langchain-azure-ai

This package contains the LangChain integration for Azure AI Foundry. To learn more about how to use this package, see the LangChain documentation in Azure AI Foundry.

[!NOTE] This package is in Public Preview. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Installation

pip install -U langchain-azure-ai

For using tracing capabilities with OpenTelemetry, you need to add the extras opentelemetry:

pip install -U langchain-azure-ai[opentelemetry]

Changelog

• 0.1.4:

  • Bug fix #91.

• 0.1.3:

  • [Breaking change]: We renamed the parameter model_name in AzureAIEmbeddingsModel and AzureAIChatCompletionsModel to model, which is the parameter expected by the method langchain.chat_models.init_chat_model.
  • We fixed an issue with JSON mode in chat models #81.
  • We fixed the dependencies for NumpPy #70.
  • We fixed an issue when tracing Pyndantic objects in the inputs #65.
  • We made connection_string parameter optional as suggested at #65.

• 0.1.2:

  • Bug fix #35.

• 0.1.1:

  • Adding AzureCosmosDBNoSqlVectorSearch and AzureCosmosDBNoSqlSemanticCache for vector search and full text search.
  • Adding AzureCosmosDBMongoVCoreVectorSearch and AzureCosmosDBMongoVCoreSemanticCache for vector search.
  • You can now create AzureAIEmbeddingsModel and AzureAIChatCompletionsModel clients directly from your AI project's connection string using the parameter project_connection_string. Your default Azure AI Services connection is used to find the model requested. This requires to have azure-ai-projects package installed.
  • Support for native LLM structure outputs. Use with_structured_output(method="json_schema") to use native structured schema support. Use with_structured_output(method="json_mode") to use native JSON outputs capabilities. By default, LangChain uses method="function_calling" which uses tool calling capabilities to generate valid structure JSON payloads. This requires to have azure-ai-inference >= 1.0.0b7.
  • Bug fix #18 and #31.

• 0.1.0:

  • Introduce AzureAIEmbeddingsModel for embedding generation and AzureAIChatCompletionsModel for chat completions generation using the Azure AI Inference API. This client also supports GitHub Models endpoint.
  • Introduce AzureAIInferenceTracer for tracing with OpenTelemetry and Azure Application Insights.