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Bruk Azure OpenAI i Fabric med REST API (forhåndsversjon)

Viktig

Denne funksjonen er i forhåndsvisning.

Dette dokumentet viser eksempler på hvordan du bruker Azure OpenAI i Fabric ved hjelp av REST-API.

Initialisering

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)
}

Nettprat

GPT-4o og GPT-4o-mini er språkmodeller som er optimalisert for samtalegrensesnitt.

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)

Utdata

==========================================================================================
| 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.
==========================================================================================

Innebygginger

En innebygging er et spesielt datarepresentasjonsformat som maskinlæringsmodeller og algoritmer enkelt kan bruke. Den inneholder informasjonsrik semantisk betydning av en tekst, representert ved en vektor av flytende punkttall. Avstanden mellom to innebygginger i vektorområdet er relatert til semantisk likhet mellom to opprinnelige innganger. Hvis for eksempel to tekster er like, bør vektorrepresentasjonene også være like.

Hvis du vil ha tilgang til Azure OpenAI-innebyggingsendepunktet i Fabric, kan du sende en API-forespørsel med følgende format:

POST <url_prefix>/openai/deployments/<deployment_name>/embeddings?api-version=2024-02-01

deployment_name kan være 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)

Ytelse:

==========================================================================================
| 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, ... ]
==========================================================================================

Relatert innhold