How to build a Python code assistant
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How to build a Python code assistant 翻译站点

使用GPT-3生成Python代码

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通过利用世界上最先进的语言模型,从简单的自然语言中自动生成Python代码。

###先决条件
请以相同的顺序浏览以下文章,以连接点并了解Python Code Assistant背后的关键技术堆栈 - 由GPT-3提供支持的应用程序:
- fastapi-超越烧瓶的高潮!
- 简化 - 革新数据应用程序创建
- [gpt-3简介](https://pub.towardsai.net/email-assistant-powered-bby-gpt-3-ba39dfe999999999999999999999999999D3)

###应用程序演练
在创建任何GPT-3应用程序时,首先要考虑的是培训提示的设计和内容。及时设计是启动GPT-3模型以提供有利和上下文响应的最重要过程。

根据设计训练提示,您应该旨在从模型中获得零射击的响应,如果不可能以少数示例前进而不是为其提供整个语料库,则应旨在从模型中获得零射击响应。训练提示设计的标准流程应该看起来像:零射击→几张射击→基于语料库的启动。

为了设计Python代码助理应用程序的培训提示,我已将以下结构用于培训提示:
- 描述:关于代码助手应该做什么的上下文的初始描述,并添加了一两行的有关其功能。
- 自然语言(英语):此组件包括对代码助手将执行的任务的最小单线描述。它有助于-GPT -3了解上下文以生成适当的Python代码。
代码:此组件包括与GPT-3模型输入的英文描述相对应的Python代码。

作者:[Shubham Saboo](https://www.linkedin.com/in/shubhamsaboo)

完整的教程可在https://pub.towardsai.net/python-code-assistant-powered-by-gpt-3-dfecf1945925中找到

原文:

Auto-generate python code from simple natural language by leveraging the world’s most advanced language model.

### Pre-Requisites
Please go through the below articles in the same order to connect the dots and understand the key tech stack behind Python Code Assistant- an application powered by GPT-3:
- FastAPI — The Spiffy Way Beyond Flask!
- Streamlit — Revolutionizing Data App Creation
- [A Brief Introduction to GPT-3](https://pub.towardsai.net/email-assistant-powered-by-gpt-3-ba39dfe999d3)

### Application walkthrough
While creating any GPT-3 application the first and foremost thing to consider is the design and content of the training prompt. Prompt design is the most significant process in priming the GPT-3 model to give a favorable and contextual response.

As a rule of thumb while designing the training prompt you should aim towards getting a zero shot response from the model, if that isn’t possible move forward with few examples rather than providing it with an entire corpus. The standard flow for training prompt design should look like: Zero Shot → Few Shots →Corpus based Priming.

For designing the training prompt for the python code assistant application, I have used the following structure for the training prompt:
- Description: An initial description of the context about what the code assistant is supposed to do and adding a line or two about its functionality.
- Natural Language (English): This component includes a minimal one-liner description of the task that will be performed by the code assistant. It helps - GPT-3 to understand the context in order to generate proper python code.
Code: This component includes the python code corresponding to the English description provided as an input to the GPT-3 model.

Author: [Shubham Saboo](https://www.linkedin.com/in/shubhamsaboo)

Full tutorial available at https://pub.towardsai.net/python-code-assistant-powered-by-gpt-3-dfecf1945925

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