# Wikipedia Page Dataset Scraper (`scrapeai/wikipedia-page-dataset-scraper`) Actor Scrape Wikipedia articles and export structured dataset fields for training, knowledge bases, and research. - **URL**: https://apify.com/scrapeai/wikipedia-page-dataset-scraper.md - **Developed by:** [ScrapeAI](https://apify.com/scrapeai) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $20.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Wikipedia Page Dataset Scraper 📚 Scrape Wikipedia pages and extract structured article content including title, summary, full text, headings, infobox data, categories, references, and internal links. Designed for AI training, RAG pipelines, knowledge base creation, research datasets, and content analysis. ### Features - Scrapes article content from Wikipedia pages. - Extracts structured fields: `page_title`, `page_url`, `summary`, `full_text`, `headings`, `infobox`, `categories`, `references`, `internal_links`, `last_updated`, and `scraped_at`. - Supports multiple start URLs and optional crawling of linked Wikipedia articles. - Uses Playwright and Apify actor conventions for reliable dataset export. ### Getting Started 1. **Install dependencies** ```bash npm install ```` 2. **Configure input** - Edit `INPUT.json` or provide actor input through the Apify platform. - Example `INPUT.json`: ```json { "startUrls": [ { "url": "https://en.wikipedia.org/wiki/Artificial_intelligence" } ], "maxPages": 50, "followLinks": true } ``` 3. **Run locally** ```bash npm start ``` 4. **Docker / Actor** - The `Dockerfile` can build the image. - `.actor/actor.json` defines the Apify actor configuration. ### Output Fields - `page_title` - `page_url` - `summary` - `full_text` - `headings` - `infobox` - `categories` - `references` - `internal_links` - `last_updated` - `scraped_at` ### File Overview - `src/main.js` – actor entry point that loads input, launches Playwright, and executes the Wikipedia scraper. - `src/scraper.js` – page extraction and crawl logic for Wikipedia articles. - `.actor/input_schema.json` – defines supported actor input fields. - `.actor/dataset_schema.json` – defines the dataset output record fields. - `.actor/actor.json` – actor metadata and Apify configuration. ### Logs & Storage - Logs are written to Apify storage during actor execution. - Scraped dataset records are stored in Apify dataset storage. ### License This project is provided as-is. Feel free to adapt and extend it for your own Wikipedia scraping needs. # Actor input Schema ## `startUrls` (type: `array`): List of Wikipedia page URLs to crawl. ## `startUrl` (type: `string`): Single Wikipedia page URL to start scraping. ## `maxPages` (type: `integer`): Maximum number of Wikipedia pages to crawl. ## `followLinks` (type: `boolean`): Crawl internal Wikipedia links found on each scraped page. ## `headless` (type: `boolean`): Run browser in headless mode. ## Actor input object example ```json { "startUrls": [ { "url": "https://en.wikipedia.org/wiki/Artificial_intelligence" } ], "startUrl": "https://en.wikipedia.org/wiki/Artificial_intelligence", "maxPages": 50, "followLinks": true, "headless": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ { "url": "https://en.wikipedia.org/wiki/Artificial_intelligence" } ], "startUrl": "https://en.wikipedia.org/wiki/Artificial_intelligence" }; // Run the Actor and wait for it to finish const run = await client.actor("scrapeai/wikipedia-page-dataset-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "startUrls": [{ "url": "https://en.wikipedia.org/wiki/Artificial_intelligence" }], "startUrl": "https://en.wikipedia.org/wiki/Artificial_intelligence", } # Run the Actor and wait for it to finish run = client.actor("scrapeai/wikipedia-page-dataset-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://en.wikipedia.org/wiki/Artificial_intelligence" } ], "startUrl": "https://en.wikipedia.org/wiki/Artificial_intelligence" }' | apify call scrapeai/wikipedia-page-dataset-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapeai/wikipedia-page-dataset-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikipedia Page Dataset Scraper", "description": "Scrape Wikipedia articles and export structured dataset fields for training, knowledge bases, and research.", "version": "1.0", "x-build-id": "VxdPjn1gsRH8gA1y0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapeai~wikipedia-page-dataset-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapeai-wikipedia-page-dataset-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapeai~wikipedia-page-dataset-scraper/runs": { "post": { "operationId": "runs-sync-scrapeai-wikipedia-page-dataset-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapeai~wikipedia-page-dataset-scraper/run-sync": { "post": { "operationId": "run-sync-scrapeai-wikipedia-page-dataset-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "List of Wikipedia page URLs to crawl.", "items": { "type": "object", "properties": { "url": { "title": "URL", "type": "string", "description": "Wikipedia page URL", "editor": "textfield" } }, "required": [ "url" ], "additionalProperties": false }, "default": [ { "url": "https://en.wikipedia.org/wiki/Artificial_intelligence" } ] }, "startUrl": { "title": "Start URL", "type": "string", "description": "Single Wikipedia page URL to start scraping." }, "maxPages": { "title": "Max Pages", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of Wikipedia pages to crawl.", "default": 50 }, "followLinks": { "title": "Follow Internal Links", "type": "boolean", "description": "Crawl internal Wikipedia links found on each scraped page.", "default": true }, "headless": { "title": "Headless Browser", "type": "boolean", "description": "Run browser in headless mode.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```