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Retrieve Wikipedia article content, summaries, page views, links, categories, and search results programmatically. Commonly used in NLP pipelines for training data collection, knowledge graph construction, entity resolution, and enriching datasets with encyclopedic context using the wikipedia-api Python library.
The `wikipedia-api` Python library enables clean extraction of article text, sections, and metadata. Engineers also use the MediaWiki REST API directly with `requests` for batch processing, storing content as Parquet for large-scale text analysis.
Wikipedia is foundational for RAG systems — its structured, factual content makes it ideal for grounding LLM responses. You can build a Wikipedia-backed QA assistant where each query retrieves relevant article sections as context, dramatically reducing hallucinations for factual questions.
# pip install wikipedia-api
import wikipediaapi
wiki = wikipediaapi.Wikipedia("my-app/1.0 (myemail@example.com)", "en")
page = wiki.page("Apache_Airflow")
print(page.summary[:500])Official dataset source
More datasets used by Python data engineers.
Access content and metadata from all Wikimedia projects including Wikipedia, Wiktionary, Wikiquote, and Commons. Used in data pipelines for multilingual text corpus construction, knowledge graph enrichment, page view analytics, and building NLP training datasets from structured encyclopaedic content in Python.
Provides structured data about Breaking Bad characters, episodes, quotes, and deaths. A clean, well-documented REST API commonly used to practise JSON ingestion, relational data modelling, and building small ETL pipelines in Python before working with larger production data sources.
A lightweight REST API that returns random facts and trivia about cats. Useful for learning API integration, testing HTTP client libraries in Python, and building practice ETL pipelines before connecting to more complex data sources.