Unlocking Langchain Azure Openai Gpt-4 for Enterprise Applications

Langchain's integration with Azure OpenAI GPT-4 opens up a world of possibilities for enterprise applications. This powerful combination enables businesses to leverage the full potential of large language models.

With Azure OpenAI GPT-4, enterprises can tap into a highly advanced language model that has been fine-tuned for a wide range of applications, including content generation, text classification, and question answering.

This integration allows for seamless deployment of GPT-4 models within Azure's scalable and secure infrastructure, making it an attractive option for large-scale enterprise applications.

To get started with langchain and Azure OpenAI, you'll need to set up a few things. You'll need to have Python 3.8 or later installed on your computer.

I use VS Code as my IDE, but you can choose any IDE that you're comfortable with. I also recommend using an Azure subscription with OpenAI service enabled, as it's required for this project. You can find more information about setting up an Azure subscription on the Azure website.

To follow along with this project, you'll need to create an Azure OpenAI service in the Azure portal. This will give you access to the OpenAI models that you'll be using.

Here's a quick rundown of the steps to set up Azure OpenAI:

You'll also need to set up an Azure SQL database, although you can use an on-prem SQL DB if you prefer.

Langchain is a platform that enables developers to build applications using OpenAI's GPT-4 model, which is a large language model that can process and generate human-like language.

The GPT-4 model is a fourth-generation transformer model that has been trained on a massive dataset of text from the internet, allowing it to generate coherent and context-specific responses.

Langchain's platform provides a set of tools and APIs that make it easy to integrate GPT-4 into applications, including a text interface and a Python API.

LangChain is an open-source framework that lets you build applications that can process natural language using Large Language Models (LLMs).

The Agent component of LangChain is a wrapper around an LLM, which decides the best steps to take to solve a problem. It has access to a set of functions called Tools, and can decide which Tool to use based on the user input.

Each Agent can perform various NLP tasks, such as parsing, calculations, and translation. These tasks help the Agent understand and process user input.

The Agent Executor is a runnable interface of the Agent and its set of Tools. It's responsible for calling the Agent, getting the action and input, and then passing all that information back into the Agent to get the next action.

An Agent Executor is typically used in an iterative process until the Agent reaches the Final Answer or output. This process involves calling the Agent, getting the action and input, and then passing all that information back into the Agent to get the next action.

The LangChain Agent can be used with the Azure OpenAI gpt-35-turbo model to query a SQL database using natural language. This means you can ask the Agent a question, and it will generate an SQL query to run on the database to get an answer.

To enhance the intelligence of our Basic Bot, we need to integrate it with Azure OpenAI. This can be done by adding the Azure OpenAI key to the environment file.

The first step is to update the runtime environment variables section in the teamsapp.local.yml file. We'll add the Azure OpenAI key to this file to enable the integration.

Adding the Azure OpenAI key will enable our Basic Bot to access the Azure OpenAI service and utilize its capabilities. This will significantly enhance the bot's intelligence and ability to understand and respond to user queries.

By integrating Azure OpenAI with our Basic Bot, we can unlock its full potential and create a more sophisticated and user-friendly experience.

Scalability and reliability are crucial aspects to consider when building with langchain, Azure OpenAI, and GPT-4. To achieve scalability, you don't need to pass the entire database to the Agent Toolkit, but rather select specific tables to work with.

If you're building a chatbot, your expected latency might not go beyond a certain number, but you should also consider the size of your database and individual tables. A good option is to identify subsets of tables for separate use cases and create multiple Agents pointing to different subsets of tables.

The average latency to produce an output involving joining all 3 tables is around 5 seconds, and there's no official information on the maximum database size. However, you can think of parameters like latency required by your application, database size, and rate and quota limits of your Azure OpenAI resource.

Here are some key factors to consider for scalability:

To ensure reliability, use case specific prompts can help improve reasoning for your use case, also known as temporary or in-context learning. Development in an iterative manner and evaluation along the way can take you to the right direction to develop an overall working system.

Scalability is a crucial aspect to consider when working with the Agent Toolkit. The average latency to produce an output involving joining all 3 tables is ~5 secs, but this can vary depending on your specific use case.

To determine your scalability requirements, you need to consider the latency required by your application. If you're building a chatbot, for example, your expected latency might not go beyond a certain number.

The size of your database is also a key factor. You'll need to take into account the size of individual tables, which you'll want to use for querying. Note that you don't need to pass the entire database to the Agent Toolkit - you can select specific tables to work with.

There are a few parameters to consider when determining your database size: the total size of the database, the size of individual tables, and the size of the tables you want to use for querying. You can also consider creating multiple Agents pointing to different subsets of tables to improve performance.

Here are some key parameters to keep in mind:

Ensuring reliability in a system is crucial for consistent and accurate responses. There is ongoing research on improving the reliability and robustness of Large Language Models (LLMs).

To improve reliability, we can use case-specific prompts that help the system reason more effectively for our specific use case, also known as temporary or in-context learning. This approach can be particularly useful when dealing with complex or nuanced topics.

We can only tweak our code, model parameters, and prompts on top of pre-trained LLMs, but we can do so in a targeted way. This means we can make adjustments without having to retrain the entire model.

Development should be done in an iterative manner, with evaluation along the way to help us get on the right track to building a reliable system.

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 can then navigate to the Azure AI Studio, where you'll find the Models section under Management. From there, search for and select the text-embedding-ada-002 and gpt-35-turbo models, and click Deploy.

Alternatively, you can use the OpenAI Node.js SDK, which is now integrated with Azure OpenAI, making it easier to switch between OpenAI and Azure OpenAI models.

Here are some key Azure OpenAI components you can reuse in your own projects:

Using Azure OpenAI is a game-changer for businesses because it fills the gap that other chat applications like ChatGPT leave behind.

One of the main reasons to choose Azure OpenAI is that it helps you connect to your organization's data, which is a crucial aspect of any business operation.

This is particularly useful because it allows you to integrate your AI models with your existing data sources, making it easier to get accurate and relevant information.

The OpenAI API used by these applications is great for responding to user prompts, but it's limited in its ability to access and use your organization's data.

LangChains, which is part of Azure OpenAI, bridges this gap by providing a way to connect your AI models to your organization's data, making it a more robust and effective solution.

This is especially important because it enables you to update your bot code to be more tailored to your business needs, making it a more valuable tool for your organization.

To set up Azure OpenAI, you'll need to follow these steps: create an Azure OpenAI service in the Azure portal, select the text-embedding-ada-002 and gpt-35-turbo models, and click Deploy.

You can find the necessary models by navigating to the Azure AI Studio and clicking on Models under Management in the left navigation.

The process of setting up Azure OpenAI is relatively straightforward, especially if you're familiar with Azure.

To deploy the models, simply click the Deploy button.

If you're migrating your prototype to Azure for production, switching to Azure OpenAI is as simple as changing the model initialization in your code.

You can use the OpenAI Node.js SDK to integrate Azure OpenAI with your existing code, making it easier to switch between OpenAI and Azure OpenAI models.

Here are some Azure AI building blocks you can reuse in your own projects:

Prototyping with LangChain.js is a great way to quickly build an AI application. LangChain.js is a JavaScript framework that provides a high-level API to interact with AI models and APIs.

It's also worth noting that LangChain.js has many built-in tools to make complex AI applications easier to build. This can save you a lot of time and effort when prototyping your AI application.

Prototyping is all about quickly testing and refining your ideas. LangChain.js is a JavaScript framework that makes it easier to build complex AI applications.

With LangChain.js, you can interact with AI models and APIs in a high-level way. This means you can focus on building your application without getting bogged down in the technical details.

Using LangChain.js for prototyping can save you a lot of time and effort. It provides many built-in tools that make it easier to get started with AI development.

You can use RAG (Retrieval-Augmented Generation) to ground answers using documents with LangChain.js. This example is a bit more complex, but it shows how you can build advanced AI scenarios in just a few lines of code.

The code for this example is in the index.js file, where you load a PDF document and split it into smaller chunks. These chunks are then converted to vectors, which are used in a multi-step workflow to perform a vector search and generate a response using the best results.

To run this code, you'll need to download the embeddings model, which is a small file (~50MB) that helps convert text to vectors. You can run the code with a command, and the resulting answer will directly come from the PDF document.

The Contoso Real Estate company used this principle to build a chatbot for their customers, allowing them to ask support questions about their products. The full source code of the project is available on GitHub.

This chatbot has a working prototype, and the final results include an added chat UI.