用GPT将非结构化文档转化为标准化格式：建立一个简历解析器

Among its numerous applications, GPT has become a game-changer in the processing and standardization of unstructured documents.

在其众多的应用中，GPT已经成为非结构化文件处理和标准化方面的一个改变者。

In this blog post, we'll explore how you can convert unstructured documents, specifically resumes, into a standardized format using GPT.

在这篇博文中，我们将探讨如何使用GPT将非结构化的文件，特别是简历，转换为标准化的格式。

Resumes come in various shapes and sizes, with no two being exactly alike. This presents a unique challenge for recruiters who need to sift through hundreds or even thousands of resumes to identify suitable candidates.

简历有各种形状和大小，没有两个完全相同的。这给招聘人员带来了独特的挑战，他们需要从数百甚至数千份简历中筛选出合适的候选人。

As you can see, a quick Google search returns resumes in various designs and formats.

正如你所看到的，在谷歌上快速搜索就能得到各种设计和格式的简历。

This is a well-structured resume but extracting the text from the PDF file would result in unstructured text, losing the original formatting.

这是一份结构良好的简历，但从PDF文件中提取文本将导致非结构化的文本，失去原有的格式。

The resume above uses a month/year format for dates but other resumes may use different date formats such as date/month/year or only the year. These variations make the task of parsing resumes challenging as it is difficult to account for all possible cases.

上面的简历使用了月/年的日期格式，但其他简历可能使用不同的日期格式，如日期/月/年或只使用年份。这些变化使得解析简历的任务具有挑战性，因为它很难考虑到所有可能的情况。

def extract_text_from_binary(file): pdf_data = io.BytesIO(file) reader = PyPDF2.PdfReader(pdf_data) num_pages = len(reader.pages) text = "" for page in range(num_pages): current_page = reader.pages[page] text += current_page.extract_text() return text

First, we need to extract the text from PDF. We can use the PyPDF2 library for this.

首先，我们需要从PDF中提取文本。我们可以使用PyPDF2库来做这件事。

To call the OpenAI API, we use LangChain. LangChain is a community-driven framework for developing Language Model powered applications. It streamlines the development process by taking care of tedious tasks under the hood.

为了调用OpenAI的API，我们使用LangChain。LangChain是一个社区驱动的框架，用于开发语言模型驱动的应用程序。它通过处理引擎盖下繁琐的任务来简化开发过程。

from langchain.llms import OpenAIChat
from langchain.chains import ConversationChain
from...