# Wikipedia to RAG — Article Scraper for AI Pipelines (`yuchiaoniu/wikipedia-rag-scraper`) Actor Search Wikipedia and download articles as clean Markdown chunks ready for RAG pipelines, Pinecone, Weaviate, Chroma, or any vector database. No API key required. - **URL**: https://apify.com/yuchiaoniu/wikipedia-rag-scraper.md - **Developed by:** [Niu Yuchiao](https://apify.com/yuchiaoniu) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 to RAG — Bulk Article Scraper for AI & Vector Databases Turn any Wikipedia topic into clean, chunked Markdown ready for RAG pipelines, vector databases, and LLM fine-tuning — **no API key required**. ### Why use this Actor? Wikipedia is the world's largest free knowledge base with 60+ million articles. This Actor automates: - 🔍 **Multi-query search** — find relevant articles by keyword - 📄 **Clean text extraction** — removes infoboxes, references, navbars - ✂️ **Smart chunking** — overlapping chunks optimized for RAG retrieval - 📦 **Vector DB ready** — outputs JSONL with `text` + `metadata` fields ### Use Cases - Building domain-specific RAG knowledge bases - Creating AI training datasets - Populating Pinecone / Weaviate / Chroma / Qdrant - Research automation and summarization pipelines ### Input ```json { "searchQueries": ["machine learning", "neural networks", "transformer model"], "articleUrls": ["https://en.wikipedia.org/wiki/BERT_(language_model)"], "language": "en", "maxArticles": 20, "chunkSize": 400, "chunkOverlap": 40, "includeIntroOnly": false, "outputMarkdown": true } ```` | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `searchQueries` | string\[] | `[]` | Keywords to search Wikipedia | | `articleUrls` | string\[] | `[]` | Direct Wikipedia article URLs | | `language` | string | `"en"` | Wikipedia language code (en, zh, de, fr, ja, ...) | | `maxArticles` | number | `20` | Maximum number of articles to process | | `chunkSize` | number | `400` | Words per chunk | | `chunkOverlap` | number | `40` | Overlapping words between chunks | | `includeIntroOnly` | boolean | `false` | Only extract the article introduction | | `outputMarkdown` | boolean | `true` | Save full markdown to Key-Value store | ### Output #### Dataset (JSONL chunks) Each row contains a text chunk ready for embedding: ```json { "text": "A transformer is a deep learning architecture...", "metadata": { "title": "Transformer (deep learning architecture)", "url": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "chunkIndex": 0, "totalChunks": 12, "language": "en", "source": "wikipedia", "scrapedAt": "2025-01-01T00:00:00.000Z" } } ``` #### Key-Value Store - `wiki-{title}.md` — full article as clean Markdown - `output.jsonl` — all chunks as downloadable JSONL file ### Integration Examples #### Pinecone ```python import json from pinecone import Pinecone import openai pc = Pinecone(api_key="YOUR_KEY") index = pc.Index("wikipedia-knowledge") ## Download output.jsonl from Actor run with open("output.jsonl") as f: for line in f: item = json.loads(line) embedding = openai.embeddings.create( input=item["text"], model="text-embedding-3-small" ).data[0].embedding index.upsert([(item["metadata"]["url"] + str(item["metadata"]["chunkIndex"]), embedding, item["metadata"])]) ``` #### LangChain ```python from langchain.vectorstores import Chroma from langchain.embeddings import OpenAIEmbeddings from langchain.schema import Document import json docs = [] with open("output.jsonl") as f: for line in f: item = json.loads(line) docs.append(Document(page_content=item["text"], metadata=item["metadata"])) vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings()) ``` ### Multilingual Support Set the `language` parameter to scrape any Wikipedia language edition: | Code | Language | |------|----------| | `en` | English | | `zh` | Chinese | | `de` | German | | `fr` | French | | `ja` | Japanese | | `es` | Spanish | | `ko` | Korean | ### FAQ **Is this legal?** Yes. Wikipedia content is published under the Creative Commons Attribution-ShareAlike license and explicitly allows programmatic access. The Actor respects rate limits with polite delays. **Does it require an API key?** No. The Wikipedia REST API is completely free and requires no authentication. **How many articles can I scrape?** The FREE Apify plan supports up to hundreds of articles per run. For bulk scraping of thousands of articles, consider upgrading. ## Actor input object example ```json {} ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("yuchiaoniu/wikipedia-rag-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 = {} # Run the Actor and wait for it to finish run = client.actor("yuchiaoniu/wikipedia-rag-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 '{}' | apify call yuchiaoniu/wikipedia-rag-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=yuchiaoniu/wikipedia-rag-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikipedia to RAG — Article Scraper for AI Pipelines", "description": "Search Wikipedia and download articles as clean Markdown chunks ready for RAG pipelines, Pinecone, Weaviate, Chroma, or any vector database. No API key required.", "version": "1.0", "x-build-id": "W0YeLzntTNeRFwOcz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/yuchiaoniu~wikipedia-rag-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-yuchiaoniu-wikipedia-rag-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/yuchiaoniu~wikipedia-rag-scraper/runs": { "post": { "operationId": "runs-sync-yuchiaoniu-wikipedia-rag-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/yuchiaoniu~wikipedia-rag-scraper/run-sync": { "post": { "operationId": "run-sync-yuchiaoniu-wikipedia-rag-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": {} }, "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 } } } } } } } } } } ```