Master Azure OpenAI C# API

Table of Contents

1. Introduction
2. Setting up the Azure Open AI deployment
3. Retrieving configuration options for the code
4. Implementing the non-streaming chat option
5. Exploring the options for chat completions
6. Understanding the parameters for model functions
7. Using temperature for response randomness
8. Using nucleus sampling factor for creativity control
9. Using frequency penalty and presence penalty for word repetition control
10. Implementing the non-streaming chat loop
11. Implementing the streaming chat option
12. Conclusion

Introduction

In this tutorial, I will guide You through the process of coding a simple chat client using C# and Azure Open AI. We will cover the steps involved in setting up the Azure Open AI deployment and retrieving the necessary configuration options. We will also explore both the non-streaming and streaming chat options and discuss how to implement them in your code. By the end of this tutorial, you will have a basic understanding of how to utilize the Azure Open AI platform to Create a chat client.

Setting up the Azure Open AI deployment

Before we can start coding our chat client, we need to create a deployment of a model in Azure Open AI. To do this, follow these steps:

1. Go to oai.azure.com.
2. Navigate to the management section and click on deployments.
3. If you don't have a deployment already, click on "Create new deployment".
4. Choose the model you wish to use, either the latest version or a specific version.
5. Give your deployment a name and make sure to remember it for later use.

Once you have created your deployment, select it and click "Open in Playground". This will allow you to retrieve the configuration options needed for our code.

Retrieving configuration options for the code

In the Playground, switch to the C# view to access the code. Here, you will find the necessary information for your code:

1. URI: Copy the URI for your deployment.
2. Model name: Use the name you assigned to your deployment in the previous step.
3. API key: Copy the API key provided.

Make sure to retrieve these three pieces of information as we will need them in the code implementation.

Implementing the non-streaming chat option

In our code example, we will create two implementations of the chat client: one using streaming and one without streaming. Let's first explore the non-streaming option.

To implement the non-streaming chat option, you need to follow these steps:

1. Include the azure.ai.OpenAI Package in your console application. You can retrieve this package from NuGet.
2. Create an OpenAIClient object by passing the URL to your Azure Open AI resource and the secret key obtained earlier.
3. Create a ChatCompletionOptions object and pass it into the API later. This object contains the settings that determine how the model functions.
4. Set the messages option in the ChatCompletionOptions. This option requires at least one message, which is a system message with the role "system". This system message provides guidance to the language model on the kind of content you want it to answer questions about and how you want it formatted.
5. Set additional options such as maxTokens, temperature, nucleusSamplingFactor, frequencyPenalty, and presencePenalty to control the behavior and quality of the responses.
6. Enter a loop to receive input from the user and send it to the chat model.
7. Print the response from the model.

By following these steps, you will be able to implement the non-streaming chat option in your code.

Exploring the options for chat completions

The ChatCompletionOptions object allows you to set various options that control the behavior of the chat model. Let's look at each of these options in Detail:

1. messages: This option takes a list of messages and determines the Context of the conversation. At least one system message is required, and it should have the role "system". You can add user messages as well to Continue the conversation.
2. maxTokens: This option helps determine the length of the inputs and outputs. It specifies the maximum number of tokens allowed in the response.
3. temperature: The temperature parameter introduces randomness into the responses. A value between 0 and 1.0 can be used, where 0 represents the least random and 1.0 represents the most random. Higher values increase the creativity of the answers.
4. nucleusSamplingFactor: This parameter affects the creativity of the responses. A value between 0 and 1 represents the percentage of likelihood above which tokens are considered. Lower values restrict the creativity, while higher values allow all tokens to be considered.
5. frequencyPenalty: This option controls the use of repetitive words in the responses. A value between 0 and 1 determines the significance of the penalty. Higher values result in the model avoiding frequently used words, while lower values allow the use of any word.
6. presencePenalty: This option controls the repetition of words in the responses. Like the frequencyPenalty, it takes a value between 0 and 1. Higher values penalize the model for repeating words, while lower values allow repetition.

By adjusting these options, you can fine-tune the behavior of the chat model according to your requirements.

Understanding the parameters for model functions

The parameters explained in the previous section help control the behavior of the chat model. Let's Recap and understand the purposes of each parameter:

1. temperature: The temperature parameter introduces randomness into the responses. Higher values (close to 1.0) make the responses more random and creative, while lower values (close to 0) make the responses more focused and deterministic.

   Pros: Higher temperature values can make the conversation more engaging and unpredictable.

   Cons: Higher temperature values can result in irrelevant or nonsensical responses.

2. nucleusSamplingFactor: The nucleus sampling factor determines the likelihood threshold for token selection. Higher values (close to 1.0) include more tokens, resulting in more random and creative responses. Lower values (close to 0) restrict the token selection to a smaller subset, resulting in more focused and deterministic responses.

   Pros: Higher nucleus sampling factor values allow for more creative and diverse responses.

   Cons: Higher nucleus sampling factor values can increase the chances of generating nonsensical or irrelevant responses.

3. frequencyPenalty: The frequency penalty controls the use of repetitive words in the responses. Higher values penalize the model for using frequently occurring words, encouraging the use of less common alternatives. Lower values allow the model to use any word without restriction.

   Pros: Higher frequency penalty values can help generate more varied and diverse responses.

   Cons: Higher frequency penalty values can result in the model using uncommon or less precise alternatives.

4. presencePenalty: The presence penalty controls the repetition of words in the responses. Higher values penalize the model for repeating words, encouraging the generation of responses with minimal word repetition. Lower values allow for more repetitions in the responses.

   Pros: Higher presence penalty values can help generate responses without repetitive phrases or sentences.

   Cons: Higher presence penalty values can limit the model's ability to use precise and accurate repetitions when necessary.

By adjusting these parameters, you can customize the behavior of the chat model to achieve the desired level of creativity, relevance, and word repetition.

Using temperature for response randomness

The temperature parameter plays a crucial role in controlling the randomness of responses from the chat model. By setting a suitable value for the temperature parameter, you can achieve the desired level of response randomness.

A temperature value closer to 1.0 introduces higher randomness into the responses. With higher values, the model is more likely to generate unexpected and creative responses. However, a temperature value closer to 0 makes the responses more focused and predictable.

For example, consider setting a temperature value of 0.8. In this case, the model might produce responses that are slightly different from the expected answers, introducing an element of unpredictability and creativity.

On the other HAND, setting a temperature value of 0.2 would result in responses that are more deterministic and closely aligned with the expected answers. This can be useful in cases where precision and accuracy are paramount.

By adjusting the temperature parameter, you can strike a balance between creativity and precision, depending on the specific requirements of your application.

Using nucleus sampling factor for creativity control

The nucleusSamplingFactor parameter provides additional control over the creativity of the chat model's responses. With this parameter, you can influence the likelihood threshold for token selection.

A nucleusSamplingFactor value closer to 1.0 allows a larger proportion of likely tokens to be considered for selection. This provides more opportunities for creative and diverse responses from the chat model.

For example, consider setting a nucleusSamplingFactor value of 0.5. In this case, the chat model will only consider tokens that are among the top 50% of likelihood for the next token. This restricts the token selection to a smaller subset and can result in more focused and deterministic responses.

On the other hand, a nucleusSamplingFactor value of 1.0 allows all tokens to be considered for selection. This provides the highest level of creative freedom for the chat model.

It's important to note that using both the temperature and nucleusSamplingFactor parameters simultaneously is not recommended by OpenAI. While they both contribute to the creativity of the responses, using them together can lead to unpredictable and potentially nonsensical outputs.

By adjusting the nucleusSamplingFactor parameter, you can fine-tune the creative output of the chat model Based on the preferences and requirements of your application.

Using frequency penalty and presence penalty for word repetition control

The frequencyPenalty and presencePenalty parameters offer control over word repetition in the chat model's responses. By adjusting these parameters, you can influence the model's behavior when it comes to repetitive language.

The frequencyPenalty parameter determines the weighting given to frequently used words. A higher frequencyPenalty value penalizes the model for selecting frequently used words. This encourages the use of less common alternatives and helps generate responses with more varied vocabulary. On the other hand, a lower frequencyPenalty value allows the model to use any word without restriction.

For example, consider setting a frequencyPenalty value of 0.8. In this case, if the model needs to describe a cat, it would not choose the word "cat" if it occurs frequently in its training data. Instead, it would opt for a less common alternative like "feline." This promotes the use of more diverse and specialized language.

Conversely, setting a lower frequencyPenalty value would allow the model to use the word "cat" even if it occurs frequently in its training data. This can be advantageous when you want the model to use more common and straightforward language.

The presencePenalty parameter, on the other hand, controls word repetition within a single response. A higher presencePenalty value penalizes the model for repeating words, encouraging it to generate responses with minimal repetition. A lower presencePenalty value allows for more repetition in the responses.

For example, consider setting a presencePenalty value of 0.9. In this case, the model would be penalized if it repeats words in its response. This can lead to more concise and varied responses as the model would strive to avoid unnecessary repetition.

Conversely, setting a lower presencePenalty value would allow the model to repeat words more freely in its responses. This can be beneficial in cases where repetition plays a role in making the response clear and accurate.

By adjusting the frequencyPenalty and presencePenalty parameters, you can control the degree of word repetition in the chat model's responses, balancing between variety and repetition according to your application's needs.

Implementing the non-streaming chat loop

To facilitate the conversation flow, we will implement a chat loop in our non-streaming chat option. This loop allows us to continuously receive input from the user, send it to the chat model, receive responses, and print them on the console.

Let's go through the steps involved in implementing the non-streaming chat loop:

1. Print a prompt to the console to indicate that the chat client is ready for input.
2. Read text input from the user and store it in a variable.
3. Check if the user wants to quit by typing "quit" (case-insensitive). If so, break out of the loop to end the conversation.
4. Add the user's message to the messages option in the ChatCompletionOptions. Use the chatRole.user value to indicate that this message is from the user.
5. Send the user's message to the Open AI API using the GetChatCompletionsAsync method on the client object. Pass in the options object and the model deployment name.
6. Retrieve the response as a ChatCompletions object.
7. Extract the content of the response and print it out.
8. Add the chat message to the messages option in the ChatCompletionOptions to retain the conversation context for the next request.
9. Repeat the loop to continue the conversation.

By implementing this chat loop, you can create an interactive chat experience where the user's input is sent to the chat model, and the response is displayed in real-time.

Implementing the streaming chat option

In addition to the non-streaming chat option, Azure Open AI also offers a streaming chat option. This allows for a more interactive and dynamic conversation flow.

To implement the streaming chat option, we will make a few modifications to our code:

1. Uncomment the streaming client method and comment out the non-streaming client method.
2. In the streaming client method, the steps for setting up the OpenAIClient and ChatCompletionOptions remain the same.
3. After retrieving the response as a ChatCompletions object, we need to handle streaming responses in a loop.
4. Iterate through each chat choice in the response. Within this loop, append the content of each chat message to a STRING variable.
5. Print the response string after the loop completes.
6. Add the response message to the messages option in the ChatCompletionOptions.
7. Continue the loop to receive and process further input from the user.

By implementing the streaming chat option, the responses will chunk in as they are received, providing a more interactive and real-time conversation experience.

Conclusion

In this tutorial, we have learned how to code a simple chat client using C# and Azure Open AI. We covered the steps involved in setting up the Azure Open AI deployment, retrieving the necessary configuration options, and implementing both non-streaming and streaming chat options in our code. By following this tutorial, you should now have the basic knowledge required to leverage Azure Open AI to build chat clients with natural language processing capabilities.

Highlights

• Setting up the Azure Open AI deployment
• Retrieving configuration options for the code
• Implementing the non-streaming chat option
• Exploring the options for chat completions
• Understanding the parameters for model functions
• Using temperature for response randomness
• Using nucleus sampling factor for creativity control
• Using frequency penalty and presence penalty for word repetition control
• Implementing the non-streaming chat loop
• Implementing the streaming chat option

FAQ:

Q: What is Azure Open AI? A: Azure Open AI is a platform that provides access to state-of-the-art AI models, including natural language processing models. It allows developers to integrate AI capabilities into their applications.

Q: How can I retrieve the configuration options for my Azure Open AI deployment? A: To retrieve the configuration options, you need to access the Azure Open AI Playground and switch to the C# view. There, you will find the necessary URI, model name, and API key for your deployment.

Q: What are the advantages of using the non-streaming chat option? A: The non-streaming chat option allows for a simpler implementation, where the entire response is received at once. It is suitable for applications where real-time conversation flow is not a priority.

Q: How does the streaming chat option differ from the non-streaming option? A: The streaming chat option provides a more interactive and dynamic conversation experience. Responses are chunked in as they are received, allowing for real-time conversation flow.

Q: Can I adjust the parameters to control the creativity and relevance of the responses? A: Yes, the temperature and nucleusSamplingFactor parameters can be adjusted to control the creativity of the responses. The frequencyPenalty and presencePenalty parameters help control word repetition for more varied and focused responses.

Q: Can I use Azure Open AI for other types of AI models? A: Yes, Azure Open AI provides access to various AI models, including natural language processing models. It can be used for tasks such as language translation, sentiment analysis, and text summarization.

Q: Can I use Azure Open AI in other programming languages? A: Yes, Azure Open AI provides SDKs and libraries for multiple programming languages, including C#, Python, Java, and JavaScript. You can choose the language that best suits your application's requirements.