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app.py
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import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
import requests
# from openai import ChatCompletion, Choice, ChatCompletionMessage
from quart import (
Blueprint,
Quart,
jsonify,
make_response,
request,
send_from_directory,
render_template
)
from openai import AsyncAzureOpenAI
from azure.identity.aio import DefaultAzureCredential, get_bearer_token_provider
from backend.auth.auth_utils import get_authenticated_user_details
from backend.history.cosmosdbservice import CosmosConversationClient
from backend.utils import format_as_ndjson, format_stream_response, generateFilterString, parse_multi_columns, format_non_streaming_response
bp = Blueprint("routes", __name__, static_folder="static", template_folder="static")
# UI configuration (optional)
UI_TITLE = os.environ.get("UI_TITLE") or "ITL Financial Chatbot"
UI_LOGO = os.environ.get("UI_LOGO")
UI_CHAT_LOGO = os.environ.get("UI_CHAT_LOGO")
UI_CHAT_TITLE = os.environ.get("UI_CHAT_TITLE") or "Start chatting"
UI_CHAT_DESCRIPTION = os.environ.get("UI_CHAT_DESCRIPTION") or "This chatbot is configured to answer questions about ITL's finances"
UI_FAVICON = os.environ.get("UI_FAVICON") or "./assets/favicon-itl.ico"
UI_SHOW_SHARE_BUTTON = os.environ.get("UI_SHOW_SHARE_BUTTON", "true").lower() == "true"
def create_app():
app = Quart(__name__)
app.register_blueprint(bp)
app.config["TEMPLATES_AUTO_RELOAD"] = True
return app
@bp.route("/")
async def index():
return await render_template("index.html", title=UI_TITLE, favicon=UI_FAVICON)
@bp.route("/favicon.ico")
async def favicon():
return await bp.send_static_file("favicon.ico")
@bp.route("/assets/<path:path>")
async def assets(path):
return await send_from_directory("static/assets", path)
load_dotenv()
HACKATHON_BACKEND_API = os.environ.get("HACKATHON_BACKEND_API")
NUMBER_OF_CHAT_HISTORY_MESSAGES = os.environ.get("NUMBER_OF_CHAT_HISTORY_MESSAGES", 3)
# Debug settings
DEBUG = os.environ.get("DEBUG", "true")
if DEBUG.lower() == "true":
logging.basicConfig(level=logging.DEBUG)
USER_AGENT = "GitHubSampleWebApp/AsyncAzureOpenAI/1.0.0"
# 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", "false")
# 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", None)
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", 'gpt-4')
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", "2024-03-01-preview")
AZURE_OPENAI_STREAM = os.environ.get("AZURE_OPENAI_STREAM", "false")
AZURE_OPENAI_MODEL_NAME = os.environ.get("AZURE_OPENAI_MODEL_NAME", "gpt-4") # 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")
AZURE_COSMOSDB_ENABLE_FEEDBACK = os.environ.get("AZURE_COSMOSDB_ENABLE_FEEDBACK", "false").lower() == "true"
# Elasticsearch Integration Settings
ELASTICSEARCH_ENDPOINT = os.environ.get("ELASTICSEARCH_ENDPOINT")
ELASTICSEARCH_ENCODED_API_KEY = os.environ.get("ELASTICSEARCH_ENCODED_API_KEY")
ELASTICSEARCH_INDEX = os.environ.get("ELASTICSEARCH_INDEX")
ELASTICSEARCH_QUERY_TYPE = os.environ.get("ELASTICSEARCH_QUERY_TYPE", "simple")
ELASTICSEARCH_TOP_K = os.environ.get("ELASTICSEARCH_TOP_K", SEARCH_TOP_K)
ELASTICSEARCH_ENABLE_IN_DOMAIN = os.environ.get("ELASTICSEARCH_ENABLE_IN_DOMAIN", SEARCH_ENABLE_IN_DOMAIN)
ELASTICSEARCH_CONTENT_COLUMNS = os.environ.get("ELASTICSEARCH_CONTENT_COLUMNS")
ELASTICSEARCH_FILENAME_COLUMN = os.environ.get("ELASTICSEARCH_FILENAME_COLUMN")
ELASTICSEARCH_TITLE_COLUMN = os.environ.get("ELASTICSEARCH_TITLE_COLUMN")
ELASTICSEARCH_URL_COLUMN = os.environ.get("ELASTICSEARCH_URL_COLUMN")
ELASTICSEARCH_VECTOR_COLUMNS = os.environ.get("ELASTICSEARCH_VECTOR_COLUMNS")
ELASTICSEARCH_STRICTNESS = os.environ.get("ELASTICSEARCH_STRICTNESS", SEARCH_STRICTNESS)
ELASTICSEARCH_EMBEDDING_MODEL_ID = os.environ.get("ELASTICSEARCH_EMBEDDING_MODEL_ID")
# Pinecone Integration Settings
PINECONE_ENVIRONMENT = os.environ.get("PINECONE_ENVIRONMENT")
PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX_NAME")
PINECONE_TOP_K = os.environ.get("PINECONE_TOP_K", SEARCH_TOP_K)
PINECONE_STRICTNESS = os.environ.get("PINECONE_STRICTNESS", SEARCH_STRICTNESS)
PINECONE_ENABLE_IN_DOMAIN = os.environ.get("PINECONE_ENABLE_IN_DOMAIN", SEARCH_ENABLE_IN_DOMAIN)
PINECONE_CONTENT_COLUMNS = os.environ.get("PINECONE_CONTENT_COLUMNS", "")
PINECONE_FILENAME_COLUMN = os.environ.get("PINECONE_FILENAME_COLUMN")
PINECONE_TITLE_COLUMN = os.environ.get("PINECONE_TITLE_COLUMN")
PINECONE_URL_COLUMN = os.environ.get("PINECONE_URL_COLUMN")
PINECONE_VECTOR_COLUMNS = os.environ.get("PINECONE_VECTOR_COLUMNS")
# Azure AI MLIndex Integration Settings - for use with MLIndex data assets created in Azure AI Studio
AZURE_MLINDEX_NAME = os.environ.get("AZURE_MLINDEX_NAME")
AZURE_MLINDEX_VERSION = os.environ.get("AZURE_MLINDEX_VERSION")
AZURE_ML_PROJECT_RESOURCE_ID = os.environ.get("AZURE_ML_PROJECT_RESOURCE_ID") # /subscriptions/{sub ID}/resourceGroups/{rg name}/providers/Microsoft.MachineLearningServices/workspaces/{AML project name}
AZURE_MLINDEX_TOP_K = os.environ.get("AZURE_MLINDEX_TOP_K", SEARCH_TOP_K)
AZURE_MLINDEX_STRICTNESS = os.environ.get("AZURE_MLINDEX_STRICTNESS", SEARCH_STRICTNESS)
AZURE_MLINDEX_ENABLE_IN_DOMAIN = os.environ.get("AZURE_MLINDEX_ENABLE_IN_DOMAIN", SEARCH_ENABLE_IN_DOMAIN)
AZURE_MLINDEX_CONTENT_COLUMNS = os.environ.get("AZURE_MLINDEX_CONTENT_COLUMNS", "")
AZURE_MLINDEX_FILENAME_COLUMN = os.environ.get("AZURE_MLINDEX_FILENAME_COLUMN")
AZURE_MLINDEX_TITLE_COLUMN = os.environ.get("AZURE_MLINDEX_TITLE_COLUMN")
AZURE_MLINDEX_URL_COLUMN = os.environ.get("AZURE_MLINDEX_URL_COLUMN")
AZURE_MLINDEX_VECTOR_COLUMNS = os.environ.get("AZURE_MLINDEX_VECTOR_COLUMNS")
AZURE_MLINDEX_QUERY_TYPE = os.environ.get("AZURE_MLINDEX_QUERY_TYPE")
# Frontend Settings via Environment Variables
AUTH_ENABLED = os.environ.get("AUTH_ENABLED", "true").lower() == "true"
CHAT_HISTORY_ENABLED = AZURE_COSMOSDB_ACCOUNT and AZURE_COSMOSDB_DATABASE and AZURE_COSMOSDB_CONVERSATIONS_CONTAINER
frontend_settings = {
"auth_enabled": AUTH_ENABLED,
"feedback_enabled": AZURE_COSMOSDB_ENABLE_FEEDBACK and CHAT_HISTORY_ENABLED,
"ui": {
"title": UI_TITLE,
"logo": UI_LOGO,
"chat_logo": UI_CHAT_LOGO or UI_LOGO,
"chat_title": UI_CHAT_TITLE,
"chat_description": UI_CHAT_DESCRIPTION,
"show_share_button": UI_SHOW_SHARE_BUTTON
}
}
def should_use_data():
global DATASOURCE_TYPE
if AZURE_SEARCH_SERVICE and AZURE_SEARCH_INDEX:
DATASOURCE_TYPE = "AzureCognitiveSearch"
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:
DATASOURCE_TYPE = "AzureCosmosDB"
logging.debug("Using Azure CosmosDB Mongo vcore")
return True
if ELASTICSEARCH_ENDPOINT and ELASTICSEARCH_ENCODED_API_KEY and ELASTICSEARCH_INDEX:
DATASOURCE_TYPE = "Elasticsearch"
logging.debug("Using Elasticsearch")
return True
if PINECONE_ENVIRONMENT and PINECONE_API_KEY and PINECONE_INDEX_NAME:
DATASOURCE_TYPE = "Pinecone"
logging.debug("Using Pinecone")
return True
if AZURE_MLINDEX_NAME and AZURE_MLINDEX_VERSION and AZURE_ML_PROJECT_RESOURCE_ID:
DATASOURCE_TYPE = "AzureMLIndex"
logging.debug("Using Azure ML Index")
return True
return False
SHOULD_USE_DATA = should_use_data()
# Initialize Azure OpenAI Client
def init_openai_client(use_data=SHOULD_USE_DATA):
azure_openai_client = None
try:
# Endpoint
if not AZURE_OPENAI_ENDPOINT and not AZURE_OPENAI_RESOURCE:
raise Exception("AZURE_OPENAI_ENDPOINT or AZURE_OPENAI_RESOURCE is required")
endpoint = AZURE_OPENAI_ENDPOINT if AZURE_OPENAI_ENDPOINT else f"https://{AZURE_OPENAI_RESOURCE}.openai.azure.com/"
# Authentication
aoai_api_key = AZURE_OPENAI_KEY
ad_token_provider = None
if not aoai_api_key:
logging.debug("No AZURE_OPENAI_KEY found, using Azure AD auth")
ad_token_provider = get_bearer_token_provider(DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")
# Deployment
deployment = AZURE_OPENAI_MODEL
if not deployment:
raise Exception("AZURE_OPENAI_MODEL is required")
# Default Headers
default_headers = {
'x-ms-useragent': USER_AGENT
}
if use_data:
base_url = f"{str(endpoint).rstrip('/')}/openai/deployments/{deployment}/extensions"
azure_openai_client = AsyncAzureOpenAI(
base_url=str(base_url),
api_version=AZURE_OPENAI_PREVIEW_API_VERSION,
api_key=aoai_api_key,
azure_ad_token_provider=ad_token_provider,
default_headers=default_headers,
)
else:
azure_openai_client = AsyncAzureOpenAI(
api_version=AZURE_OPENAI_PREVIEW_API_VERSION,
api_key=aoai_api_key,
azure_ad_token_provider=ad_token_provider,
default_headers=default_headers,
azure_endpoint=endpoint
)
return azure_openai_client
except Exception as e:
logging.exception("Exception in Azure OpenAI initialization", e)
azure_openai_client = None
raise e
def init_cosmosdb_client():
cosmos_conversation_client = None
if CHAT_HISTORY_ENABLED:
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,
enable_message_feedback=AZURE_COSMOSDB_ENABLE_FEEDBACK
)
except Exception as e:
logging.exception("Exception in CosmosDB initialization", e)
cosmos_conversation_client = None
raise e
else:
logging.debug("CosmosDB not configured")
return cosmos_conversation_client
def get_configured_data_source():
data_source = {}
query_type = "simple"
if DATASOURCE_TYPE == "AzureCognitiveSearch":
# Set query type
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', "")
logging.debug(f"USER TOKEN is {'present' if userToken else 'not present'}")
if not userToken:
raise Exception("Document-level access control is enabled, but user access token could not be fetched.")
filter = generateFilterString(userToken)
logging.debug(f"FILTER: {filter}")
# Set authentication
authentication = {}
if AZURE_SEARCH_KEY:
authentication = {
"type": "APIKey",
"key": AZURE_SEARCH_KEY,
"apiKey": AZURE_SEARCH_KEY
}
else:
# If key is not provided, assume AOAI resource identity has been granted access to the search service
authentication = {
"type": "SystemAssignedManagedIdentity"
}
data_source = {
"type": "AzureCognitiveSearch",
"parameters": {
"endpoint": f"https://{AZURE_SEARCH_SERVICE}.search.windows.net",
"authentication": authentication,
"indexName": AZURE_SEARCH_INDEX,
"fieldsMapping": {
"contentFields": parse_multi_columns(AZURE_SEARCH_CONTENT_COLUMNS) 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": parse_multi_columns(AZURE_SEARCH_VECTOR_COLUMNS) if AZURE_SEARCH_VECTOR_COLUMNS else []
},
"inScope": True if AZURE_SEARCH_ENABLE_IN_DOMAIN.lower() == "true" else False,
"topNDocuments": int(AZURE_SEARCH_TOP_K) if AZURE_SEARCH_TOP_K else int(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) if AZURE_SEARCH_STRICTNESS else int(SEARCH_STRICTNESS)
}
}
elif DATASOURCE_TYPE == "AzureCosmosDB":
query_type = "vector"
data_source = {
"type": "AzureCosmosDB",
"parameters": {
"authentication": {
"type": "ConnectionString",
"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": parse_multi_columns(AZURE_COSMOSDB_MONGO_VCORE_CONTENT_COLUMNS) 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": parse_multi_columns(AZURE_COSMOSDB_MONGO_VCORE_VECTOR_COLUMNS) if AZURE_COSMOSDB_MONGO_VCORE_VECTOR_COLUMNS else []
},
"inScope": True if AZURE_COSMOSDB_MONGO_VCORE_ENABLE_IN_DOMAIN.lower() == "true" else False,
"topNDocuments": int(AZURE_COSMOSDB_MONGO_VCORE_TOP_K) if AZURE_COSMOSDB_MONGO_VCORE_TOP_K else int(SEARCH_TOP_K),
"strictness": int(AZURE_COSMOSDB_MONGO_VCORE_STRICTNESS) if AZURE_COSMOSDB_MONGO_VCORE_STRICTNESS else int(SEARCH_STRICTNESS),
"queryType": query_type,
"roleInformation": AZURE_OPENAI_SYSTEM_MESSAGE
}
}
elif DATASOURCE_TYPE == "Elasticsearch":
if ELASTICSEARCH_QUERY_TYPE:
query_type = ELASTICSEARCH_QUERY_TYPE
data_source = {
"type": "Elasticsearch",
"parameters": {
"endpoint": ELASTICSEARCH_ENDPOINT,
"authentication": {
"type": "EncodedAPIKey",
"encodedApiKey": ELASTICSEARCH_ENCODED_API_KEY
},
"indexName": ELASTICSEARCH_INDEX,
"fieldsMapping": {
"contentFields": parse_multi_columns(ELASTICSEARCH_CONTENT_COLUMNS) if ELASTICSEARCH_CONTENT_COLUMNS else [],
"titleField": ELASTICSEARCH_TITLE_COLUMN if ELASTICSEARCH_TITLE_COLUMN else None,
"urlField": ELASTICSEARCH_URL_COLUMN if ELASTICSEARCH_URL_COLUMN else None,
"filepathField": ELASTICSEARCH_FILENAME_COLUMN if ELASTICSEARCH_FILENAME_COLUMN else None,
"vectorFields": parse_multi_columns(ELASTICSEARCH_VECTOR_COLUMNS) if ELASTICSEARCH_VECTOR_COLUMNS else []
},
"inScope": True if ELASTICSEARCH_ENABLE_IN_DOMAIN.lower() == "true" else False,
"topNDocuments": int(ELASTICSEARCH_TOP_K) if ELASTICSEARCH_TOP_K else int(SEARCH_TOP_K),
"queryType": query_type,
"roleInformation": AZURE_OPENAI_SYSTEM_MESSAGE,
"strictness": int(ELASTICSEARCH_STRICTNESS) if ELASTICSEARCH_STRICTNESS else int(SEARCH_STRICTNESS)
}
}
elif DATASOURCE_TYPE == "AzureMLIndex":
if AZURE_MLINDEX_QUERY_TYPE:
query_type = AZURE_MLINDEX_QUERY_TYPE
data_source = {
"type": "AzureMLIndex",
"parameters": {
"name": AZURE_MLINDEX_NAME,
"version": AZURE_MLINDEX_VERSION,
"projectResourceId": AZURE_ML_PROJECT_RESOURCE_ID,
"fieldsMapping": {
"contentFields": parse_multi_columns(AZURE_MLINDEX_CONTENT_COLUMNS) if AZURE_MLINDEX_CONTENT_COLUMNS else [],
"titleField": AZURE_MLINDEX_TITLE_COLUMN if AZURE_MLINDEX_TITLE_COLUMN else None,
"urlField": AZURE_MLINDEX_URL_COLUMN if AZURE_MLINDEX_URL_COLUMN else None,
"filepathField": AZURE_MLINDEX_FILENAME_COLUMN if AZURE_MLINDEX_FILENAME_COLUMN else None,
"vectorFields": parse_multi_columns(AZURE_MLINDEX_VECTOR_COLUMNS) if AZURE_MLINDEX_VECTOR_COLUMNS else []
},
"inScope": True if AZURE_MLINDEX_ENABLE_IN_DOMAIN.lower() == "true" else False,
"topNDocuments": int(AZURE_MLINDEX_TOP_K) if AZURE_MLINDEX_TOP_K else int(SEARCH_TOP_K),
"queryType": query_type,
"roleInformation": AZURE_OPENAI_SYSTEM_MESSAGE,
"strictness": int(AZURE_MLINDEX_STRICTNESS) if AZURE_MLINDEX_STRICTNESS else int(SEARCH_STRICTNESS)
}
}
elif DATASOURCE_TYPE == "Pinecone":
query_type = "vector"
data_source = {
"type": "Pinecone",
"parameters": {
"environment": PINECONE_ENVIRONMENT,
"authentication": {
"type": "APIKey",
"key": PINECONE_API_KEY
},
"indexName": PINECONE_INDEX_NAME,
"fieldsMapping": {
"contentFields": parse_multi_columns(PINECONE_CONTENT_COLUMNS) if PINECONE_CONTENT_COLUMNS else [],
"titleField": PINECONE_TITLE_COLUMN if PINECONE_TITLE_COLUMN else None,
"urlField": PINECONE_URL_COLUMN if PINECONE_URL_COLUMN else None,
"filepathField": PINECONE_FILENAME_COLUMN if PINECONE_FILENAME_COLUMN else None,
"vectorFields": parse_multi_columns(PINECONE_VECTOR_COLUMNS) if PINECONE_VECTOR_COLUMNS else []
},
"inScope": True if PINECONE_ENABLE_IN_DOMAIN.lower() == "true" else False,
"topNDocuments": int(PINECONE_TOP_K) if PINECONE_TOP_K else int(SEARCH_TOP_K),
"strictness": int(PINECONE_STRICTNESS) if PINECONE_STRICTNESS else int(SEARCH_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() and DATASOURCE_TYPE != "AzureMLIndex":
embeddingDependency = {}
if AZURE_OPENAI_EMBEDDING_NAME:
embeddingDependency = {
"type": "DeploymentName",
"deploymentName": AZURE_OPENAI_EMBEDDING_NAME
}
elif AZURE_OPENAI_EMBEDDING_ENDPOINT and AZURE_OPENAI_EMBEDDING_KEY:
embeddingDependency = {
"type": "Endpoint",
"endpoint": AZURE_OPENAI_EMBEDDING_ENDPOINT,
"authentication": {
"type": "APIKey",
"key": AZURE_OPENAI_EMBEDDING_KEY
}
}
elif DATASOURCE_TYPE == "Elasticsearch" and ELASTICSEARCH_EMBEDDING_MODEL_ID:
embeddingDependency = {
"type": "ModelId",
"modelId": ELASTICSEARCH_EMBEDDING_MODEL_ID
}
else:
raise Exception(f"Vector query type ({query_type}) is selected for data source type {DATASOURCE_TYPE} but no embedding dependency is configured")
data_source["parameters"]["embeddingDependency"] = embeddingDependency
return data_source
def prepare_model_args(request_body):
request_messages = request_body.get("messages", [])
messages = []
if not SHOULD_USE_DATA:
messages = [
{
"role": "system",
"content": AZURE_OPENAI_SYSTEM_MESSAGE
}
]
for message in request_messages:
if message:
messages.append({
"role": message["role"] ,
"content": message["content"]
})
model_args = {
"messages": messages,
"temperature": float(AZURE_OPENAI_TEMPERATURE),
"max_tokens": int(AZURE_OPENAI_MAX_TOKENS),
"top_p": float(AZURE_OPENAI_TOP_P),
"stop": parse_multi_columns(AZURE_OPENAI_STOP_SEQUENCE) if AZURE_OPENAI_STOP_SEQUENCE else None,
"stream": SHOULD_STREAM,
"model": AZURE_OPENAI_MODEL,
}
if SHOULD_USE_DATA:
model_args["extra_body"] = {
"dataSources": [get_configured_data_source()]
}
model_args_clean = copy.deepcopy(model_args)
if model_args_clean.get("extra_body"):
secret_params = ["key", "connectionString", "embeddingKey", "encodedApiKey", "apiKey"]
for secret_param in secret_params:
if model_args_clean["extra_body"]["dataSources"][0]["parameters"].get(secret_param):
model_args_clean["extra_body"]["dataSources"][0]["parameters"][secret_param] = "*****"
authentication = model_args_clean["extra_body"]["dataSources"][0]["parameters"].get("authentication", {})
for field in authentication:
if field in secret_params:
model_args_clean["extra_body"]["dataSources"][0]["parameters"]["authentication"][field] = "*****"
embeddingDependency = model_args_clean["extra_body"]["dataSources"][0]["parameters"].get("embeddingDependency", {})
if "authentication" in embeddingDependency:
for field in embeddingDependency["authentication"]:
if field in secret_params:
model_args_clean["extra_body"]["dataSources"][0]["parameters"]["embeddingDependency"]["authentication"][field] = "*****"
logging.debug(f"REQUEST BODY: {json.dumps(model_args_clean, indent=4)}")
return model_args
async def send_chat_request(request):
# print("!!!Request", request, "Request!!!\n\n")
query = "Here is the user query - \n" + request['messages'][-1]['content']
chat_history = ""
# n = int(NUMBER_OF_CHAT_HISTORY_MESSAGES)
n = 5
num_chat_history = n if n < (len(request['messages']) - 1) else (len(request['messages']) - 1)
chat_history = []
i = 0
count = 0
for m in request['messages'][::-1]:
if count and m:
chat_history.append(m['role'] + ": " + m['content'] + "\n")
# j = 1
# while not request['messages'][-(count - j)]:
# j +=1
# chat_history += request['messages'][-(count - j)]['role'] + ": " + request['messages'][-(count - j)]['content'] + "\n"
if m['role'] == "user":
i += 1
if i >= num_chat_history:
break
count += 1
# print(chat_history)
i = 0
combine_history = ""
for x in chat_history[::-1]:
if i == 0:
combine_history += "Here is the chat history for context - \n"
combine_history += x
i += 1
# print("!!!message", combined_message , "message!!!\n\n")
request['messages'] = [request['messages'][-1]]
request['messages'][0]['content'] = combine_history + query
print("!!!messages", request['messages'] , "messages!!!\n\n")
model_args = prepare_model_args(request)
# print("!!!model_args", model_args, "model_args!!!\n\n")
try:
if HACKATHON_BACKEND_API:
# headers = {'Content-type': 'application/json'}
# connection = http.client.HTTPConnection(host=HACKATHON_BACKEND_API, port=80)
# print("!!!!!!!!!\n\n", request, "!!!!!!!!!\n\n\n")
# response = connection.request('POST', '/query', request)
response = requests.post(HACKATHON_BACKEND_API, json=request)
# Check the response
if response.status_code == 200:
print(f"Success! Response content: {response.content.decode('utf-8')}")
else:
print(f"Error! Status code: {response.status_code}")
# response =ChatCompletion(id='chatcmpl-94srCDUFFtQqiBcIlO3ZFtW4ARNwU', choices=[
# Choice(finish_reason='stop', index=0, logprobs=None, message=
# ChatCompletionMessage(content="Hello! It seems like you're testing the system. How can I assist you today? If you have any questions or need information, feel free to ask.",
# role='assistant', function_call=None, tool_calls=None),
# content_filter_results={'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'},
# 'violence': {'filtered': False, 'severity': 'safe'}})], created=1710951210, model='gpt-4', object='chat.completion',
# system_fingerprint='fp_46925cd5d1', usage=CompletionUsage(completion_tokens=32, prompt_tokens=23, total_tokens=55), prompt_filter_results=[{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}])
# azure_openai_client = init_openai_client()
# response = await azure_openai_client.chat.completions.create(**model_args)
# response = {
# "id": "cmpl-4kGh7iXtjW4lc9eGhff6Hp8C7btdQ",
# "object": "text_completion",
# "created": 1646932609,
# "model": "gpt-4",
# "choices": [
# {
# "text": ", a dark line crossed",
# "index": 0,
# "finish_reason": "length"
# }
# ]
# }
except Exception as e:
logging.exception("Exception in send_chat_request")
raise e
return response
async def complete_chat_request(request_body):
response = await send_chat_request(request_body)
history_metadata = request_body.get("history_metadata", {})
return format_aoai_backend_response(response, history_metadata)
def format_aoai_backend_response(response, history_metadata, message_uuid=None):
# print("!!!response", response, "response!!!\n\n")
new_response = json.loads(response.content)
# print( "n\n!!! new response", new_response, "new response!!! \n\n")
# print( "n\n!!! new response", new_response['result']['choices'][0]['text'], "new response!!! \n\n")
response_obj = {
"id": new_response['result']['id'],
"model": new_response['result']['model'],
"created": new_response['result']['created'],
"object": new_response['result']['object'],
"choices": [
{
"messages": [
{
"role": "assistant",
"content": new_response['result']['choices'][0]['text'],
}
],
}
],
"history_metadata": history_metadata
}
# if len(response.choices) > 0:
# message = new_response['result']['choices'][0]['text']
# if message:
# if hasattr(message, "context") and message.context.get("messages"):
# for m in message.context["messages"]:
# if m["role"] == "tool":
# response_obj["choices"][0]["messages"].append({
# "role": "tool",
# "content": m["content"]
# })
# elif hasattr(message, "context"):
# response_obj["choices"][0]["messages"].append({
# "role": "tool",
# "content": json.dumps(message.context),
# })
# response_obj["choices"][0]["messages"].append({
# "role": "assistant",
# "content": message.content,
# })
# return response_obj
# print(response_obj)
return response_obj
async def stream_chat_request(request_body):
response = await send_chat_request(request_body)
history_metadata = request_body.get("history_metadata", {})
async def generate():
async for completionChunk in response:
yield format_stream_response(completionChunk, history_metadata)
return generate()
async def conversation_internal(request_body):
try:
if SHOULD_STREAM:
result = await stream_chat_request(request_body)
response = await make_response(format_as_ndjson(result))
response.timeout = None
response.mimetype = "application/json-lines"
return response
else:
result = await complete_chat_request(request_body)
return jsonify(result)
except Exception as ex:
logging.exception(ex)
if hasattr(ex, "status_code"):
return jsonify({"error": str(ex)}), ex.status_code
else:
return jsonify({"error": str(ex)}), 500
@bp.route("/conversation", methods=["POST"])
async def conversation():
if not request.is_json:
return jsonify({"error": "request must be json"}), 415
request_json = await request.get_json()
result = await conversation_internal(request_json)
# print("!!Result\n\n", result, "Result!!\n\n")
return result
@bp.route("/frontend_settings", methods=["GET"])
def get_frontend_settings():
try:
return jsonify(frontend_settings), 200
except Exception as e:
logging.exception("Exception in /frontend_settings")
return jsonify({"error": str(e)}), 500
## Conversation History API ##
@bp.route("/history/generate", methods=["POST"])
async 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
request_json = await request.get_json()
conversation_id = request_json.get('conversation_id', None)
try:
# make sure cosmos is configured
cosmos_conversation_client = init_cosmosdb_client()
if not cosmos_conversation_client:
raise Exception("CosmosDB is not configured or not working")
# check for the conversation_id, if the conversation is not set, we will create a new one
history_metadata = {}
if not conversation_id:
title = await generate_title(request_json["messages"])
conversation_dict = await 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":
createdMessageValue = await cosmos_conversation_client.create_message(
uuid=str(uuid.uuid4()),
conversation_id=conversation_id,
user_id=user_id,
input_message=messages[-1]
)
if createdMessageValue == "Conversation not found":
raise Exception("Conversation not found for the given conversation ID: " + conversation_id + ".")
else:
raise Exception("No user message found")
await cosmos_conversation_client.cosmosdb_client.close()
# Submit request to Chat Completions for response
request_body = await request.get_json()
history_metadata['conversation_id'] = conversation_id
request_body['history_metadata'] = history_metadata
return await conversation_internal(request_body)
except Exception as e:
logging.exception("Exception in /history/generate")
return jsonify({"error": str(e)}), 500
@bp.route("/history/update", methods=["POST"])
async 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
request_json = await request.get_json()
conversation_id = request_json.get('conversation_id', None)
try:
# make sure cosmos is configured
cosmos_conversation_client = init_cosmosdb_client()
if not cosmos_conversation_client:
raise Exception("CosmosDB is not configured or not working")
# 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
await cosmos_conversation_client.create_message(
uuid=str(uuid.uuid4()),
conversation_id=conversation_id,
user_id=user_id,
input_message=messages[-2]
)
# write the assistant message
await cosmos_conversation_client.create_message(
uuid=messages[-1]['id'],
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
await cosmos_conversation_client.cosmosdb_client.close()
response = {'success': True}
return jsonify(response), 200
except Exception as e:
logging.exception("Exception in /history/update")
return jsonify({"error": str(e)}), 500
@bp.route("/history/message_feedback", methods=["POST"])
async def update_message():
authenticated_user = get_authenticated_user_details(request_headers=request.headers)
user_id = authenticated_user['user_principal_id']
cosmos_conversation_client = init_cosmosdb_client()
## check request for message_id
request_json = await request.get_json()
message_id = request_json.get('message_id', None)
message_feedback = request_json.get("message_feedback", None)
try:
if not message_id:
return jsonify({"error": "message_id is required"}), 400
if not message_feedback:
return jsonify({"error": "message_feedback is required"}), 400
## update the message in cosmos
updated_message = await cosmos_conversation_client.update_message_feedback(user_id, message_id, message_feedback)
if updated_message:
return jsonify({"message": f"Successfully updated message with feedback {message_feedback}", "message_id": message_id}), 200
else:
return jsonify({"error": f"Unable to update message {message_id}. It either does not exist or the user does not have access to it."}), 404
except Exception as e:
logging.exception("Exception in /history/message_feedback")
return jsonify({"error": str(e)}), 500
@bp.route("/history/delete", methods=["DELETE"])
async 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
request_json = await request.get_json()
conversation_id = request_json.get('conversation_id', None)
try:
if not conversation_id:
return jsonify({"error": "conversation_id is required"}), 400
## make sure cosmos is configured
cosmos_conversation_client = init_cosmosdb_client()
if not cosmos_conversation_client:
raise Exception("CosmosDB is not configured or not working")
## delete the conversation messages from cosmos first
deleted_messages = await cosmos_conversation_client.delete_messages(conversation_id, user_id)
## Now delete the conversation
deleted_conversation = await cosmos_conversation_client.delete_conversation(user_id, conversation_id)
await cosmos_conversation_client.cosmosdb_client.close()
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
@bp.route("/history/list", methods=["GET"])
async def list_conversations():
offset = request.args.get("offset", 0)
authenticated_user = get_authenticated_user_details(request_headers=request.headers)
user_id = authenticated_user['user_principal_id']
## make sure cosmos is configured
cosmos_conversation_client = init_cosmosdb_client()
if not cosmos_conversation_client:
raise Exception("CosmosDB is not configured or not working")
## get the conversations from cosmos
conversations = await cosmos_conversation_client.get_conversations(user_id, offset=offset, limit=25)
await cosmos_conversation_client.cosmosdb_client.close()
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
@bp.route("/history/read", methods=["POST"])
async 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
request_json = await request.get_json()
conversation_id = request_json.get('conversation_id', None)
if not conversation_id:
return jsonify({"error": "conversation_id is required"}), 400
## make sure cosmos is configured
cosmos_conversation_client = init_cosmosdb_client()
if not cosmos_conversation_client:
raise Exception("CosmosDB is not configured or not working")
## get the conversation object and the related messages from cosmos
conversation = await 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 = await 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'], 'feedback': msg.get('feedback')} for msg in conversation_messages]
await cosmos_conversation_client.cosmosdb_client.close()
return jsonify({"conversation_id": conversation_id, "messages": messages}), 200
@bp.route("/history/rename", methods=["POST"])
async 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
request_json = await request.get_json()
conversation_id = request_json.get('conversation_id', None)
if not conversation_id:
return jsonify({"error": "conversation_id is required"}), 400
## make sure cosmos is configured
cosmos_conversation_client = init_cosmosdb_client()
if not cosmos_conversation_client:
raise Exception("CosmosDB is not configured or not working")
## get the conversation from cosmos
conversation = await 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 = await cosmos_conversation_client.upsert_conversation(conversation)
await cosmos_conversation_client.cosmosdb_client.close()
return jsonify(updated_conversation), 200
@bp.route("/history/delete_all", methods=["DELETE"])
async 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:
## make sure cosmos is configured
cosmos_conversation_client = init_cosmosdb_client()
if not cosmos_conversation_client:
raise Exception("CosmosDB is not configured or not working")
conversations = await cosmos_conversation_client.get_conversations(user_id, offset=0, limit=None)
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 = await cosmos_conversation_client.delete_messages(conversation['id'], user_id)
## Now delete the conversation
deleted_conversation = await cosmos_conversation_client.delete_conversation(user_id, conversation['id'])
await cosmos_conversation_client.cosmosdb_client.close()