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app.py
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app.py
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import urllib.request
import ssl
import json
import os
import logging
import requests
import openai
from flask import Flask, Response, request, jsonify
from dotenv import load_dotenv
load_dotenv()
app = Flask(__name__)
@app.route("/", defaults={"path": "index.html"})
@app.route("/<path:path>")
def static_file(path):
return app.send_static_file(path)
# ACS Integration Settings
AZURE_SEARCH_SERVICE = os.environ.get("AZURE_SEARCH_SERVICE")
AZURE_SEARCH_INDEX = os.environ.get("AZURE_SEARCH_INDEX")
AZURE_SEARCH_KEY = os.environ.get("AZURE_SEARCH_KEY")
AZURE_SEARCH_USE_SEMANTIC_SEARCH = os.environ.get("AZURE_SEARCH_USE_SEMANTIC_SEARCH", "false")
AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG = os.environ.get("AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG", "default")
AZURE_SEARCH_TOP_K = os.environ.get("AZURE_SEARCH_TOP_K", 5)
AZURE_SEARCH_ENABLE_IN_DOMAIN = os.environ.get("AZURE_SEARCH_ENABLE_IN_DOMAIN", "true")
AZURE_SEARCH_CONTENT_COLUMNS = os.environ.get("AZURE_SEARCH_CONTENT_COLUMNS")
AZURE_SEARCH_FILENAME_COLUMN = os.environ.get("AZURE_SEARCH_FILENAME_COLUMN")
AZURE_SEARCH_TITLE_COLUMN = os.environ.get("AZURE_SEARCH_TITLE_COLUMN")
AZURE_SEARCH_URL_COLUMN = os.environ.get("AZURE_SEARCH_URL_COLUMN")
# AOAI Integration Settings
AZURE_OPENAI_RESOURCE = os.environ.get("AZURE_OPENAI_RESOURCE")
AZURE_OPENAI_MODEL = os.environ.get("AZURE_OPENAI_MODEL")
AZURE_OPENAI_KEY = os.environ.get("AZURE_OPENAI_KEY")
AZURE_OPENAI_TEMPERATURE = os.environ.get("AZURE_OPENAI_TEMPERATURE", 0)
AZURE_OPENAI_TOP_P = os.environ.get("AZURE_OPENAI_TOP_P", 1.0)
AZURE_OPENAI_MAX_TOKENS = os.environ.get("AZURE_OPENAI_MAX_TOKENS", 1000)
AZURE_OPENAI_STOP_SEQUENCE = os.environ.get("AZURE_OPENAI_STOP_SEQUENCE")
AZURE_OPENAI_SYSTEM_MESSAGE = os.environ.get("AZURE_OPENAI_SYSTEM_MESSAGE", "You are an AI assistant that helps people find information.")
AZURE_OPENAI_PREVIEW_API_VERSION = os.environ.get("AZURE_OPENAI_PREVIEW_API_VERSION", "2023-06-01-preview")
AZURE_OPENAI_STREAM = os.environ.get("AZURE_OPENAI_STREAM", "true")
AZURE_OPENAI_MODEL_NAME = os.environ.get("AZURE_OPENAI_MODEL_NAME", "gpt-35-turbo") # Name of the model, e.g. 'gpt-35-turbo' or 'gpt-4'
# LLAMA 2 Related Settings
AZURE_LLAMA2_KEY = os.environ.get("AZURE_LLAMA2_KEY")
SHOULD_STREAM = True if AZURE_OPENAI_STREAM.lower() == "true" else False
def is_chat_model():
if 'gpt-4' in AZURE_OPENAI_MODEL_NAME.lower() or AZURE_OPENAI_MODEL_NAME.lower() in ['gpt-35-turbo-4k', 'gpt-35-turbo-16k']:
return True
return False
def should_use_data():
if AZURE_SEARCH_SERVICE and AZURE_SEARCH_INDEX and AZURE_SEARCH_KEY:
return True
return False
def prepare_body_headers_with_data(request):
request_messages = request.json["messages"]
body = {
"messages": request_messages,
"temperature": float(AZURE_OPENAI_TEMPERATURE),
"max_tokens": int(AZURE_OPENAI_MAX_TOKENS),
"top_p": float(AZURE_OPENAI_TOP_P),
"stop": AZURE_OPENAI_STOP_SEQUENCE.split("|") if AZURE_OPENAI_STOP_SEQUENCE else None,
"stream": SHOULD_STREAM,
"dataSources": [
{
"type": "AzureCognitiveSearch",
"parameters": {
"endpoint": f"https://{AZURE_SEARCH_SERVICE}.search.windows.net",
"key": AZURE_SEARCH_KEY,
"indexName": AZURE_SEARCH_INDEX,
"fieldsMapping": {
"contentField": AZURE_SEARCH_CONTENT_COLUMNS.split("|") if AZURE_SEARCH_CONTENT_COLUMNS else [],
"titleField": AZURE_SEARCH_TITLE_COLUMN if AZURE_SEARCH_TITLE_COLUMN else None,
"urlField": AZURE_SEARCH_URL_COLUMN if AZURE_SEARCH_URL_COLUMN else None,
"filepathField": AZURE_SEARCH_FILENAME_COLUMN if AZURE_SEARCH_FILENAME_COLUMN else None
},
"inScope": True if AZURE_SEARCH_ENABLE_IN_DOMAIN.lower() == "true" else False,
"topNDocuments": AZURE_SEARCH_TOP_K,
"queryType": "semantic" if AZURE_SEARCH_USE_SEMANTIC_SEARCH.lower() == "true" else "simple",
"semanticConfiguration": AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG if AZURE_SEARCH_USE_SEMANTIC_SEARCH.lower() == "true" and AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG else "",
"roleInformation": AZURE_OPENAI_SYSTEM_MESSAGE
}
}
]
}
chatgpt_url = f"https://{AZURE_OPENAI_RESOURCE}.openai.azure.com/openai/deployments/{AZURE_OPENAI_MODEL}"
if is_chat_model():
chatgpt_url += "/chat/completions?api-version=2023-03-15-preview"
else:
chatgpt_url += "/completions?api-version=2023-03-15-preview"
headers = {
'Content-Type': 'application/json',
'api-key': AZURE_OPENAI_KEY,
'chatgpt_url': chatgpt_url,
'chatgpt_key': AZURE_OPENAI_KEY,
"x-ms-useragent": "GitHubSampleWebApp/PublicAPI/1.0.0"
}
return body, headers
def stream_with_data(body, headers, endpoint):
s = requests.Session()
response = {
"id": "",
"model": "",
"created": 0,
"object": "",
"choices": [{
"messages": []
}]
}
try:
with s.post(endpoint, json=body, headers=headers, stream=True) as r:
for line in r.iter_lines(chunk_size=10):
if line:
lineJson = json.loads(line.lstrip(b'data:').decode('utf-8'))
if 'error' in lineJson:
yield json.dumps(lineJson).replace("\n", "\\n") + "\n"
response["id"] = lineJson["id"]
response["model"] = lineJson["model"]
response["created"] = lineJson["created"]
response["object"] = lineJson["object"]
role = lineJson["choices"][0]["messages"][0]["delta"].get("role")
if role == "tool":
response["choices"][0]["messages"].append(lineJson["choices"][0]["messages"][0]["delta"])
elif role == "assistant":
response["choices"][0]["messages"].append({
"role": "assistant",
"content": ""
})
else:
deltaText = lineJson["choices"][0]["messages"][0]["delta"]["content"]
if deltaText != "[DONE]":
response["choices"][0]["messages"][1]["content"] += deltaText
yield json.dumps(response).replace("\n", "\\n") + "\n"
except Exception as e:
yield json.dumps({"error": str(e)}).replace("\n", "\\n") + "\n"
def conversation_with_data(request):
body, headers = prepare_body_headers_with_data(request)
endpoint = f"https://{AZURE_OPENAI_RESOURCE}.openai.azure.com/openai/deployments/{AZURE_OPENAI_MODEL}/extensions/chat/completions?api-version={AZURE_OPENAI_PREVIEW_API_VERSION}"
if not SHOULD_STREAM:
r = requests.post(endpoint, headers=headers, json=body)
status_code = r.status_code
r = r.json()
return Response(json.dumps(r).replace("\n", "\\n"), status=status_code)
else:
if request.method == "POST":
return Response(stream_with_data(body, headers, endpoint), mimetype='text/event-stream')
else:
return Response(None, mimetype='text/event-stream')
def stream_without_data(response):
responseText = ""
for line in response:
deltaText = line["choices"][0]["delta"].get('content')
if deltaText and deltaText != "[DONE]":
responseText += deltaText
response_obj = {
"id": line["id"],
"model": line["model"],
"created": line["created"],
"object": line["object"],
"choices": [{
"messages": [{
"role": "assistant",
"content": responseText
}]
}]
}
yield json.dumps(response_obj).replace("\n", "\\n") + "\n"
def conversation_without_data(request):
openai.api_type = "azure"
openai.api_base = f"https://{AZURE_OPENAI_RESOURCE}.openai.azure.com/"
openai.api_version = "2023-03-15-preview"
openai.api_key = AZURE_OPENAI_KEY
request_messages = request.json["messages"]
messages = [
{
"role": "system",
"content": AZURE_OPENAI_SYSTEM_MESSAGE
}
]
for message in request_messages:
messages.append({
"role": message["role"] ,
"content": message["content"]
})
response = openai.ChatCompletion.create(
engine=AZURE_OPENAI_MODEL,
messages = messages,
temperature=float(AZURE_OPENAI_TEMPERATURE),
max_tokens=int(AZURE_OPENAI_MAX_TOKENS),
top_p=float(AZURE_OPENAI_TOP_P),
stop=AZURE_OPENAI_STOP_SEQUENCE.split("|") if AZURE_OPENAI_STOP_SEQUENCE else None,
stream=SHOULD_STREAM
)
if not SHOULD_STREAM:
response_obj = {
"id": response,
"model": response.model,
"created": response.created,
"object": response.object,
"choices": [{
"messages": [{
"role": "assistant",
"content": response.choices[0].message.content
}]
}]
}
return jsonify(response_obj), 200
else:
if request.method == "POST":
return Response(stream_without_data(response), mimetype='text/event-stream')
else:
return Response(None, mimetype='text/event-stream')
@app.route("/conversation", methods=["GET", "POST"])
def conversation():
try:
use_data = should_use_data()
if use_data:
return conversation_with_data(request)
else:
return conversation_without_data(request)
except Exception as e:
logging.exception("Exception in /conversation")
return jsonify({"error": str(e)}), 500
def allowSelfSignedHttps(allowed):
# bypass the server certificate verification on client side
if allowed and not os.environ.get('PYTHONHTTPSVERIFY', '') and getattr(ssl, '_create_unverified_context', None):
ssl._create_default_https_context = ssl._create_unverified_context
@app.route("/conversationllama", methods=["POST"])
def conversationLlama():
request_messages = request.json["messages"]
input_string = []
for request_msg in request_messages:
input_string.append({
"role": request_msg["role"],
"content": request_msg["content"]
})
# jsonArray = json.loads(request_messages)
# print(jsonArray)
allowSelfSignedHttps(True) # this line is needed if you use self-signed certificate in your scoring service.
# Request data goes here
# The example below assumes JSON formatting which may be updated
# depending on the format your endpoint expects.
# More information can be found here:
# https://docs.microsoft.com/azure/machine-learning/how-to-deploy-advanced-entry-script
print(input_string)
data = {
"input_data": {
"input_string": input_string,
"parameters": {
"temperature": 0.9,
"top_p": 0.6,
"do_sample": "True",
"max_new_tokens": 10000
}
}
}
body = str.encode(json.dumps(data))
url = 'https://llama-2-13b-chat-test-ep.westeurope.inference.ml.azure.com/score'
# Replace this with the primary/secondary key or AMLToken for the endpoint
api_key = AZURE_LLAMA2_KEY
if not api_key:
raise Exception("A key should be provided to invoke the endpoint")
# The azureml-model-deployment header will force the request to go to a specific deployment.
# Remove this header to have the request observe the endpoint traffic rules
headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ api_key), 'azureml-model-deployment': 'llama' }
req = urllib.request.Request(url, body, headers)
try:
response = urllib.request.urlopen(req)
result = response.read()
return result
# print("This is the response:"+response)
# return Response(stream_without_data(response), mimetype='text/event-stream')
except urllib.error.HTTPError as error:
print("The request failed with status code at /conversationllama: " + str(error.code))
# Print the headers - they include the requert ID and the timestamp, which are useful for debugging the failure
print(error.info())
print(error.read().decode("utf8", 'ignore'))
if __name__ == "__main__":
app.run()