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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_conversation_multi_turn_prediction.py
DESCRIPTION:
Run a multi-turn conversation prediction synchronously using the
Conversational AI task. Prints intents and entities, including spans,
datetime resolutions, and subtype/tag metadata.
USAGE:
python sample_conversation_multi_turn_prediction.py
REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
AZURE_CONVERSATIONS_ENDPOINT
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
AZURE_CONVERSATIONS_PROJECT_NAME
AZURE_CONVERSATIONS_DEPLOYMENT_NAME
NOTE:
If you prefer `AzureKeyCredential`, set:
AZURE_CONVERSATIONS_ENDPOINT
AZURE_CONVERSATIONS_KEY
"""
# [START conversation_multi_turn_prediction]
import os
from azure.identity import DefaultAzureCredential
from azure.ai.language.conversations import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
ConversationalAITask,
ConversationalAIAnalysisInput,
ConversationalAIActionContent,
TextConversation,
TextConversationItem,
StringIndexType,
ConversationalAITaskResult,
DateTimeResolution,
EntitySubtype,
EntityTag,
)
def sample_conversation_multi_turn_prediction():
# get settings
endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
project_name = os.environ["AZURE_CONVERSATIONS_PROJECT_NAME"]
deployment_name = os.environ["AZURE_CONVERSATIONS_DEPLOYMENT_NAME"]
# AAD credential
credential = DefaultAzureCredential()
client = ConversationAnalysisClient(endpoint, credential=credential)
# Build a small multi-turn dialog
data = ConversationalAITask(
analysis_input=ConversationalAIAnalysisInput(
conversations=[
TextConversation(
id="order",
language="en-GB",
conversation_items=[
TextConversationItem(id="1", participant_id="user", text="Hi"),
TextConversationItem(id="2", participant_id="bot", text="Hello, how can I help you?"),
TextConversationItem(
id="3",
participant_id="user",
text="Send an email to Carol about tomorrow's demo",
),
],
)
]
),
parameters=ConversationalAIActionContent(
project_name=project_name,
deployment_name=deployment_name,
string_index_type=StringIndexType.UTF16_CODE_UNIT,
),
)
# Sync call
response = client.analyze_conversation(data)
if isinstance(response, ConversationalAITaskResult):
ai_result = response.result
if not ai_result or not ai_result.conversations:
print("No conversations found in result.")
return
for conversation in ai_result.conversations or []:
print(f"Conversation ID: {conversation.id}\n")
# Intents
print("Intents:")
for intent in conversation.intents or []:
print(f" Name: {intent.name}")
print(f" Type: {intent.type}")
print(" Conversation Item Ranges:")
for rng in intent.conversation_item_ranges or []:
print(f" - Offset: {rng.offset}, Count: {rng.count}")
print("\n Entities (Scoped to Intent):")
for ent in intent.entities or []:
print(f" Name: {ent.name}")
print(f" Text: {ent.text}")
print(f" Confidence: {ent.confidence_score}")
print(f" Offset: {ent.offset}, Length: {ent.length}")
print(
f" Conversation Item ID: {ent.conversation_item_id}, "
f"Index: {ent.conversation_item_index}"
)
# Date/time resolutions
for res in ent.resolutions or []:
if isinstance(res, DateTimeResolution):
print(
f" - [DateTimeResolution] SubKind: {res.date_time_sub_kind}, "
f"Timex: {res.timex}, Value: {res.value}"
)
# Extra information (entity subtype + tags)
for extra in ent.extra_information or []:
if isinstance(extra, EntitySubtype):
print(f" - [EntitySubtype] Value: {extra.value}")
for tag in extra.tags or []:
print(f" • Tag: {tag.name}, Confidence: {tag.confidence_score}")
print()
# Global entities
print("Global Entities:")
for ent in conversation.entities or []:
print(f" Name: {ent.name}")
print(f" Text: {ent.text}")
print(f" Confidence: {ent.confidence_score}")
print(f" Offset: {ent.offset}, Length: {ent.length}")
print(
f" Conversation Item ID: {ent.conversation_item_id}, " f"Index: {ent.conversation_item_index}"
)
for extra in ent.extra_information or []:
if isinstance(extra, EntitySubtype):
print(f" - [EntitySubtype] Value: {extra.value}")
for tag in extra.tags or []:
print(f" • Tag: {tag.name}, Confidence: {tag.confidence_score}")
print("-" * 40)
else:
print("No Conversational AI result returned.")
# [END conversation_multi_turn_prediction]
def main():
sample_conversation_multi_turn_prediction()
if __name__ == "__main__":
main()
|
