Build Wikipedia command line tools using Python and Wikipedia API
Creating a Wikipedia command line interface (CLI) tool was a very fulfilling project that combined the simplicity of Python with the vast knowledge base of Wikipedia. This tutorial will walk you step-by-step through building a CLI tool that gets information from Wikipedia using its API.
Prerequisites
Before you begin, make sure you have the following:
- Python 3.7 or higher installed on your system.
- Basic knowledge of Python and experience using APIs.
- Internet connection for accessing the Wikipedia API.
Step 1: Understand the Wikipedia API
Wikipedia provides a RESTful API at https://www.php.cn/link/27bf6226213cf288dfbf62ffc02bad4f. This API allows developers to query Wikipedia for content, metadata, and more. Key endpoints we will use include:
-
action=query
: Get general content from Wikipedia. -
list=search
: Search articles by keyword. -
prop=extracts
: Retrieve article abstracts.
The base URL for all API requests is:
<code>https://www.php.cn/link/27bf6226213cf288dfbf62ffc02bad4f</code>
Step 2: Set up the Python environment
First create a Python virtual environment and install the required libraries. We will use requests
to make HTTP requests and argparse
to handle CLI parameters.
<code># 创建虚拟环境 python -m venv wikipedia-cli-env # 激活环境 # 在Windows上: wikipedia-cli-env\Scripts\activate # 在Mac/Linux上: source wikipedia-cli-env/bin/activate # 安装依赖项 pip install requests argparse</code>
Step 3: Plan CLI functions
Our CLI tool will include the following features:
- Search Wikipedia articles : Allows users to search for articles by keywords.
- Get article summary: Retrieve a short summary of a specific article.
- View CLI Help: Displays usage instructions.
Step 4: Implement CLI tool
The following is the Python code for the CLI tool:
import argparse import requests # 定义维基百科API的基本URL WIKIPEDIA_API_URL = "https://www.php.cn/link/27bf6226213cf288dfbf62ffc02bad4f" def search_articles(query): """搜索与查询匹配的维基百科文章。""" params = { 'action': 'query', 'list': 'search', 'srsearch': query, 'format': 'json', } response = requests.get(WIKIPEDIA_API_URL, params=params) response.raise_for_status() # 对错误的响应引发错误 data = response.json() if 'query' in data: return data['query']['search'] else: return [] def get_article_summary(title): """获取维基百科文章的摘要。""" params = { 'action': 'query', 'prop': 'extracts', 'exintro': True, 'titles': title, 'format': 'json', } response = requests.get(WIKIPEDIA_API_URL, params=params) response.raise_for_status() data = response.json() pages = data.get('query', {}).get('pages', {}) for page_id, page in pages.items(): if 'extract' in page: return page['extract'] return "No summary available." def main(): parser = argparse.ArgumentParser(description="一个与维基百科交互的CLI工具。") subparsers = parser.add_subparsers(dest="command") # 子命令:search search_parser = subparsers.add_parser("search", help="在维基百科上搜索文章。") search_parser.add_argument("query", help="搜索查询。") # 子命令:summary summary_parser = subparsers.add_parser("summary", help="获取特定维基百科文章的摘要。") summary_parser.add_argument("title", help="维基百科文章的标题。") args = parser.parse_args() if args.command == "search": results = search_articles(args.query) if results: print("搜索结果:") for result in results: print(f"- {result['title']}: {result['snippet']}") else: print("未找到结果。") elif args.command == "summary": summary = get_article_summary(args.title) print(summary) else: parser.print_help() if __name__ == "__main__": main()
Step 5: Test the CLI tool
Save the script as wikipedia_cli.py
. You can now run the tool from the terminal:
- Search articles:
<code>python wikipedia_cli.py search "Python programming"</code>
- Get article summary:
<code>python wikipedia_cli.py summary "Python (programming language)"</code>
Step 6: Enhance Tools
To make the tool more powerful and user-friendly, please consider adding the following:
- Error handling: Provide detailed error messages for failed API requests.
-
Formatting: Use libraries like
rich
to get prettier output. - Caching: Implement caching to avoid repeated API calls for the same query.
- Additional Features: Add support for getting related articles, categories or images.
Conclusion
You have successfully built a Wikipedia CLI tool using Python and its API! This tool can serve as a good starting point for more advanced projects, such as integrating it into other applications or creating a GUI version. Happy coding!
The above is the detailed content of How to Create a Wikipedia CLI. For more information, please follow other related articles on the PHP Chinese website!

Arraysaregenerallymorememory-efficientthanlistsforstoringnumericaldataduetotheirfixed-sizenatureanddirectmemoryaccess.1)Arraysstoreelementsinacontiguousblock,reducingoverheadfrompointersormetadata.2)Lists,oftenimplementedasdynamicarraysorlinkedstruct

ToconvertaPythonlisttoanarray,usethearraymodule:1)Importthearraymodule,2)Createalist,3)Usearray(typecode,list)toconvertit,specifyingthetypecodelike'i'forintegers.Thisconversionoptimizesmemoryusageforhomogeneousdata,enhancingperformanceinnumericalcomp

Python lists can store different types of data. The example list contains integers, strings, floating point numbers, booleans, nested lists, and dictionaries. List flexibility is valuable in data processing and prototyping, but it needs to be used with caution to ensure the readability and maintainability of the code.

Pythondoesnothavebuilt-inarrays;usethearraymoduleformemory-efficienthomogeneousdatastorage,whilelistsareversatileformixeddatatypes.Arraysareefficientforlargedatasetsofthesametype,whereaslistsofferflexibilityandareeasiertouseformixedorsmallerdatasets.

ThemostcommonlyusedmoduleforcreatingarraysinPythonisnumpy.1)Numpyprovidesefficienttoolsforarrayoperations,idealfornumericaldata.2)Arrayscanbecreatedusingnp.array()for1Dand2Dstructures.3)Numpyexcelsinelement-wiseoperationsandcomplexcalculationslikemea

ToappendelementstoaPythonlist,usetheappend()methodforsingleelements,extend()formultipleelements,andinsert()forspecificpositions.1)Useappend()foraddingoneelementattheend.2)Useextend()toaddmultipleelementsefficiently.3)Useinsert()toaddanelementataspeci

TocreateaPythonlist,usesquarebrackets[]andseparateitemswithcommas.1)Listsaredynamicandcanholdmixeddatatypes.2)Useappend(),remove(),andslicingformanipulation.3)Listcomprehensionsareefficientforcreatinglists.4)Becautiouswithlistreferences;usecopy()orsl

In the fields of finance, scientific research, medical care and AI, it is crucial to efficiently store and process numerical data. 1) In finance, using memory mapped files and NumPy libraries can significantly improve data processing speed. 2) In the field of scientific research, HDF5 files are optimized for data storage and retrieval. 3) In medical care, database optimization technologies such as indexing and partitioning improve data query performance. 4) In AI, data sharding and distributed training accelerate model training. System performance and scalability can be significantly improved by choosing the right tools and technologies and weighing trade-offs between storage and processing speeds.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment

Dreamweaver Mac version
Visual web development tools

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.
