Unlocking Azure Chat Openai Langchain Potential for Business

Azure Chat OpenAI LangChain is a powerful combination that can revolutionize the way businesses interact with their customers. It's a game-changer for customer service, allowing companies to provide personalized and efficient support.

By integrating OpenAI's natural language processing capabilities with LangChain's ability to reason and learn, Azure Chat can understand and respond to complex queries. This means businesses can automate routine tasks and free up human agents to focus on more strategic work.

One of the key benefits of Azure Chat OpenAI LangChain is its ability to learn from customer interactions. As the system processes more data, it becomes increasingly effective at anticipating and resolving common issues. This leads to improved customer satisfaction and reduced support costs.

By leveraging Azure Chat OpenAI LangChain, businesses can create a seamless and engaging customer experience that sets them apart from the competition.

For another approach, see: Chat with Your Data Azure

LangChain is an open-source framework that helps you build applications using LLMs (Large Language Models).

It connects LLMs like OpenAI's GPT-3.5 and GPT-4 with external data sources, such as your organization's data, YouTube videos, and articles.

You can use LangChain to integrate your own data, like one of my published articles, into your applications.

LangChain makes it possible to tap into the power of LLMs and combine it with your own data to create something truly unique.

This framework is designed to simplify the process of building applications that leverage LLMs and external data sources.

To set up Azure OpenAI, you need to create an Azure OpenAI service in the Azure portal. This involves clicking on Models in the Azure AI Studio and searching for the text-embedding-ada-002 and gpt-35-turbo models.

You can then click Deploy to deploy these models. Note that for later ChatGPT models like gpt-35-turbo and gpt4, you'll need to use the AzureChatOpenAI object instead of AzureOpenAI.

Here's a quick rundown of the differences between the two objects:

These changes will allow you to use the newer models with the Chat Completion API.

To set up Azure OpenAI, you'll need to create an Azure OpenAI service in the Azure portal. This is the first step in getting started with Azure OpenAI.

You'll also need to search and select the text-embedding-ada-002 and gpt-35-turbo models in the Azure AI Studio. These models are the foundation of your Azure OpenAI setup.

Once you've selected the models, click Deploy to get started with Azure OpenAI. This will enable you to use the models for various AI tasks.

Here are the specific steps to set up Azure OpenAI:

Note that the specific models you select may vary depending on your needs and goals with Azure OpenAI.

Active Directory Authentication is a more secure way to authenticate with Azure OpenAI, especially for complex security requirements. You can find more information on how to use Azure Active Directory with Azure OpenAI here.

There are two methods to authenticate with Azure OpenAI: API Key and Azure Active Directory (AAD). Using the API key is the easiest way to get started, but AAD is a better option if you need more security.

Explore further: Azure Openai Key

To use AAD in Azure OpenAI, you need to add a role, specifically the Cognitive Services OpenAI User role, to your Azure OpenAI resource. This role assignment allows you to get a token from AAD to use with Azure OpenAI.

You can grant the Cognitive Services OpenAI User role to a user, group, service principal, or managed identity. For more information about Azure OpenAI RBAC roles, see here.

To use AAD in Python with LangChain, you need to install the azure-identity package. Then, set the OPENAI_API_TYPE environment variable to azure_ad.

The DefaultAzureCredential class is an easy way to get started with AAD authentication. It tries to use Managed Identity first, and then falls back to the Azure CLI if necessary.

Readers also liked: Azure Identity

OpenAI Chat Completion is a powerful tool that allows you to integrate conversational AI into your applications. To use OpenAI Chat Completion, you'll need to use the Chat Completion API, which requires a specific JSON format for conversation history.

See what others are reading: Azure Chat

The Chat Completion API is used by the later ChatGPT models, such as gpt-35-turbo and gpt4. These models require changing the object classes to their chat counterparts, including AzureOpenAI to AzureChatOpenAI and PromptTemplate to ChatPromptTemplate.

The conversation history is stored in a memory object, and the ConversationSummaryBufferMemory is a simpler way to store the most recent conversation history word-per-word, while also summarizing the past conversation. This memory object is declared with a non-chat AzureOpenAI() to perform the summarization.

The max_tokens configuration is not a hard value that LangChain will follow, and it appears that even with low settings, the sample still sends ~1000 tokens for every message.

Here are the key differences between the two memory objects:

To store multiple conversations, you can use a dictionary to store multiple memory objects, each with a unique session ID. This way, each conversation is stored separately and can be retrieved later.

Azure Chat is a powerful tool that allows you to integrate OpenAI's ChatGPT models into your LangChain applications. To use Azure Chat, you'll need to switch from the old AzureOpenAI object to the new AzureChatOpenAI object, which requires a different JSON format for conversation history.

Additional reading: Azure Chat Completions Api

This means changing the object classes to their chat counterparts: AzureOpenAI → AzureChatOpenAI and PromptTemplate → ChatPromptTemplate. The code for this is shown in the example, where the chat history is stored in a specific JSON format.

The ConversationSummaryBufferMemory is a simpler way to store conversation history, but it requires a non-chat AzureOpenAI to perform the summarization. This memory stores the most recent conversation history word-per-word and summarizes the past conversation.

You can declare ConversationSummaryBufferMemory like this: `memory=ConversationSummaryBufferMemory(llm=llm,max_token_limit=CHAT_MEMORY_MAX_TOKENS,return_messages=True)`. The max_token_limit is the maximum number of tokens that can be stored in memory.

Here are some key settings for Azure Chat:

It's worth noting that the max_tokens configuration is not a hard value that LangChain will follow, and you may still send a large number of tokens for every message.

With Azure OpenAI, you set up your own deployments of the common GPT-3 and Codex models.

You can specify the deployment you want to use when calling the API, and you need to specify the deployment name.

For example, if your deployment name is gpt-35-turbo-instruct-prod, you can use the engine parameter in the openai Python API to specify this deployment.

To use a specific deployment, you need to know its name and use the engine parameter correctly.

You can use the gpt-35-turbo-instruct-prod deployment as an example of how to specify a deployment in the openai Python API.

For another approach, see: Azure Azure-common Python Module