# SkillPatch skill: azure-ai-language-conversations-py

This skill guides agents in implementing Conversational Language Understanding (CLU) using the Azure AI Language Conversations Python SDK. It covers authentication patterns (DefaultAzureCredential and AzureKeyCredential), client lifecycle management, and best practices for building NLP features with ConversationAnalysisClient. It includes structured code examples and actionable instructions for both sync and async usage.

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---

## Skill files (1)

- `SKILL.md`


### `SKILL.md`

````markdown
---
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']}")
````