app.py

import copy
import json
import os
import logging
import uuid
from dotenv import load_dotenv
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.planners.sequential_planner import SequentialPlanner
from semantic_kernel.planners.stepwise_planner import StepwisePlanner
from semantic_kernel.core_plugins.math_plugin import MathPlugin

logger: logging.Logger = logging.getLogger(__name__)
kernel = sk.Kernel()

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.skills.license_operations import LicenseOperationsPlugin

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")

# Current minimum Azure OpenAI version supported
MINIMUM_SUPPORTED_AZURE_OPENAI_PREVIEW_API_VERSION="2024-02-15-preview"

# UI configuration (optional)
UI_TITLE = os.environ.get("UI_TITLE") or "Contoso"
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 your questions"
UI_FAVICON = os.environ.get("UI_FAVICON") or "/favicon.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()

# Debug settings
DEBUG = os.environ.get("DEBUG", "false")
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", "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", 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")
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", MINIMUM_SUPPORTED_AZURE_OPENAI_PREVIEW_API_VERSION)
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")
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
SANITIZE_ANSWER = os.environ.get("SANITIZE_ANSWER", "false").lower() == "true"
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
    },
    "sanitize_answer": SANITIZE_ANSWER
}

service_id = "aoai_chat"  # used later in the notebook
azure_chat_service = AzureChatCompletion(
    service_id=service_id, 
    deployment_name=AZURE_OPENAI_MODEL, 
    endpoint=AZURE_OPENAI_ENDPOINT, 
    api_key=AZURE_OPENAI_KEY
)  # set the deployment name to the value of your chat model
kernel.add_service(azure_chat_service)

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:
        # API version check
        if AZURE_OPENAI_PREVIEW_API_VERSION < MINIMUM_SUPPORTED_AZURE_OPENAI_PREVIEW_API_VERSION:
            raise Exception(f"The minimum supported Azure OpenAI preview API version is '{MINIMUM_SUPPORTED_AZURE_OPENAI_PREVIEW_API_VERSION}'")
        
        # 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
        }

        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": "api_key",
                "api_key": AZURE_SEARCH_KEY
            }
        else:
            # If key is not provided, assume AOAI resource identity has been granted access to the search service
            authentication = {
                "type": "system_assigned_managed_identity"
            }

        data_source = {
                "type": "azure_search",
                "parameters": {
                    "endpoint": f"https://{AZURE_SEARCH_SERVICE}.search.windows.net",
                    "authentication": authentication,
                    "index_name": AZURE_SEARCH_INDEX,
                    "fields_mapping": {
                        "content_fields": parse_multi_columns(AZURE_SEARCH_CONTENT_COLUMNS) if AZURE_SEARCH_CONTENT_COLUMNS else [],
                        "title_field": AZURE_SEARCH_TITLE_COLUMN if AZURE_SEARCH_TITLE_COLUMN else None,
                        "url_field": AZURE_SEARCH_URL_COLUMN if AZURE_SEARCH_URL_COLUMN else None,
                        "filepath_field": AZURE_SEARCH_FILENAME_COLUMN if AZURE_SEARCH_FILENAME_COLUMN else None,
                        "vector_fields": parse_multi_columns(AZURE_SEARCH_VECTOR_COLUMNS) if AZURE_SEARCH_VECTOR_COLUMNS else []
                    },
                    "in_scope": True if AZURE_SEARCH_ENABLE_IN_DOMAIN.lower() == "true" else False,
                    "top_n_documents": int(AZURE_SEARCH_TOP_K) if AZURE_SEARCH_TOP_K else int(SEARCH_TOP_K),
                    "query_type": query_type,
                    "semantic_configuration": AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG if AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG else "",
                    "role_information": 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": "azure_cosmos_db",
                "parameters": {
                    "authentication": {
                        "type": "connection_string",
                        "connection_string": AZURE_COSMOSDB_MONGO_VCORE_CONNECTION_STRING
                    },
                    "index_name": AZURE_COSMOSDB_MONGO_VCORE_INDEX,
                    "database_name": AZURE_COSMOSDB_MONGO_VCORE_DATABASE,
                    "container_name": AZURE_COSMOSDB_MONGO_VCORE_CONTAINER,                    
                    "fields_mapping": {
                        "content_fields": parse_multi_columns(AZURE_COSMOSDB_MONGO_VCORE_CONTENT_COLUMNS) if AZURE_COSMOSDB_MONGO_VCORE_CONTENT_COLUMNS else [],
                        "title_field": AZURE_COSMOSDB_MONGO_VCORE_TITLE_COLUMN if AZURE_COSMOSDB_MONGO_VCORE_TITLE_COLUMN else None,
                        "url_field": AZURE_COSMOSDB_MONGO_VCORE_URL_COLUMN if AZURE_COSMOSDB_MONGO_VCORE_URL_COLUMN else None,
                        "filepath_field": AZURE_COSMOSDB_MONGO_VCORE_FILENAME_COLUMN if AZURE_COSMOSDB_MONGO_VCORE_FILENAME_COLUMN else None,
                        "vector_fields": parse_multi_columns(AZURE_COSMOSDB_MONGO_VCORE_VECTOR_COLUMNS) if AZURE_COSMOSDB_MONGO_VCORE_VECTOR_COLUMNS else []
                    },
                    "in_scope": True if AZURE_COSMOSDB_MONGO_VCORE_ENABLE_IN_DOMAIN.lower() == "true" else False,
                    "top_n_documents": 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),
                    "query_type": query_type,
                    "role_information": 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": "encoded_api_key",
                    "encoded_api_key": ELASTICSEARCH_ENCODED_API_KEY
                },
                "index_name": ELASTICSEARCH_INDEX,
                "fields_mapping": {
                    "content_fields": parse_multi_columns(ELASTICSEARCH_CONTENT_COLUMNS) if ELASTICSEARCH_CONTENT_COLUMNS else [],
                    "title_field": ELASTICSEARCH_TITLE_COLUMN if ELASTICSEARCH_TITLE_COLUMN else None,
                    "url_field": ELASTICSEARCH_URL_COLUMN if ELASTICSEARCH_URL_COLUMN else None,
                    "filepath_field": ELASTICSEARCH_FILENAME_COLUMN if ELASTICSEARCH_FILENAME_COLUMN else None,
                    "vector_fields": parse_multi_columns(ELASTICSEARCH_VECTOR_COLUMNS) if ELASTICSEARCH_VECTOR_COLUMNS else []
                },
                "in_scope": True if ELASTICSEARCH_ENABLE_IN_DOMAIN.lower() == "true" else False,
                "top_n_documents": int(ELASTICSEARCH_TOP_K) if ELASTICSEARCH_TOP_K else int(SEARCH_TOP_K),
                "query_type": query_type,
                "role_information": 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": "azure_ml_index",
            "parameters": {
                "name": AZURE_MLINDEX_NAME,
                "version": AZURE_MLINDEX_VERSION,
                "project_resource_id": AZURE_ML_PROJECT_RESOURCE_ID,
                "fieldsMapping": {
                    "content_fields": parse_multi_columns(AZURE_MLINDEX_CONTENT_COLUMNS) if AZURE_MLINDEX_CONTENT_COLUMNS else [],
                    "title_field": AZURE_MLINDEX_TITLE_COLUMN if AZURE_MLINDEX_TITLE_COLUMN else None,
                    "url_field": AZURE_MLINDEX_URL_COLUMN if AZURE_MLINDEX_URL_COLUMN else None,
                    "filepath_field": AZURE_MLINDEX_FILENAME_COLUMN if AZURE_MLINDEX_FILENAME_COLUMN else None,
                    "vector_fields": parse_multi_columns(AZURE_MLINDEX_VECTOR_COLUMNS) if AZURE_MLINDEX_VECTOR_COLUMNS else []
                },
                "in_scope": True if AZURE_MLINDEX_ENABLE_IN_DOMAIN.lower() == "true" else False,
                "top_n_documents": int(AZURE_MLINDEX_TOP_K) if AZURE_MLINDEX_TOP_K else int(SEARCH_TOP_K),
                "query_type": query_type,
                "role_information": 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": "api_key",
                    "key": PINECONE_API_KEY
                },
                "index_name": PINECONE_INDEX_NAME,
                "fields_mapping": {
                    "content_fields": parse_multi_columns(PINECONE_CONTENT_COLUMNS) if PINECONE_CONTENT_COLUMNS else [],
                    "title_field": PINECONE_TITLE_COLUMN if PINECONE_TITLE_COLUMN else None,
                    "url_field": PINECONE_URL_COLUMN if PINECONE_URL_COLUMN else None,
                    "filepath_field": PINECONE_FILENAME_COLUMN if PINECONE_FILENAME_COLUMN else None,
                    "vector_fields": parse_multi_columns(PINECONE_VECTOR_COLUMNS) if PINECONE_VECTOR_COLUMNS else []
                },
                "in_scope": True if PINECONE_ENABLE_IN_DOMAIN.lower() == "true" else False,
                "top_n_documents": int(PINECONE_TOP_K) if PINECONE_TOP_K else int(SEARCH_TOP_K),
                "strictness": int(PINECONE_STRICTNESS) if PINECONE_STRICTNESS else int(SEARCH_STRICTNESS),
                "query_type": query_type,
                "role_information": 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": "deployment_name",
                "deployment_name": AZURE_OPENAI_EMBEDDING_NAME
            }
        elif AZURE_OPENAI_EMBEDDING_ENDPOINT and AZURE_OPENAI_EMBEDDING_KEY:
            embeddingDependency = {
                "type": "endpoint",
                "endpoint": AZURE_OPENAI_EMBEDDING_ENDPOINT,
                "authentication": {
                    "type": "api_key",
                    "key": AZURE_OPENAI_EMBEDDING_KEY
                }
            }
        elif DATASOURCE_TYPE == "Elasticsearch" and ELASTICSEARCH_EMBEDDING_MODEL_ID:
            embeddingDependency = {
                "type": "model_id",
                "model_id": 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"]["embedding_dependency"] = 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"] = {
            "data_sources": [get_configured_data_source()]
        }

    model_args_clean = copy.deepcopy(model_args)
    if model_args_clean.get("extra_body"):
        secret_params = ["key", "connection_string", "embedding_key", "encoded_api_key", "api_key"]
        for secret_param in secret_params:
            if model_args_clean["extra_body"]["data_sources"][0]["parameters"].get(secret_param):
                model_args_clean["extra_body"]["data_sources"][0]["parameters"][secret_param] = "*****"
        authentication = model_args_clean["extra_body"]["data_sources"][0]["parameters"].get("authentication", {})
        for field in authentication:
            if field in secret_params:
                model_args_clean["extra_body"]["data_sources"][0]["parameters"]["authentication"][field] = "*****"
        embeddingDependency = model_args_clean["extra_body"]["data_sources"][0]["parameters"].get("embedding_dependency", {})
        if "authentication" in embeddingDependency:
            for field in embeddingDependency["authentication"]:
                if field in secret_params:
                    model_args_clean["extra_body"]["data_sources"][0]["parameters"]["embedding_dependency"]["authentication"][field] = "*****"
        
    logging.debug(f"REQUEST BODY: {json.dumps(model_args_clean, indent=4)}")
    
    return model_args

async def send_chat_request(request):
    model_args = prepare_model_args(request)

    try:
        azure_openai_client = init_openai_client()
        response = await azure_openai_client.chat.completions.create(**model_args)

    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_non_streaming_response(response, history_metadata)

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: 
        kernel.import_plugin_from_object(LicenseOperationsPlugin(), plugin_name="LicenseOperationsSkillPlugin")
        kernel.import_plugin_from_object(MathPlugin(), plugin_name="MathPlugin")
        
        history = ""
        for message in request_body["messages"][0:-1]:
            if("role" in message and "content" in message):
                history += f"{message['role']} wrote \"{message['content']}\"\n"
        
        prompt = "You are a chatbot that helps people find non-technical information."
        if(history != ""):
            prompt += f"Previous context: { history }\n"

        prompt += f"Current message: {request_body['messages'][-1]['role']} wrote \"{request_body['messages'][-1]['content']}\""
        
        # Calculates the plan from available functions and throw an exception if no function is suitable for user input
        planner = SequentialPlanner(kernel, service_id)
        plan = await planner.create_plan(prompt)

        planner = StepwisePlanner(kernel)
        plan = planner.create_plan(prompt)
        
        # Execute the plan
        result = await plan.invoke(kernel)
        response = await make_response({
            "choices": [
                {
                    "messages": [
                        {
                            "content": str(result),
                            "role": "assistant"
                        }
                    ]
                }
            ],
            "history_metadata": {},
            "object": "chat.completion"
        })
        response.timeout = None
        response.mimetype = "application/json-lines"

        return response
    except Exception as ex:
        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()
    
    return await conversation_internal(request_json)

@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()
        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

@bp.route("/history/clear", methods=["POST"])
async 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
    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
        deleted_messages = await 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


@bp.route("/history/ensure", methods=["GET"])
async def ensure_cosmos():
    if not AZURE_COSMOSDB_ACCOUNT:
        return jsonify({"error": "CosmosDB is not configured"}), 404
    
    try:
        cosmos_conversation_client = init_cosmosdb_client()
        success, err = await cosmos_conversation_client.ensure()
        if not cosmos_conversation_client or not success:
            if err:
                return jsonify({"error": err}), 422
            return jsonify({"error": "CosmosDB is not configured or not working"}), 500
        
        await cosmos_conversation_client.cosmosdb_client.close()
        return jsonify({"message": "CosmosDB is configured and working"}), 200
    except Exception as e:
        logging.exception("Exception in /history/ensure")
        cosmos_exception = str(e)
        if "Invalid credentials" in cosmos_exception:
            return jsonify({"error": cosmos_exception}), 401
        elif "Invalid CosmosDB database name" in cosmos_exception:
            return jsonify({"error": f"{cosmos_exception} {AZURE_COSMOSDB_DATABASE} for account {AZURE_COSMOSDB_ACCOUNT}"}), 422
        elif "Invalid CosmosDB container name" in cosmos_exception:
            return jsonify({"error": f"{cosmos_exception}: {AZURE_COSMOSDB_CONVERSATIONS_CONTAINER}"}), 422
        else:
            return jsonify({"error": "CosmosDB is not working"}), 500


async 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:
        azure_openai_client = init_openai_client(use_data=False)
        response = await azure_openai_client.chat.completions.create(
            model=AZURE_OPENAI_MODEL,
            messages=messages,
            temperature=1,
            max_tokens=64
        )
        
        title = json.loads(response.choices[0].message.content)['title']
        return title
    except Exception as e:
        return messages[-2]['content']


app = create_app()