azure-ai-language-conversations-py

# 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. |