1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
|
# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_conversation_multi_turn_prediction_async.py
DESCRIPTION:
Run a multi-turn conversation prediction asynchronously using the
Conversational AI task. Prints intents and entities, including spans,
datetime resolutions, and subtype/tag metadata.
USAGE:
python sample_conversation_multi_turn_prediction_async.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_async]
import os
import asyncio
from azure.identity.aio import DefaultAzureCredential
from azure.ai.language.conversations.aio import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
# Request
ConversationalAITask,
ConversationalAIAnalysisInput,
ConversationalAIActionContent,
TextConversation,
TextConversationItem,
StringIndexType,
# Response/result discriminators
ConversationalAITaskResult,
ConversationalAIResult,
ConversationalAIAnalysis,
ConversationalAIIntent,
ConversationalAIEntity,
ConversationItemRange,
DateTimeResolution,
EntitySubtype,
EntityTag,
)
async def sample_conversation_multi_turn_prediction_async():
# 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()
async with ConversationAnalysisClient(endpoint, credential=credential) as client:
# 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,
),
)
# Async call
response = await client.analyze_conversation(data)
if isinstance(response, ConversationalAITaskResult):
ai_result: ConversationalAIResult = 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_async]
async def main():
await sample_conversation_multi_turn_prediction_async()
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
|
