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app.py
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app.py
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import json
import os
import logging
import requests
import openai
import copy
from azure.identity import DefaultAzureCredential
from flask import Flask, Response, request, jsonify, send_from_directory
from dotenv import load_dotenv
from backend.auth.auth_utils import get_authenticated_user_details
from backend.history.cosmosdbservice import CosmosConversationClient
load_dotenv()
app = Flask(__name__, static_folder="static")
# Static Files
@app.route("/")
def index():
return app.send_static_file("index.html")
@app.route("/favicon.ico")
def favicon():
return app.send_static_file('favicon.ico')
@app.route("/assets/<path:path>")
def assets(path):
return send_from_directory("static/assets", path)
# Debug settings
DEBUG = os.environ.get("DEBUG", "false")
DEBUG_LOGGING = DEBUG.lower() == "true"
if DEBUG_LOGGING:
logging.basicConfig(level=logging.DEBUG)
# On Your Data Settings
DATASOURCE_TYPE = os.environ.get("DATASOURCE_TYPE", "AzureCognitiveSearch")
SEARCH_TOP_K = os.environ.get("SEARCH_TOP_K", 5)
SEARCH_STRICTNESS = os.environ.get("SEARCH_STRICTNESS", 3)
SEARCH_ENABLE_IN_DOMAIN = os.environ.get("SEARCH_ENABLE_IN_DOMAIN", "true")
# 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", SEARCH_TOP_K)
AZURE_SEARCH_ENABLE_IN_DOMAIN = os.environ.get("AZURE_SEARCH_ENABLE_IN_DOMAIN", SEARCH_ENABLE_IN_DOMAIN)
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")
AZURE_SEARCH_VECTOR_COLUMNS = os.environ.get("AZURE_SEARCH_VECTOR_COLUMNS")
AZURE_SEARCH_QUERY_TYPE = os.environ.get("AZURE_SEARCH_QUERY_TYPE")
AZURE_SEARCH_PERMITTED_GROUPS_COLUMN = os.environ.get("AZURE_SEARCH_PERMITTED_GROUPS_COLUMN")
AZURE_SEARCH_STRICTNESS = os.environ.get("AZURE_SEARCH_STRICTNESS", SEARCH_STRICTNESS)
# AOAI Integration Settings
AZURE_OPENAI_RESOURCE = os.environ.get("AZURE_OPENAI_RESOURCE")
AZURE_OPENAI_MODEL = os.environ.get("AZURE_OPENAI_MODEL")
AZURE_OPENAI_ENDPOINT = os.environ.get("AZURE_OPENAI_ENDPOINT")
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", "2023-08-01-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-16k") # Name of the model, e.g. 'gpt-35-turbo-16k' or 'gpt-4'
AZURE_OPENAI_EMBEDDING_ENDPOINT = os.environ.get("AZURE_OPENAI_EMBEDDING_ENDPOINT")
AZURE_OPENAI_EMBEDDING_KEY = os.environ.get("AZURE_OPENAI_EMBEDDING_KEY")
AZURE_OPENAI_EMBEDDING_NAME = os.environ.get("AZURE_OPENAI_EMBEDDING_NAME", "")
# CosmosDB Mongo vcore vector db Settings
AZURE_COSMOSDB_MONGO_VCORE_CONNECTION_STRING = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_CONNECTION_STRING") #This has to be secure string
AZURE_COSMOSDB_MONGO_VCORE_DATABASE = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_DATABASE")
AZURE_COSMOSDB_MONGO_VCORE_CONTAINER = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_CONTAINER")
AZURE_COSMOSDB_MONGO_VCORE_INDEX = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_INDEX")
AZURE_COSMOSDB_MONGO_VCORE_TOP_K = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_TOP_K", AZURE_SEARCH_TOP_K)
AZURE_COSMOSDB_MONGO_VCORE_STRICTNESS = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_STRICTNESS", AZURE_SEARCH_STRICTNESS)
AZURE_COSMOSDB_MONGO_VCORE_ENABLE_IN_DOMAIN = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_ENABLE_IN_DOMAIN", AZURE_SEARCH_ENABLE_IN_DOMAIN)
AZURE_COSMOSDB_MONGO_VCORE_CONTENT_COLUMNS = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_CONTENT_COLUMNS", "")
AZURE_COSMOSDB_MONGO_VCORE_FILENAME_COLUMN = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_FILENAME_COLUMN")
AZURE_COSMOSDB_MONGO_VCORE_TITLE_COLUMN = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_TITLE_COLUMN")
AZURE_COSMOSDB_MONGO_VCORE_URL_COLUMN = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_URL_COLUMN")
AZURE_COSMOSDB_MONGO_VCORE_VECTOR_COLUMNS = os.environ.get("AZURE_COSMOSDB_MONGO_VCORE_VECTOR_COLUMNS")
SHOULD_STREAM = True if AZURE_OPENAI_STREAM.lower() == "true" else False
# Chat History CosmosDB Integration Settings
AZURE_COSMOSDB_DATABASE = os.environ.get("AZURE_COSMOSDB_DATABASE")
AZURE_COSMOSDB_ACCOUNT = os.environ.get("AZURE_COSMOSDB_ACCOUNT")
AZURE_COSMOSDB_CONVERSATIONS_CONTAINER = os.environ.get("AZURE_COSMOSDB_CONVERSATIONS_CONTAINER")
AZURE_COSMOSDB_ACCOUNT_KEY = os.environ.get("AZURE_COSMOSDB_ACCOUNT_KEY")
# Initialize a CosmosDB client with AAD auth and containers for Chat History
cosmos_conversation_client = None
if AZURE_COSMOSDB_DATABASE and AZURE_COSMOSDB_ACCOUNT and AZURE_COSMOSDB_CONVERSATIONS_CONTAINER:
try :
cosmos_endpoint = f'https://{AZURE_COSMOSDB_ACCOUNT}.documents.azure.com:443/'
if not AZURE_COSMOSDB_ACCOUNT_KEY:
credential = DefaultAzureCredential()
else:
credential = AZURE_COSMOSDB_ACCOUNT_KEY
cosmos_conversation_client = CosmosConversationClient(
cosmosdb_endpoint=cosmos_endpoint,
credential=credential,
database_name=AZURE_COSMOSDB_DATABASE,
container_name=AZURE_COSMOSDB_CONVERSATIONS_CONTAINER
)
except Exception as e:
logging.exception("Exception in CosmosDB initialization", e)
cosmos_conversation_client = None
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:
if DEBUG_LOGGING:
logging.debug("Using Azure Cognitive Search")
return True
if AZURE_COSMOSDB_MONGO_VCORE_DATABASE and AZURE_COSMOSDB_MONGO_VCORE_CONTAINER and AZURE_COSMOSDB_MONGO_VCORE_INDEX and AZURE_COSMOSDB_MONGO_VCORE_CONNECTION_STRING:
if DEBUG_LOGGING:
logging.debug("Using Azure CosmosDB Mongo vcore")
return True
return False
def format_as_ndjson(obj: dict) -> str:
return json.dumps(obj, ensure_ascii=False) + "\n"
def fetchUserGroups(userToken, nextLink=None):
# Recursively fetch group membership
if nextLink:
endpoint = nextLink
else:
endpoint = "https://graph.microsoft.com/v1.0/me/transitiveMemberOf?$select=id"
headers = {
'Authorization': "bearer " + userToken
}
try :
r = requests.get(endpoint, headers=headers)
if r.status_code != 200:
if DEBUG_LOGGING:
logging.error(f"Error fetching user groups: {r.status_code} {r.text}")
return []
r = r.json()
if "@odata.nextLink" in r:
nextLinkData = fetchUserGroups(userToken, r["@odata.nextLink"])
r['value'].extend(nextLinkData)
return r['value']
except Exception as e:
logging.error(f"Exception in fetchUserGroups: {e}")
return []
def generateFilterString(userToken):
# Get list of groups user is a member of
userGroups = fetchUserGroups(userToken)
# Construct filter string
if not userGroups:
logging.debug("No user groups found")
group_ids = ", ".join([obj['id'] for obj in userGroups])
return f"{AZURE_SEARCH_PERMITTED_GROUPS_COLUMN}/any(g:search.in(g, '{group_ids}'))"
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": []
}
if DATASOURCE_TYPE == "AzureCognitiveSearch":
# Set query type
query_type = "simple"
if AZURE_SEARCH_QUERY_TYPE:
query_type = AZURE_SEARCH_QUERY_TYPE
elif AZURE_SEARCH_USE_SEMANTIC_SEARCH.lower() == "true" and AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG:
query_type = "semantic"
# Set filter
filter = None
userToken = None
if AZURE_SEARCH_PERMITTED_GROUPS_COLUMN:
userToken = request.headers.get('X-MS-TOKEN-AAD-ACCESS-TOKEN', "")
if DEBUG_LOGGING:
logging.debug(f"USER TOKEN is {'present' if userToken else 'not present'}")
filter = generateFilterString(userToken)
if DEBUG_LOGGING:
logging.debug(f"FILTER: {filter}")
body["dataSources"].append(
{
"type": "AzureCognitiveSearch",
"parameters": {
"endpoint": f"https://{AZURE_SEARCH_SERVICE}.search.windows.net",
"key": AZURE_SEARCH_KEY,
"indexName": AZURE_SEARCH_INDEX,
"fieldsMapping": {
"contentFields": 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,
"vectorFields": AZURE_SEARCH_VECTOR_COLUMNS.split("|") if AZURE_SEARCH_VECTOR_COLUMNS else []
},
"inScope": True if AZURE_SEARCH_ENABLE_IN_DOMAIN.lower() == "true" else False,
"topNDocuments": AZURE_SEARCH_TOP_K,
"queryType": query_type,
"semanticConfiguration": AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG if AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG else "",
"roleInformation": AZURE_OPENAI_SYSTEM_MESSAGE,
"filter": filter,
"strictness": int(AZURE_SEARCH_STRICTNESS)
}
})
elif DATASOURCE_TYPE == "AzureCosmosDB":
# Set query type
query_type = "vector"
body["dataSources"].append(
{
"type": "AzureCosmosDB",
"parameters": {
"connectionString": AZURE_COSMOSDB_MONGO_VCORE_CONNECTION_STRING,
"indexName": AZURE_COSMOSDB_MONGO_VCORE_INDEX,
"databaseName": AZURE_COSMOSDB_MONGO_VCORE_DATABASE,
"containerName": AZURE_COSMOSDB_MONGO_VCORE_CONTAINER,
"fieldsMapping": {
"contentFields": AZURE_COSMOSDB_MONGO_VCORE_CONTENT_COLUMNS.split("|") if AZURE_COSMOSDB_MONGO_VCORE_CONTENT_COLUMNS else [],
"titleField": AZURE_COSMOSDB_MONGO_VCORE_TITLE_COLUMN if AZURE_COSMOSDB_MONGO_VCORE_TITLE_COLUMN else None,
"urlField": AZURE_COSMOSDB_MONGO_VCORE_URL_COLUMN if AZURE_COSMOSDB_MONGO_VCORE_URL_COLUMN else None,
"filepathField": AZURE_COSMOSDB_MONGO_VCORE_FILENAME_COLUMN if AZURE_COSMOSDB_MONGO_VCORE_FILENAME_COLUMN else None,
"vectorFields": AZURE_COSMOSDB_MONGO_VCORE_VECTOR_COLUMNS.split("|") if AZURE_COSMOSDB_MONGO_VCORE_VECTOR_COLUMNS else []
},
"inScope": True if AZURE_COSMOSDB_MONGO_VCORE_ENABLE_IN_DOMAIN.lower() == "true" else False,
"topNDocuments": AZURE_COSMOSDB_MONGO_VCORE_TOP_K,
"strictness": int(AZURE_COSMOSDB_MONGO_VCORE_STRICTNESS),
"queryType": query_type,
"roleInformation": AZURE_OPENAI_SYSTEM_MESSAGE
}
})
else:
raise Exception(f"DATASOURCE_TYPE is not configured or unknown: {DATASOURCE_TYPE}")
if "vector" in query_type.lower():
if AZURE_OPENAI_EMBEDDING_NAME:
body["dataSources"][0]["parameters"]["embeddingDeploymentName"] = AZURE_OPENAI_EMBEDDING_NAME
else:
body["dataSources"][0]["parameters"]["embeddingEndpoint"] = AZURE_OPENAI_EMBEDDING_ENDPOINT
body["dataSources"][0]["parameters"]["embeddingKey"] = AZURE_OPENAI_EMBEDDING_KEY
if DEBUG_LOGGING:
body_clean = copy.deepcopy(body)
if body_clean["dataSources"][0]["parameters"].get("key"):
body_clean["dataSources"][0]["parameters"]["key"] = "*****"
if body_clean["dataSources"][0]["parameters"].get("connectionString"):
body_clean["dataSources"][0]["parameters"]["connectionString"] = "*****"
if body_clean["dataSources"][0]["parameters"].get("embeddingKey"):
body_clean["dataSources"][0]["parameters"]["embeddingKey"] = "*****"
logging.debug(f"REQUEST BODY: {json.dumps(body_clean, indent=4)}")
headers = {
'Content-Type': 'application/json',
'api-key': AZURE_OPENAI_KEY,
"x-ms-useragent": "GitHubSampleWebApp/PublicAPI/3.0.0"
}
return body, headers
def stream_with_data(body, headers, endpoint, history_metadata={}):
s = requests.Session()
try:
with s.post(endpoint, json=body, headers=headers, stream=True) as r:
for line in r.iter_lines(chunk_size=10):
response = {
"id": "",
"model": "",
"created": 0,
"object": "",
"choices": [{
"messages": []
}],
"apim-request-id": "",
'history_metadata': history_metadata
}
if line:
if AZURE_OPENAI_PREVIEW_API_VERSION == '2023-06-01-preview':
lineJson = json.loads(line.lstrip(b'data:').decode('utf-8'))
else:
try:
rawResponse = json.loads(line.lstrip(b'data:').decode('utf-8'))
lineJson = formatApiResponseStreaming(rawResponse)
except json.decoder.JSONDecodeError:
continue
if 'error' in lineJson:
yield format_as_ndjson(lineJson)
response["id"] = lineJson["id"]
response["model"] = lineJson["model"]
response["created"] = lineJson["created"]
response["object"] = lineJson["object"]
response["apim-request-id"] = r.headers.get('apim-request-id')
role = lineJson["choices"][0]["messages"][0]["delta"].get("role")
if role == "tool":
response["choices"][0]["messages"].append(lineJson["choices"][0]["messages"][0]["delta"])
yield format_as_ndjson(response)
elif role == "assistant":
if response['apim-request-id'] and DEBUG_LOGGING:
logging.debug(f"RESPONSE apim-request-id: {response['apim-request-id']}")
response["choices"][0]["messages"].append({
"role": "assistant",
"content": ""
})
yield format_as_ndjson(response)
else:
deltaText = lineJson["choices"][0]["messages"][0]["delta"]["content"]
if deltaText != "[DONE]":
response["choices"][0]["messages"].append({
"role": "assistant",
"content": deltaText
})
yield format_as_ndjson(response)
except Exception as e:
yield format_as_ndjson({"error" + str(e)})
def formatApiResponseNoStreaming(rawResponse):
if 'error' in rawResponse:
return {"error": rawResponse["error"]}
response = {
"id": rawResponse["id"],
"model": rawResponse["model"],
"created": rawResponse["created"],
"object": rawResponse["object"],
"choices": [{
"messages": []
}],
}
toolMessage = {
"role": "tool",
"content": rawResponse["choices"][0]["message"]["context"]["messages"][0]["content"]
}
assistantMessage = {
"role": "assistant",
"content": rawResponse["choices"][0]["message"]["content"]
}
response["choices"][0]["messages"].append(toolMessage)
response["choices"][0]["messages"].append(assistantMessage)
return response
def formatApiResponseStreaming(rawResponse):
if 'error' in rawResponse:
return {"error": rawResponse["error"]}
response = {
"id": rawResponse["id"],
"model": rawResponse["model"],
"created": rawResponse["created"],
"object": rawResponse["object"],
"choices": [{
"messages": []
}],
}
if rawResponse["choices"][0]["delta"].get("context"):
messageObj = {
"delta": {
"role": "tool",
"content": rawResponse["choices"][0]["delta"]["context"]["messages"][0]["content"]
}
}
response["choices"][0]["messages"].append(messageObj)
elif rawResponse["choices"][0]["delta"].get("role"):
messageObj = {
"delta": {
"role": "assistant",
}
}
response["choices"][0]["messages"].append(messageObj)
else:
if rawResponse["choices"][0]["end_turn"]:
messageObj = {
"delta": {
"content": "[DONE]",
}
}
response["choices"][0]["messages"].append(messageObj)
else:
messageObj = {
"delta": {
"content": rawResponse["choices"][0]["delta"]["content"],
}
}
response["choices"][0]["messages"].append(messageObj)
return response
def conversation_with_data(request_body):
body, headers = prepare_body_headers_with_data(request)
base_url = AZURE_OPENAI_ENDPOINT if AZURE_OPENAI_ENDPOINT else f"https://{AZURE_OPENAI_RESOURCE}.openai.azure.com/"
endpoint = f"{base_url}openai/deployments/{AZURE_OPENAI_MODEL}/extensions/chat/completions?api-version={AZURE_OPENAI_PREVIEW_API_VERSION}"
history_metadata = request_body.get("history_metadata", {})
if not SHOULD_STREAM:
r = requests.post(endpoint, headers=headers, json=body)
status_code = r.status_code
r = r.json()
if AZURE_OPENAI_PREVIEW_API_VERSION == "2023-06-01-preview":
r['history_metadata'] = history_metadata
return Response(format_as_ndjson(r), status=status_code)
else:
result = formatApiResponseNoStreaming(r)
result['history_metadata'] = history_metadata
return Response(format_as_ndjson(result), status=status_code)
else:
return Response(stream_with_data(body, headers, endpoint, history_metadata), mimetype='text/event-stream')
def stream_without_data(response, history_metadata={}):
responseText = ""
for line in response:
if line["choices"]:
deltaText = line["choices"][0]["delta"].get('content')
else:
deltaText = ""
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
}]
}],
"history_metadata": history_metadata
}
yield format_as_ndjson(response_obj)
def conversation_without_data(request_body):
openai.api_type = "azure"
openai.api_base = AZURE_OPENAI_ENDPOINT if AZURE_OPENAI_ENDPOINT else f"https://{AZURE_OPENAI_RESOURCE}.openai.azure.com/"
openai.api_version = "2023-08-01-preview"
openai.api_key = AZURE_OPENAI_KEY
request_messages = request_body["messages"]
messages = [
{
"role": "system",
"content": AZURE_OPENAI_SYSTEM_MESSAGE
}
]
for message in request_messages:
if message:
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
)
history_metadata = request_body.get("history_metadata", {})
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
}]
}],
"history_metadata": history_metadata
}
return jsonify(response_obj), 200
else:
return Response(stream_without_data(response, history_metadata), mimetype='text/event-stream')
@app.route("/conversation", methods=["GET", "POST"])
def conversation():
request_body = request.json
return conversation_internal(request_body)
def conversation_internal(request_body):
try:
use_data = should_use_data()
if use_data:
return conversation_with_data(request_body)
else:
return conversation_without_data(request_body)
except Exception as e:
logging.exception("Exception in /conversation")
return jsonify({"error": str(e)}), 500
## Conversation History API ##
@app.route("/history/generate", methods=["POST"])
def add_conversation():
authenticated_user = get_authenticated_user_details(request_headers=request.headers)
user_id = authenticated_user['user_principal_id']
## check request for conversation_id
conversation_id = request.json.get("conversation_id", None)
try:
# make sure cosmos is configured
if not cosmos_conversation_client:
raise Exception("CosmosDB is not configured")
# check for the conversation_id, if the conversation is not set, we will create a new one
history_metadata = {}
if not conversation_id:
title = generate_title(request.json["messages"])
conversation_dict = cosmos_conversation_client.create_conversation(user_id=user_id, title=title)
conversation_id = conversation_dict['id']
history_metadata['title'] = title
history_metadata['date'] = conversation_dict['createdAt']
## Format the incoming message object in the "chat/completions" messages format
## then write it to the conversation history in cosmos
messages = request.json["messages"]
if len(messages) > 0 and messages[-1]['role'] == "user":
cosmos_conversation_client.create_message(
conversation_id=conversation_id,
user_id=user_id,
input_message=messages[-1]
)
else:
raise Exception("No user message found")
# Submit request to Chat Completions for response
request_body = request.json
history_metadata['conversation_id'] = conversation_id
request_body['history_metadata'] = history_metadata
return conversation_internal(request_body)
except Exception as e:
logging.exception("Exception in /history/generate")
return jsonify({"error": str(e)}), 500
@app.route("/history/update", methods=["POST"])
def update_conversation():
authenticated_user = get_authenticated_user_details(request_headers=request.headers)
user_id = authenticated_user['user_principal_id']
## check request for conversation_id
conversation_id = request.json.get("conversation_id", None)
try:
# make sure cosmos is configured
if not cosmos_conversation_client:
raise Exception("CosmosDB is not configured")
# check for the conversation_id, if the conversation is not set, we will create a new one
if not conversation_id:
raise Exception("No conversation_id found")
## Format the incoming message object in the "chat/completions" messages format
## then write it to the conversation history in cosmos
messages = request.json["messages"]
if len(messages) > 0 and messages[-1]['role'] == "assistant":
if len(messages) > 1 and messages[-2].get('role', None) == "tool":
# write the tool message first
cosmos_conversation_client.create_message(
conversation_id=conversation_id,
user_id=user_id,
input_message=messages[-2]
)
# write the assistant message
cosmos_conversation_client.create_message(
conversation_id=conversation_id,
user_id=user_id,
input_message=messages[-1]
)
else:
raise Exception("No bot messages found")
# Submit request to Chat Completions for response
response = {'success': True}
return jsonify(response), 200
except Exception as e:
logging.exception("Exception in /history/update")
return jsonify({"error": str(e)}), 500
@app.route("/history/delete", methods=["DELETE"])
def delete_conversation():
## get the user id from the request headers
authenticated_user = get_authenticated_user_details(request_headers=request.headers)
user_id = authenticated_user['user_principal_id']
## check request for conversation_id
conversation_id = request.json.get("conversation_id", None)
try:
if not conversation_id:
return jsonify({"error": "conversation_id is required"}), 400
## delete the conversation messages from cosmos first
deleted_messages = cosmos_conversation_client.delete_messages(conversation_id, user_id)
## Now delete the conversation
deleted_conversation = cosmos_conversation_client.delete_conversation(user_id, conversation_id)
return jsonify({"message": "Successfully deleted conversation and messages", "conversation_id": conversation_id}), 200
except Exception as e:
logging.exception("Exception in /history/delete")
return jsonify({"error": str(e)}), 500
@app.route("/history/list", methods=["GET"])
def list_conversations():
authenticated_user = get_authenticated_user_details(request_headers=request.headers)
user_id = authenticated_user['user_principal_id']
## get the conversations from cosmos
conversations = cosmos_conversation_client.get_conversations(user_id)
if not isinstance(conversations, list):
return jsonify({"error": f"No conversations for {user_id} were found"}), 404
## return the conversation ids
return jsonify(conversations), 200
@app.route("/history/read", methods=["POST"])
def get_conversation():
authenticated_user = get_authenticated_user_details(request_headers=request.headers)
user_id = authenticated_user['user_principal_id']
## check request for conversation_id
conversation_id = request.json.get("conversation_id", None)
if not conversation_id:
return jsonify({"error": "conversation_id is required"}), 400
## get the conversation object and the related messages from cosmos
conversation = cosmos_conversation_client.get_conversation(user_id, conversation_id)
## return the conversation id and the messages in the bot frontend format
if not conversation:
return jsonify({"error": f"Conversation {conversation_id} was not found. It either does not exist or the logged in user does not have access to it."}), 404
# get the messages for the conversation from cosmos
conversation_messages = cosmos_conversation_client.get_messages(user_id, conversation_id)
## format the messages in the bot frontend format
messages = [{'id': msg['id'], 'role': msg['role'], 'content': msg['content'], 'createdAt': msg['createdAt']} for msg in conversation_messages]
return jsonify({"conversation_id": conversation_id, "messages": messages}), 200
@app.route("/history/rename", methods=["POST"])
def rename_conversation():
authenticated_user = get_authenticated_user_details(request_headers=request.headers)
user_id = authenticated_user['user_principal_id']
## check request for conversation_id
conversation_id = request.json.get("conversation_id", None)
if not conversation_id:
return jsonify({"error": "conversation_id is required"}), 400
## get the conversation from cosmos
conversation = cosmos_conversation_client.get_conversation(user_id, conversation_id)
if not conversation:
return jsonify({"error": f"Conversation {conversation_id} was not found. It either does not exist or the logged in user does not have access to it."}), 404
## update the title
title = request.json.get("title", None)
if not title:
return jsonify({"error": "title is required"}), 400
conversation['title'] = title
updated_conversation = cosmos_conversation_client.upsert_conversation(conversation)
return jsonify(updated_conversation), 200
@app.route("/history/delete_all", methods=["DELETE"])
def delete_all_conversations():
## get the user id from the request headers
authenticated_user = get_authenticated_user_details(request_headers=request.headers)
user_id = authenticated_user['user_principal_id']
# get conversations for user
try:
conversations = cosmos_conversation_client.get_conversations(user_id)
if not conversations:
return jsonify({"error": f"No conversations for {user_id} were found"}), 404
# delete each conversation
for conversation in conversations:
## delete the conversation messages from cosmos first
deleted_messages = cosmos_conversation_client.delete_messages(conversation['id'], user_id)
## Now delete the conversation
deleted_conversation = cosmos_conversation_client.delete_conversation(user_id, conversation['id'])
return jsonify({"message": f"Successfully deleted conversation and messages for user {user_id}"}), 200
except Exception as e:
logging.exception("Exception in /history/delete_all")
return jsonify({"error": str(e)}), 500
@app.route("/history/clear", methods=["POST"])
def clear_messages():
## get the user id from the request headers
authenticated_user = get_authenticated_user_details(request_headers=request.headers)
user_id = authenticated_user['user_principal_id']
## check request for conversation_id
conversation_id = request.json.get("conversation_id", None)
try:
if not conversation_id:
return jsonify({"error": "conversation_id is required"}), 400
## delete the conversation messages from cosmos
deleted_messages = cosmos_conversation_client.delete_messages(conversation_id, user_id)
return jsonify({"message": "Successfully deleted messages in conversation", "conversation_id": conversation_id}), 200
except Exception as e:
logging.exception("Exception in /history/clear_messages")
return jsonify({"error": str(e)}), 500
@app.route("/history/ensure", methods=["GET"])
def ensure_cosmos():
if not AZURE_COSMOSDB_ACCOUNT:
return jsonify({"error": "CosmosDB is not configured"}), 404
if not cosmos_conversation_client or not cosmos_conversation_client.ensure():
return jsonify({"error": "CosmosDB is not working"}), 500
return jsonify({"message": "CosmosDB is configured and working"}), 200
def generate_title(conversation_messages):
## make sure the messages are sorted by _ts descending
title_prompt = 'Summarize the conversation so far into a 4-word or less title. Do not use any quotation marks or punctuation. Respond with a json object in the format {{"title": string}}. Do not include any other commentary or description.'
messages = [{'role': msg['role'], 'content': msg['content']} for msg in conversation_messages]
messages.append({'role': 'user', 'content': title_prompt})
try:
## Submit prompt to Chat Completions for response
base_url = AZURE_OPENAI_ENDPOINT if AZURE_OPENAI_ENDPOINT else f"https://{AZURE_OPENAI_RESOURCE}.openai.azure.com/"
openai.api_type = "azure"
openai.api_base = base_url
openai.api_version = "2023-03-15-preview"
openai.api_key = AZURE_OPENAI_KEY
completion = openai.ChatCompletion.create(
engine=AZURE_OPENAI_MODEL,
messages=messages,
temperature=1,
max_tokens=64
)
title = json.loads(completion['choices'][0]['message']['content'])['title']
return title
except Exception as e:
return messages[-2]['content']
if __name__ == "__main__":
app.run()