触发API：使用GPT构建智能助手

触发API：使用GPT构建智能助手

Table of Contents

1. Introduction
2. What is GPT and its limitations
3. Utilizing GPT to trigger APIs
4. Setting up an OpenAI account
5. Exploring and playing with Prompts using OpenAI's playground
6. Building a simple application with GPT
7. Writing code in Python
8. Communicating with the GPT API
9. Integrating with external APIs
10. Conclusion

Introduction

Over the past few weeks, the topic of GPT (Generative Pre-trained Transformer) has been driving the internet crazy. Particularly, Chad GPT has gained Attention with videos showcasing its ability to answer crazy questions and generate contextually Relevant and grammatically correct text. In this article, we will dive deeper into GPT and explore how we can take it to the next level by utilizing it to trigger APIs. This way, we can integrate GPT into various use cases, including building a complete Smart Assistant. Before we start, let's first understand what GPT is and its limitations.

What is GPT and its limitations

GPT, short for Generative Pre-trained Transformer, is a powerful large language model developed by OpenAI. It can generate contextually relevant and grammatically correct text, making it capable of performing a wide range of tasks such as question answering, summarization, translation, and text generation. However, one of the limitations of GPT is that it cannot Read from the internet or make API calls to access external data. This means that GPT solely relies on the data provided during its training and cannot access real-time information from the web or external sources. But with some prompt engineering, we can work around this limitation and use GPT to trigger APIs, which is exactly what we'll explore in the next section.

Utilizing GPT to trigger APIs

To harness the power of GPT in triggering APIs, we need to integrate it into our applications. A basic idea for a simple application would be to ask GPT a question or make a statement, and Based on that input, trigger a specific API. For example, imagine having an API to switch lights on and off. By saying "I want to turn the lights on," GPT can understand this statement and map it to the corresponding API call to switch the lights on. While GPT itself cannot directly call APIs, we can use its text processing capabilities to map statements to specific commands and handle the API calls in our codebase.

Setting up an OpenAI account

Before we dive into building our application, we need to Create an OpenAI account. The account creation process is straightforward, and OpenAI offers 18 USD as test credit to explore and test their API. This generous amount allows us to play around with text generation without worrying about costs. Once we have our account created, we can move on to the next step.

Exploring and playing with prompts using OpenAI's playground

OpenAI provides us with a playground that serves as an excellent starting point for exploring and playing around with prompts. We can experiment with different statements and commands using the playground, which will be helpful when building our application. By giving a statement to Chat GPT and examining the generated output, we can fine-tune our prompts and ensure that GPT understands and maps statements correctly to commands. Through trial and error, we can refine the prompt engineering process.

Building a simple application with GPT

Now that we have familiarized ourselves with the GPT API and experimented with prompts in the playground, it's time to build a simple application utilizing GPT and API triggering. The basic idea is to take user input, ask GPT to match the input with commands, and based on the response, trigger the corresponding API call. We can write this application in Python, although the language choice isn't critical as we can easily convert the code into any other language using GPT itself. The future of coding will likely involve bridging language barriers, allowing programmers to focus on the building side while leveraging AI assistance to increase productivity.

Writing code in Python

To start building our application, we need to define several functions that will handle the Core operations. These functions include lights_on, lights_off, do_not_disturb, and bitcoin_price. The purpose of these functions will become clear as we progress in our code. With the help of GitHub COPILOT, an AI assistant based on GPT technology, we can receive autocomplete suggestions, saving us time and increasing productivity. GitHub Copilot significantly enhances the coding experience, boosting productivity by up to 50%. For a small monthly fee, it offers an incredible return on investment. Importing the necessary libraries, defining the main function, and creating an array of commands are crucial steps in building our application.

Communicating with the GPT API

To communicate with the GPT API, we need to import the OpenAI library in Python. This library simplifies the interaction with the GPT API. We also need to export our OpenAI API Key, which can be obtained by creating a new secret key on the OpenAI Website. Once we have our key, we can use the OpenAI Python library to send prompts to the GPT API and receive responses. The completion.choices object allows us to access the generated text from GPT and perform further actions based on the chosen command. Handling different responses and dealing with cases where no command matches the input are essential for a robust application.

Integrating with external APIs

Now, let's explore the integration of GPT with external APIs. In our case, we want to integrate GPT with a Bitcoin exchange API to retrieve the actual price of Bitcoin. When the user inputs the command "Bitcoin price," instead of simply printing the phrase, we will make an API call to fetch the real-time price of Bitcoin. This integration demonstrates the full cycle of GPT and external API interaction. With the help of the requests library and the JSON response from the Bitcoin exchange API, we can extract the price of Bitcoin and display it to the user.

Conclusion

In conclusion, GPT offers tremendous potential for various applications. By utilizing GPT to trigger APIs, we can integrate it into our own projects, such as building a smart assistant or automating tasks. The possibilities are endless when combining the power of GPT and external APIs. With some prompt engineering, we can make GPT understand and map statements to commands. By incorporating AI in our coding workflow, we can enhance productivity and focus on the more challenging aspects of software development, ultimately becoming architects who construct smaller systems and combine them to create larger software solutions.

Highlights

• GPT (Generative Pre-trained Transformer) has been a hot topic on the internet.
• GPT is a powerful language model developed by OpenAI with various applications.
• GPT has limitations, including the inability to access external data and make API calls.
• Prompt engineering can be used to overcome GPT's limitations and trigger APIs.
• OpenAI provides a playground for exploring and experimenting with prompts.
• Building a simple application with GPT involves asking user input and triggering specific API calls based on GPT's response.
• Python is a suitable language for implementing GPT-based applications.
• GitHub Copilot, an AI assistant based on GPT technology, can help with code autocompletion.
• The OpenAI API enables communication with GPT and retrieval of responses.
• Integration with external APIs expands the capabilities of GPT applications.
• Combining GPT with external APIs opens up possibilities for automation and enhanced user experiences.

FAQ

Q: Can GPT read from the internet or access external data sources? A: No, GPT has limitations in accessing external data sources or making API calls directly. It relies solely on the data provided during its training.

Q: How can GPT be used to trigger APIs? A: With prompt engineering, GPT can understand statements and map them to specific commands. Developers can then handle these commands in their code and make the necessary API calls.

Q: Can the code shown in the article be converted into other programming languages? A: Yes, the code can be easily converted into other programming languages. The future of coding involves bridging language barriers, and GPT-based AI assistants like GitHub Copilot can assist in the conversion process.

Q: What are the benefits of integrating GPT with external APIs? A: Integrating GPT with external APIs allows for real-time data retrieval and interaction with various services. This expands the capabilities of applications built with GPT, enabling automation and enhanced functionality.