azure-ai-language-conversations-py
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
ソース情報
- リポジトリ
- microsoft/skills
- ソースの最終更新活動
- 2026年7月28日 21:46
- 検出された SKILL.md の言語
- 英語
- スター
- 3,063
- フォーク
- 351
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SKILL.md
ソースの指示 · 読み取り専用プレビュー- name
- azure-ai-language-conversations-py
- description
- Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
- license
- MIT
- metadata
- {"author":"Microsoft","version":"1.0.0"}
# Azure AI Language Conversations for Python
## System Prompt
You are an expert Python developer specializing in Azure AI Services and Natural Language Processing.
Your task is to help users implement Conversational Language Understanding (CLU) using the `azure-ai-language-conversations` SDK.
When responding to requests about Azure AI Language Conversations:
1. Always use the latest version of the `azure-ai-language-conversations` SDK.
2. Emphasize the use of `ConversationAnalysisClient` with `DefaultAzureCredential`.
3. Provide clear code examples demonstrating how to structure the conversation payload.
4. Handle exceptions properly.
## Authentication & Lifecycle
> **🔑 Two rules apply to every code sample below:**
>
> 1. **Prefer `DefaultAzureCredential`.** It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
> - Local dev: `DefaultAzureCredential` works as-is.
> - Production: set `AZURE_TOKEN_CREDENTIALS=prod` (or `AZURE_TOKEN_CREDENTIALS=<specific_credential>`) to constrain the credential chain to production-safe credentials.
> 2. **Wrap every client in a context manager** so HTTP transports, sockets, and token caches are released deterministically:
> - Sync: `with <Client>(...) as client:`
> - Async: `async with <Client>(...) as client:` **and** `async with DefaultAzureCredential() as credential:` (from `azure.identity.aio`)
>
> Snippets may abbreviate this setup, but production code should always follow both rules.
`ConversationAnalysisClient` accepts a `TokenCredential` such as `DefaultAzureCredential`. Use the token credential — it works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change.
### Legacy: API Key (existing keyed deployments)
New code should use `DefaultAzureCredential`. Use `AzureKeyCredential` only if you have an existing keyed deployment that hasn't been migrated to Entra ID yet — for example, regulated environments still completing their Entra rollout.
```python
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.language.conversations import ConversationAnalysisClient
endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
key = os.environ["AZURE_CONVERSATIONS_KEY"]
with ConversationAnalysisClient(endpoint, AzureKeyCredential(key)) as client:
# See "Basic Conversation Analysis" below for the analyze_conversation payload
...
```
## Best Practices
- **Pick sync OR async and stay consistent.** Do not mix `azure.ai.language.conversations` sync clients with `azure.ai.language.conversations.aio` async clients in the same call path. Choose one mode per module.
- **Always use context managers for clients and async credentials.** Wrap every client in `with ConversationAnalysisClient(...) as client:` (sync) or `async with ConversationAnalysisClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
- **Use `DefaultAzureCredential`** for portable auth across local dev and Azure (avoid API keys; they bypass Entra audit and rotation).
- Use environment variables for the endpoint, project name, and deployment name.
- Clearly map the `participantId` and `id` in the `conversationItem` payload.
## Examples
### Basic Conversation Analysis
```python
import os
from azure.identity import DefaultAzureCredential
from azure.ai.language.conversations import ConversationAnalysisClient
endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
project_name = os.environ["AZURE_CONVERSATIONS_PROJECT"]
deployment_name = os.environ["AZURE_CONVERSATIONS_DEPLOYMENT"]
# DefaultAzureCredential works locally and in Azure with no code change.
credential = DefaultAzureCredential()
with ConversationAnalysisClient(endpoint, credential) as client:
query = "Send an email to Carol about the tomorrow's meeting"
result = client.analyze_conversation(
task={
"kind": "Conversation",
"analysisInput": {
"conversationItem": {
"participantId": "1",
"id": "1",
"modality": "text",
"language": "en",
"text": query
},
"isLoggingEnabled": False
},
"parameters": {
"projectName": project_name,
"deploymentName": deployment_name,
"verbose": True
}
}
)
print(f"Top intent: {result['result']['prediction']['topIntent']}")
```
## Reference Files
| File | Contents |
|------|----------|
| [references/capabilities.md](references/capabilities.md) | Additional non-hero capabilities, operation-group coverage, and production checklists. |
| [references/non-hero-scenarios.md](references/non-hero-scenarios.md) | Dedicated non-hero examples for secondary/advanced scenarios. |
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