Supercharging Python with ChatGPT API

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Supercharging Python with ChatGPT API

Table of Contents:

  1. Introduction
  2. Creating an Account and Obtaining API Key
  3. Interacting with Chat GPT API in Python
  4. Sending Requests and Receiving Replies from Chat GPT
  5. Creating a Method for Chat GPT Interaction
  6. Defining the Prompt and Commentary
  7. Testing the Chat GPT Method
  8. Bringing Python Code into Power Query
  9. Generating Results in Power Query
  10. Conclusion

Introduction

Greetings and salutations! In this tutorial, I, James, your ba Sensei, will guide you through the process of interacting with Chat GPT using Power Query in Excel. While the primary focus is on Power Query, I will also Show you how to make use of the Chat GPT API in Python. Our mission is to send a request to Chat GPT in Power Query, receive a reply, and bring it back into the Excel spreadsheet. This tutorial will be divided into two parts: the first part will demonstrate how to interact with the Chat GPT API in Python, while the Second part will showcase how to integrate it into Power Query. So, let's dive in and see how it all works!

Creating an Account and Obtaining API Key

Before we begin, You need to Create an account on the Chat GPT Website. Once you have created an account, navigate to the API keys section and generate a new secret key. This secret key is essential to Interact with the Chat GPT API. Copy your secret key as it will be used in the subsequent steps. To ensure the security of your key, I recommend loading it as an environment variable. This way, you can protect your secret code from unwanted access.

Interacting with Chat GPT API in Python

To interact with the Chat GPT API in Python, we need to install the "OpenAI" library. Use the command "pip install openai" to install it. Additionally, import the "os" library to perform certain operating system functions. Once installed, import the "openai" library in your Python script.

Sending Requests and Receiving Replies from Chat GPT

To send a request and receive a reply from Chat GPT, we can make use of the "openai" library's "chat_completion" method. This method enables us to call the Chat GPT model using the GPT 3.5 turbo version. By specifying the desired message content using curly brackets, we can communicate with Chat GPT. After sending the request, we will receive a response containing the reply from Chat GPT. Extract the desired reply from the response by accessing the appropriate fields (choices -> first choice -> messages -> content).

Creating a Method for Chat GPT Interaction

To simplify the process of interacting with Chat GPT, let's create our own method. We will define a method called "ask_chat_GPT" that takes a message as input. Inside this method, we will integrate the API call to Chat GPT by replacing the message with the input parameter. Finally, we will return the reply from Chat GPT, allowing us to store it in a variable and use it later in Power Query.

Defining the Prompt and Commentary

To provide Context to Chat GPT, we will define a prompt role and the main commentary. The prompt role can be used to specify the user's role, while the main commentary represents the actual content we want to summarize. By concatenating these two elements, we can create a prompt that guides Chat GPT in generating a response.

Testing the Chat GPT Method

To test our Chat GPT method, let's call it with the prompt role and main commentary as inputs. This will allow us to see the reply generated by Chat GPT. You can further fine-tune the method and experiment with different inputs to explore the capabilities of Chat GPT.

Bringing Python Code into Power Query

Now that we have successfully created and tested our method in Python, it's time to integrate it into Power Query. By leveraging Power Query's capabilities, we can interface with the Python script and generate the desired results directly in Excel.

Generating Results in Power Query

Using Power Query, we can implement the Python code into our Excel environment. This will enable us to make requests to Chat GPT, receive replies, and display them in the Excel spreadsheet. By combining the power of Power Query and Chat GPT, we can streamline our data analysis processes and enhance our market commentary generation.

Conclusion

In conclusion, we have explored the process of interacting with Chat GPT through Power Query and Python. We have learned how to send requests, receive replies, and utilize the capabilities of Chat GPT in generating concise summaries. By integrating Chat GPT into our data analysis workflow, we can enhance our financial analysis and market commentary generation. So, go ahead and give it a try in your own projects to harness the power of Chat GPT!

Highlights:

  1. Learn how to interact with Chat GPT using Power Query and Python.
  2. Send requests and receive replies from Chat GPT API.
  3. Create a method for seamless interaction with Chat GPT.
  4. Define prompt and commentary for generating concise summaries.
  5. Incorporate Chat GPT into Power Query for streamlined data analysis.

FAQ:

Q: What is Chat GPT? A: Chat GPT is a language model developed by OpenAI that can be used to generate human-like text responses based on given prompts.

Q: How can I obtain an API key for Chat GPT? A: To obtain an API key for Chat GPT, you need to create an account on the Chat GPT website and generate a secret key in the API keys section.

Q: Can I integrate Chat GPT with Power Query? A: Yes, you can integrate Chat GPT with Power Query by leveraging its capabilities to interface with Python scripts.

Q: How can Chat GPT be useful in financial analysis? A: Chat GPT can assist financial analysts in generating concise summaries of market commentary, which can be further utilized for various purposes such as social media posts or quick updates.

Q: Is it possible to fine-tune the Prompts and commentary for better responses from Chat GPT? A: Yes, you can experiment with different prompts and commentary to fine-tune the responses generated by Chat GPT, thereby improving its effectiveness in generating summaries.

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