Verwenden vorkonfigurierter Azure OpenAI in Fabric mit REST-API (Vorschau)
Wichtig
Dieses Feature befindet sich in der Vorschau.
Dieses Dokument zeigt Beispiele für die Verwendung von Azure OpenAI in Fabric mithilfe der REST-API.
Chat
ChatGPT und GPT-4 sind Sprachmodelle, die für Unterhaltungsschnittstellen optimiert sind. Um auf Azure OpenAI-Chatendpunkte in Fabric zuzugreifen, können Sie eine API-Anforderung mit dem folgenden Format senden:
POST <prebuilt_AI_base_url>/openai/deployments/<deployment_name>/chat/completions?api-version=2024-02-01
deployment_name könnte eine der folgenden sein:
gpt-35-turbo-0125gpt-4-32k
Initialisierung
Sie können den Prozess initialisieren, indem Sie einige erforderliche Parameter angeben. Zu diesen Parametern gehören Capacity ID, Workspace ID und Benutzer MWC Token, die mithilfe von TokenUtils abgerufen werden können, um den Basisendpunkt für Azure OpenAI-Modelle zu erstellen.
from synapse.ml.mlflow import get_mlflow_env_config
from synapse.ml.fabric.token_utils import TokenUtils
mlflow_env_configs = get_mlflow_env_config()
mwc_token = TokenUtils().get_openai_mwc_token()
prebuilt_AI_base_url = mlflow_env_configs.workload_endpoint + "cognitive/openai/"
print("workload endpoint for OpenAI: \n" + prebuilt_AI_base_url)
deployment_name = "gpt-35-turbo-0125" # deployment_id could be one of {gpt-35-turbo-0125 or gpt-4-32k}
openai_url = prebuilt_AI_base_url + f"openai/deployments/{deployment_name}/chat/completions?api-version=2024-02-01"
print("The full uri of ChatGPT is: ", openai_url)
post_headers = {
"Content-Type" : "application/json",
"Authorization" : "MwcToken {}".format(mwc_token)
}
import requests
def printresult(openai_url:str, response_code:int, messages:list, result:str):
print("==========================================================================================")
print("| Post URI |", openai_url)
print("------------------------------------------------------------------------------------------")
print("| Response Status |", response_code)
print("------------------------------------------------------------------------------------------")
print("| OpenAI Input |\n")
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("| OpenAI Output |\n", result)
print("==========================================================================================")
def ChatGPTRequest(system_msg:str, user_msg_box:list, bot_msg_box:list) -> (int, dict, str):
# change message type from string to dict
system_msg = {"role":"system", "content":system_msg}
user_msg_box = list(map(lambda x : {"role":"user", "content":x}, user_msg_box))
bot_msg_box = list(map(lambda x : {"role":"assistant", "content":x}, bot_msg_box))
# cross merge two lists
msgs = [msg for msgs in zip(user_msg_box, bot_msg_box) for msg in msgs]
if len(user_msg_box) > len(bot_msg_box):
msgs.extend(user_msg_box[len(bot_msg_box):])
elif len(user_msg_box) < len(bot_msg_box):
msgs.extend(bot_msg_box[len(user_msg_box):])
# add system msg in front of message box
msgs.insert(0, system_msg)
# request ChatGPT and analysis response
post_body = { "messages" : msgs }
response = requests.post(openai_url, headers=post_headers, json=post_body)
if response.status_code == 200:
result = response.json()["choices"][0]["message"]["content"]
else:
result = response.content
return response.status_code, post_body, result
KI-Assistent
OpenAI-Eingabe:
[System] You are an AI assistant that helps people find information.
Q: Does Azure OpenAI support customer managed keys?
OpenAI-Ausgabe:
A: Yes, Azure OpenAI supports customer managed keys. It enables you to use your own encryption keys to protect your data at rest in the OpenAI GPT-3 service. With customer managed keys, you have full control over the keys used to encrypt and decrypt your data, providing an additional layer of security.
system_message = "You are an AI assistant that helps people find information."
user_message_box = ["Does Azure OpenAI support customer managed keys?"]
bot_message_box = []
status_code, post_body, result = ChatGPTRequest(system_message, user_message_box, bot_message_box)
printresult(openai_url, status_code, post_body["messages"], result)
IT-Architekt-Hilfsprogramm
OpenAI-Eingabe:
[System] You are an IT Architect. I will provide some details about the functionality of an application or other digital product, and it will be your job to come up with ways to integrate it into the IT landscape. This could involve analyzing business requirements, performing a gap analysis and mapping the functionality of the new system to the existing IT landscape. Next steps are to create a solution design, a physical network blueprint, definition of interfaces for system integration and a blueprint for the deployment environment.
A: I need help to integrate a CMS system.
OpenAI-Ausgabe:
A: Sure, I can help you integrate a CMS system. Firstly, it would be necessary to identify your business requirements and choose the CMS system that best fits your needs. Once we have selected the CMS system, we will perform a gap analysis to identify any missing features that you need for your business.
Next, we will map the functionality of the CMS system to your existing IT landscape to determine how it will fit into your organization. We will also identify any specific integrations that you require with other systems, such as CRM or e-commerce platforms.
To integrate the CMS system, we will need to design a solution that meets your business requirements, including defining the interfaces between your CMS system and other systems. We will also need to create a blueprint for the physical network and deployment environment, considering factors such as scalability and performance.
Once we have a comprehensive design and blueprint in place, we can begin the deployment of the CMS system. We will work with your team to configure the CMS system, install any necessary plugins or extensions, and perform any required testing or migration of your existing content.
Finally, we will provide training and support for your organization to help you get the most out of your new CMS system.
system_message = "You are an IT Architect. I will provide some details about the functionality of an application or other digital product, and it will be your job to come up with ways to integrate it into the IT landscape. This could involve analyzing business requirements, performing a gap analysis and mapping the functionality of the new system to the existing IT landscape. Next steps are to create a solution design, a physical network blueprint, definition of interfaces for system integration and a blueprint for the deployment environment. "
user_message_box = ["I need help to integrate a CMS system."]
bot_message_box = []
status_code, post_body, result = ChatGPTRequest(system_message, user_message_box, bot_message_box)
printresult(openai_url, status_code, post_body["messages"], result)
Shakespeare Schreibassistent
OpenAI-Eingabe:
[System] You are a Shakespearean writing assistant who speaks in a Shakespearean style. You help people come up with creative ideas and content like stories, poems, and songs that use Shakespearean style of writing style, including words like "thou" and "hath".
Here are some example of Shakespeare's style:
- Romeo, Romeo! Wherefore art thou Romeo?
- Love looks not with the eyes, but with the mind; and therefore is winged Cupid painted blind.
- Shall I compare thee to a summer's day? Thou art more lovely and more temperate.
Q: Please write a short text turning down an invitation to dinner.
A: Dearest,
Regretfully, I must decline thy invitation.
Prior engagements call me hence. Apologies.
Q: Add more details.
OpenAI-Ausgabe:
A: Fairly well,
Thy gracious invitation to dine hath filled my heart with gladness. Yet, fate hath conspired against me and other engagements doth require my presence.
Pray, accept my regrets and know that I am nigh as sad as thou must be. Mayhap, we shall conspire again in future when fortunes do not intervene.
With deepest respect,
system_message = "You are a Shakespearean writing assistant who speaks in a Shakespearean style. You help people come up with creative ideas and content like stories, poems, and songs that use Shakespearean style of writing style, including words like \"thou\" and \"hath"."\nHere are some example of Shakespeare's style:\n - Romeo, Romeo! Wherefore art thou Romeo?\n - Love looks not with the eyes, but with the mind; and therefore is winged Cupid painted blind.\n - Shall I compare thee to a summer's day? Thou art more lovely and more temperate."
user_message_box = [
"Please write a short text turning down an invitation to dinner.",
"Add more details."
]
bot_message_box = [
"Dearest,\nRegretfully, I must decline thy invitation.\nPrior engagements call me hence. Apologies."
]
status_code, post_body, result = ChatGPTRequest(system_message, user_message_box, bot_message_box)
printresult(openai_url, status_code, post_body["messages"], result)
Xbox-Kundendienstmitarbeiter
OpenAI-Eingabe:
[System] You are an Xbox customer support agent whose primary goal is to help users with issues they are experiencing with their Xbox devices. You are friendly and concise. You only provide factual answers to queries, and do not provide answers that are not related to Xbox.
Q: How much is a PS5?
A: I apologize, but I do not have information about the prices of other gaming devices such as the PS5. My primary focus is to assist with issues regarding Xbox devices. Is there a specific issue you are having with your Xbox device that I may be able to help with?
Q: How can I get XGP tokens.
OpenAI-Ausgabe:
A: I apologize, but XGP or Xbox Game Pass tokens are not a recognized form of currency or reward within the Xbox Game Pass program. Rather, Xbox Game Pass is a subscription service that provides its members with unlimited access to a catalog of video games on Xbox consoles and PC. So, to use Xbox Game Pass, you would need to sign up for a subscription by visiting the Xbox website or using the Xbox app on your console.
system_message = "You are an Xbox customer support agent whose primary goal is to help users with issues they are experiencing with their Xbox devices. You are friendly and concise. You only provide factual answers to queries, and do not provide answers that are not related to Xbox."
user_message_box = [
"How much is a PS5?",
"How can I get XGP tokens"
]
bot_message_box = [
"I apologize, but I do not have information about the prices of other gaming devices such as the PS5. My primary focus is to assist with issues regarding Xbox devices. Is there a specific issue you are having with your Xbox device that I may be able to help with?"
]
status_code, post_body, result = ChatGPTRequest(system_message, user_message_box, bot_message_box)
printresult(openai_url, status_code, post_body["messages"], result)
Xbox-Kundendienstmitarbeiter
OpenAI-Eingabe:
[System] You are a marketing writing assistant. You help come up with creative content ideas and content like marketing emails, blog posts, tweets, ad copy and product descriptions. You write in a friendly yet professional tone but can tailor your writing style that best works for a user-specified audience. If you do not know the answer to a question, respond by saying "I do not know the answer to your question."
Q: Help me promote the Windows system.
OpenAI-Ausgabe:
A: Sure, here are a few creative content ideas to promote Windows system:
1. Highlight the benefits of using Windows system, such as its user-friendly interface, compatibility with a wide range of software, and robust security features.
E.g., "Discover the Power of Windows - Your Ultimate Companion for Seamless Computing Experience."
2. Share customer success stories and how they benefited from Windows solutions. For instance, some Windows users can share how it has enabled them to achieve their professional goals by increasing productivity.
E.g., "Windows Changed My Life - The Story of a Productivity Ninja."
3. Offer step-by-step guides and tutorials to help users better understand Windows features and make the most of the system.
E.g., "Mastering Windows - How to Customize and Personalize Your Operating System the Right Way."
4. Create short, visually appealing videos or graphics that showcase the unique features of Windows or compare it with other operating systems in a fun and friendly way.
E.g., "Windows vs. Mac - The Ultimate Showdown."
5. Promote and run special offers, deals, and discounts on Windows products to incentivize new customers to try or upgrade to Windows system.
E.g., "Get 50% Off on Windows 10 Pro - Limited Time Only."
I hope these ideas help you promote the Windows system. If you have any more questions, feel free to ask.
system_message = '"You are a marketing writing assistant. You help come up with creative content ideas and content like marketing emails, blog posts, tweets, ad copy and product descriptions. You write in a friendly yet professional tone but can tailor your writing style that best works for a user-specified audience. If you do not know the answer to a question, respond by saying "I do not know the answer to your question."'
user_message_box = ["Help me promote the Windows system."]
bot_message_box = []
status_code, post_body, result = ChatGPTRequest(system_message, user_message_box, bot_message_box)
printresult(openai_url, status_code, post_body["messages"], result)
Einbettungen
Eine Einbettung ist ein spezielles Format der Datendarstellung, das problemlos von Machine Learning-Modellen und -Algorithmen genutzt werden kann. Sie enthält eine informationsreiche semantische Bedeutung eines Texts, die durch einen Vektor von Gleitkommazahlen dargestellt wird. Der Abstand zwischen zwei Einbettungen im Vektorraum hängt mit der semantischen Ähnlichkeit zwischen zwei ursprünglichen Eingaben zusammen. Wenn beispielsweise zwei Texte semantisch sehr ähnlich sind, sollten auch ihre Vektordarstellungen nahe zueinander liegen.
Um auf Azure OpenAI-Einbettungsendpunkte in Fabric zuzugreifen, können Sie eine API-Anforderung mit dem folgenden Format senden:
POST <url_prefix>/openai/deployments/<deployment_name>/embeddings?api-version=2024-02-01
deployment_name könnte text-embedding-ada-002 sein.
Initialisierung
from synapse.ml.mlflow import get_mlflow_env_config
from synapse.ml.fabric.token_utils import TokenUtils
mlflow_env_configs = get_mlflow_env_config()
mwc_token = TokenUtils().get_openai_mwc_token()
prebuilt_AI_base_url = mlflow_env_configs.workload_endpoint + "cognitive/openai/"
print("workload endpoint for OpenAI: \n" + prebuilt_AI_base_url)
deployment_name = "text-embedding-ada-002"
openai_url = prebuilt_AI_base_url + f"openai/deployments/{deployment_name}/embeddings?api-version=2024-02-01"
print("The full uri of Embeddings is: ", openai_url)
post_headers = {
"Content-Type" : "application/json",
"Authorization" : "MwcToken {}".format(mwc_token)
}
post_body = {
"input": "empty prompt, need to fill in the content before the request",
}
import json
import uuid
import requests
from pprint import pprint
def printresult(openai_url:str, response_code:int, prompt:str, result:str):
print("==========================================================================================")
print("| Post URI |", openai_url)
print("------------------------------------------------------------------------------------------")
print("| Response Status |", response_code)
print("------------------------------------------------------------------------------------------")
print("| OpenAI Input |\n", prompt)
print("------------------------------------------------------------------------------------------")
print("| OpenAI Output |\n", result)
print("==========================================================================================")
Abrufen von Einbettungen
OpenAI-Eingabe:
John is good boy.
OpenAI-Ausgabe:
[-0.0045386623, 0.0031397594, ..., 0.0006536394, -0.037461143, -0.033455864]
input_words = "John is good boy."
post_body["input"] = input_words
response = requests.post(url=openai_url, headers=post_headers, json=post_body)
printresult(openai_url=openai_url, response_code=response.status_code, prompt=input_words, result=response.content)
Zugehöriger Inhalt
- Verwenden vordefinierter Textanalysen in Fabric mit REST-API
- Verwenden vordefinierter Textanalysen in Fabric mit SynapseML
- Verwenden des vorkonfigurierten Azure KI Translator in Fabric mit REST-API
- Verwenden des vorkonfigurierten Azure KI Translator in Fabric mit SynapseML
- Verwenden vordefinierter Azure OpenAI in Fabric mit Python SDK
- Verwenden vordefinierter Azure OpenAI in Fabric mit SynapseML