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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 httpx
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,
convert_to_pf_format,
format_pf_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"
load_dotenv()
# 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)
# 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")
# Promptflow Integration Settings
USE_PROMPTFLOW = os.environ.get("USE_PROMPTFLOW", "false").lower() == "true"
PROMPTFLOW_ENDPOINT = os.environ.get("PROMPTFLOW_ENDPOINT")
PROMPTFLOW_API_KEY = os.environ.get("PROMPTFLOW_API_KEY")
PROMPTFLOW_RESPONSE_TIMEOUT = os.environ.get("PROMPTFLOW_RESPONSE_TIMEOUT", 30.0)
# default request and response field names are input -> 'query' and output -> 'reply'
PROMPTFLOW_REQUEST_FIELD_NAME = os.environ.get("PROMPTFLOW_REQUEST_FIELD_NAME", "query")
PROMPTFLOW_RESPONSE_FIELD_NAME = os.environ.get(
"PROMPTFLOW_RESPONSE_FIELD_NAME", "reply"
)
# 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,
}
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 promptflow_request(request):
try:
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {PROMPTFLOW_API_KEY}",
}
# Adding timeout for scenarios where response takes longer to come back
logging.debug(f"Setting timeout to {PROMPTFLOW_RESPONSE_TIMEOUT}")
async with httpx.AsyncClient(
timeout=float(PROMPTFLOW_RESPONSE_TIMEOUT)
) as client:
pf_formatted_obj = convert_to_pf_format(
request, PROMPTFLOW_REQUEST_FIELD_NAME, PROMPTFLOW_RESPONSE_FIELD_NAME
)
# NOTE: This only support question and chat_history parameters
# If you need to add more parameters, you need to modify the request body
response = await client.post(
PROMPTFLOW_ENDPOINT,
json={
f"{PROMPTFLOW_REQUEST_FIELD_NAME}": pf_formatted_obj[-1]["inputs"][
PROMPTFLOW_REQUEST_FIELD_NAME
],
"chat_history": pf_formatted_obj[:-1],
},
headers=headers,
)
resp = response.json()
resp["id"] = request["messages"][-1]["id"]
return resp
except Exception as e:
logging.error(f"An error occurred while making promptflow_request: {e}")
async def send_chat_request(request):
filtered_messages = [message for message in request['messages'] if message['role'] != 'tool']
request['messages'] = filtered_messages
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):
if USE_PROMPTFLOW and PROMPTFLOW_ENDPOINT and PROMPTFLOW_API_KEY:
response = await promptflow_request(request_body)
history_metadata = request_body.get("history_metadata", {})
return format_pf_non_streaming_response(
response, history_metadata, PROMPTFLOW_RESPONSE_FIELD_NAME
)
else:
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:
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