Does Langchain Support Azure Integration with Chat Models

Langchain does support Azure integration with chat models, but it requires some extra setup.

You can integrate Langchain with Azure using the Azure Cognitive Services Text Analytics API, which allows you to analyze text and extract insights.

This integration enables you to use Azure's natural language processing capabilities with Langchain's chat models, creating a powerful combination for text-based applications.

To integrate Azure OpenAI with LangChain, you need to follow a structured approach that ensures seamless interaction between the two platforms.

You can start by deploying an instance on the Azure Portal, following the official guide to create your resource. This will allow you to set up your instance and begin integrating it with your applications.

Once your instance is ready, you can start integrating it with LangChain, leveraging the powerful capabilities of Azure's OpenAI models within the LangChain framework.

With the setup complete, you can now utilize Azure OpenAI models in your LangChain applications, including creating an Azure Chat model that sends a prompt and receives a response.

Azure OpenAI provides access to advanced models such as GPT-3, Codex, and various embedding models, which can be leveraged for diverse applications including content generation, summarization, and natural language processing tasks.

To utilize AzureChatOpenAI, you can use the AzureChatOpenAI class from the LangChain library, which allows you to interact with the chat models seamlessly.

You can create an Azure Chat model by initializing it and sending a prompt to it, receiving a response that can be used in your application.

With the environment set up, you can now use AzureChatOpenAI to interact with the chat models, making it easier to develop AI-driven applications.

To configure vector store on Azure, you need an Azure subscription and Azure AI Search service, which are available for small and limited workloads at no cost.

You'll need to set variables for your Azure AI Search URL and admin API key, which can be obtained from the Azure portal.

With these variables in place, you can create instances of the OpenAIEmbeddings and AzureSearch classes, resulting in an empty search index on your Azure AI Search resource with a default schema provided by the integration module.

To configure vector store settings, you need an Azure subscription and an Azure AI Search service. These are the necessary components for using the vector store integration.

You can get started with no-cost versions of Azure for small and limited workloads. This is a great option for testing or small-scale projects.

First, set variables for your Azure AI Search URL. This is the URL where your Azure AI Search service is hosted. You can find this information in the Azure portal.

Next, set the admin API key. This is a unique key that grants access to your Azure AI Search service's administrative API. You can also find this information in the Azure portal.

To securely store and manage credentials separately from your code, you should convert environmental variables into a prompt flow connection. This is better than exposing credentials as environment variables when running an Azure Machine Learning prompt flow in the cloud.

To create a connection that securely stores custom credentials, such as your LLM API key or other required keys, follow these steps:

At least one value must be set as secret to create a custom connection. You can store multiple credentials and keys in the same connection.

To create embeddings and vector stores in Azure, you need an Azure subscription and an Azure AI Search service, which are available for small and limited workloads at no cost.

You'll need to set variables for your Azure AI Search URL and admin API key, which can be obtained from the Azure portal.

First, create instances of the OpenAIEmbeddings and AzureSearch classes. This will create an empty search index on your Azure AI Search resource, with a default schema provided by the integration module.

To proceed, ensure you have an Azure AI Search resource set up, as the integration module relies on it to function.

You can convert your LangChain code into a runnable Azure Machine Learning prompt flow using the process outlined in the Azure documentation. This involves selecting a flow type and authoring the flow in the Azure Machine Learning studio.

To create a flow, start by selecting "Create" on the Prompt flow page in Azure Machine Learning studio, and choose a flow type. Then, select tool types at the top of the page to insert corresponding nodes into the flow.

You can directly run your LangChain code in Python nodes in your flow, as long as your compute session contains the langchain package dependency. This means you don't need to modify your code to work with Azure Machine Learning.

There are two ways to convert your LangChain code into an Azure Machine Learning prompt flow, depending on your use case. You can either use Azure Machine Learning Python and prompt tools in the flow, or call the LangChain LLM library directly from within Python nodes.

Here are the two options in more detail:

This means you can choose the approach that best fits your needs, whether you want to easily manage and tune your prompts or optimize your code for faster testing.