学習モデルとアルゴリズムで簡単に利用できる特別なデータ表現形式です。 これには、浮動小数点数のベクトルで表される、情報が豊富な、テキストのセマンティックな意味が含まれています。 ベクトル空間内の 2 つの埋め込み間の距離は、2 つの元の入力間のセマンティック類似性に関連しています。 たとえば、2 つのテキストが似ている場合、それらのベクトル表現も似ているはずです。
Fabric で Azure OpenAI 埋め込みポイントにアクセスするため、次の形式を使用して API 要求を送信できます。
POST <url_prefix>/openai/deployments/<deployment_name>/embeddings?api-version=2024-02-01
deployment_name は text-embedding-ada-002 である可能性があります。
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)
アウトプット：
==========================================================================================
| 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, ... ]
==========================================================================================