# Wikipedia Pro Scraper - Sections, Infobox, References (`wetyr_corporation/wikipedia-pro-scraper`) Actor Wikipedia scraper for AI/RAG. Extracts structured sections, infobox key-value data, references. Multilingual, batch-friendly. Ready for vector databases. - **URL**: https://apify.com/wetyr\_corporation/wikipedia-pro-scraper.md - **Developed by:** [WETYR CORPORATION](https://apify.com/wetyr_corporation) (community) - **Categories:** AI, Education, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 Pro Scraper The Wikipedia scraper built for AI/RAG pipelines. Extracts: - **Structured sections** (Introduction, History, etc) ready for chunking - **Infobox key-value data** (birthdate, country, founder, etc) — perfect for fact extraction - **References** with full citation text - **Internal wikilinks** for knowledge graph building - **Image URLs** for multimodal datasets - **Categories + langlinks** for cross-language enrichment Multilingual (300+ Wikipedia editions). Properly attributed under CC BY-SA. ### Why Pro vs other Wikipedia scrapers Most Wikipedia scrapers on Apify return raw HTML or stripped plaintext. Ours gives you: - Sectioned output for vector DB chunking - Parsed infobox (no manual table parsing) - Clean text with citation markers removed - Wikilinks graph for knowledge graphs ### Pricing - $0.05 per actor start - $0.01 per article scraped - $0.003 per infobox parsed Typical run: 1,000 articles with infoboxes = ~$13.05. # Actor input Schema ## `titles` (type: `array`): Wikipedia article titles (e.g. 'Artificial intelligence') or full URLs. ## `language` (type: `string`): Two-letter language code (en, es, fr, de, etc). ## `includeInfobox` (type: `boolean`): Parse the structured infobox key-value data (birthdate, country, founder, etc). ## `includeReferences` (type: `boolean`): Pull the full list of cited references at the bottom. ## `includeSections` (type: `boolean`): Return content organized by section headings (Introduction, History, etc). ## `includeImages` (type: `boolean`): List image URLs from the article. ## `includeWikilinks` (type: `boolean`): List linked Wikipedia articles for graph building. ## Actor input object example ```json { "titles": [ "Artificial intelligence", "Large language model" ], "language": "en", "includeInfobox": true, "includeReferences": true, "includeSections": true, "includeImages": false, "includeWikilinks": false } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "titles": [ "Artificial intelligence", "Large language model" ] }; // Run the Actor and wait for it to finish const run = await client.actor("wetyr_corporation/wikipedia-pro-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "titles": [ "Artificial intelligence", "Large language model", ] } # Run the Actor and wait for it to finish run = client.actor("wetyr_corporation/wikipedia-pro-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 '{ "titles": [ "Artificial intelligence", "Large language model" ] }' | apify call wetyr_corporation/wikipedia-pro-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=wetyr_corporation/wikipedia-pro-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikipedia Pro Scraper - Sections, Infobox, References", "description": "Wikipedia scraper for AI/RAG. Extracts structured sections, infobox key-value data, references. Multilingual, batch-friendly. Ready for vector databases.", "version": "0.1", "x-build-id": "dONNPDVtuINu8izgm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/wetyr_corporation~wikipedia-pro-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-wetyr_corporation-wikipedia-pro-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/wetyr_corporation~wikipedia-pro-scraper/runs": { "post": { "operationId": "runs-sync-wetyr_corporation-wikipedia-pro-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/wetyr_corporation~wikipedia-pro-scraper/run-sync": { "post": { "operationId": "run-sync-wetyr_corporation-wikipedia-pro-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "titles" ], "properties": { "titles": { "title": "Article Titles or URLs", "type": "array", "description": "Wikipedia article titles (e.g. 'Artificial intelligence') or full URLs.", "items": { "type": "string" } }, "language": { "title": "Wikipedia Language", "type": "string", "description": "Two-letter language code (en, es, fr, de, etc).", "default": "en" }, "includeInfobox": { "title": "Extract Infobox", "type": "boolean", "description": "Parse the structured infobox key-value data (birthdate, country, founder, etc).", "default": true }, "includeReferences": { "title": "Extract References", "type": "boolean", "description": "Pull the full list of cited references at the bottom.", "default": true }, "includeSections": { "title": "Split into Sections", "type": "boolean", "description": "Return content organized by section headings (Introduction, History, etc).", "default": true }, "includeImages": { "title": "Include Image URLs", "type": "boolean", "description": "List image URLs from the article.", "default": false }, "includeWikilinks": { "title": "Extract Internal Links", "type": "boolean", "description": "List linked Wikipedia articles for graph building.", "default": false } } }, "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 } } } } } } } } } } ```