# Universal Knowledge Base Scraper (RAG Ready) (`actums/universal-rag-scraper`) Actor Turn any Help Center into LLM-ready Markdown. Supports Zendesk, Intercom, Docusaurus, and generic sites. Perfect for RAG and AI Agents. - **URL**: https://apify.com/actums/universal-rag-scraper.md - **Developed by:** [Actums](https://apify.com/actums) (community) - **Categories:** Developer tools, Automation, AI - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $49.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ## 🧠 Universal Knowledge Base Scraper (RAG Ready) **Feed your AI Agents with clean, structured Markdown. Stop feeding them HTML garbage.** --- ### 🚀 What is Universal RAG Scraper? **Universal RAG Scraper** is an "ETL-in-a-Box" for AI Developers. It turns messy Help Centers (Zendesk, Intercom, Docusaurus, Notion) into pure, train-ready Markdown (`.md`) files. If you are building **RAG Pipelines (Retrieval-Augmented Generation)** or **AI Agents**, you know that HTML noise (navbars, footers, cookie banners) ruins your vector embeddings. This Actor solves that problem instantly. #### Why not just use a generic scraper? Generic scrapers give you the *page*. We give you the *content*. * **Auto-Detect:** We identify the platform (e.g., Zendesk) and apply surgical clean-up rules. * **Markdown Native:** We don't just "strip tags"; we convert tables, lists, and code blocks into perfect Markdown. * **Metadata Rich:** We extract the Title, URL, and Last Updated Date for your Vector DB. --- ### ⚡ Enterprise-Grade Features **Built for scale and reliability:** 1. **🛡️ Zero-Config Proxies:** Scrape protected Help Centers without getting 403 Blocked. Request rotation is built-in. 2. **⏰ Auto-Sync Scheduling:** Set it to run every Friday night. Keep your RAG Knowledge Base in sync with your product docs automatically. 3. **💾 Infinite Storage:** Scrape 10,000 pages or 10 million. All data is stored, indexed, and ready for export (JSON, CSV, Excel). 4. **🔌 Native Integrations:** Pipe the Markdown directly to **Pinecone**, **LangChain**, or **Zapier**. No glue code needed. --- ### 🎯 Supported Platforms (Auto-Detected) | Platform | Capability | | :--- | :--- | | **Zendesk** | Full support. Strips "Related Articles" & sidebars. | | **Intercom** | Full support. Handles dynamic loading. | | **Docusaurus** | Perfect for V2/V3 docs. Preserves code block languages. | | **Notion** | Scrapes public Notion Knowledge Bases. | | **Generic** | **Smart Fallback:** If we don't recognize the platform, we use advanced readability algorithms to extract the main content. | --- ### 📚 How to scrape a Knowledge Base in 3 steps 1. **Paste the URL:** Go to the input tab and enter the URL of the Help Center home page (e.g., `https://support.zoom.us/hc/en-us`). 2. **Set Depth:** Choose how many links to follow (default: 2 levels deep). 3. **Run:** Click "Start". In minutes, you can download a JSON file containing all articles in Markdown. --- ### 💰 Pricing & Usage **This is a Rental Actor.** * **Free Trial:** You can test the scraper for a limited time to verify the Markdown quality. * **Rental Plan:** Access unlimited scale, high-frequency scheduling, and priority support. **Cost Estimation:** * Scraping a typical Help Center (500 pages) takes ~5-10 minutes. * The output is "Vector Ready" - no post-processing costs. --- ### 📤 Input & Output #### Input Configuration Simple, developer-friendly input: ```json { "startUrls": [ { "url": "https://docs.apify.com" } ], "maxDepth": 10, "outputFormat": "markdown" } ```` #### Output (JSON/Dataset) Each item in the dataset is one article: ```json { "url": "https://docs.apify.com/academy/web-scraping", "title": "Web Scraping Academy", "platform": "Docusaurus", "scrapedAt": "2023-10-27T10:00:00Z", "markdown": "# Web Scraping Academy\n\nLearn how to scrape..." } ``` *** ### ❓ FAQ #### Can I scrape a custom-built Help Center? **Yes.** The Actor uses a "Smart Fallback" (Readability algorithm). If it doesn't detect Zendesk/Intercom, it will still scan the page, identify the visual "main content" area, and extract it. #### Does this handle dynamic Javascript sites? **Yes.** We use **Playwright** (headless browser) under the hood. We render the full page, execute JavaScript, and then scrape. This works even on React/Vue/Angular apps. #### How do I feed this into my LLM? 1. Run the Actor. 2. Download the `JSON` output. 3. Use the `markdown` field as the `content` in your LLM Prompt or Embedding request. *** ### 📞 Support & Feedback Found a site we can't scrape? Missing a platform? - **Report a Bug:** Use the "Issues" tab. - **Request a Feature:** We add new Platforms (e.g., Gitbook, ReadTheDocs) based on user votes! # Actor input Schema ## `startUrls` (type: `array`): List of Help Center URLs to scrape (e.g., https://support.zoom.us, https://help.intercom.com). ## `maxDepth` (type: `integer`): Maximum depth of crawling. ## `maxPages` (type: `integer`): Maximum number of pages to scrape. ## `outputFormat` (type: `string`): Format of the output data. ## `cleanNoise` (type: `boolean`): Remove navigation, footers, and ads from the output. ## `proxyConfiguration` (type: `object`): Select proxies to be used by your crawler. ## Actor input object example ```json { "startUrls": [ { "url": "https://docs.apify.com" } ], "maxDepth": 3, "maxPages": 100, "outputFormat": "markdown", "cleanNoise": true, "proxyConfiguration": { "useApifyProxy": 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://docs.apify.com" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("actums/universal-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 = { "startUrls": [{ "url": "https://docs.apify.com" }] } # Run the Actor and wait for it to finish run = client.actor("actums/universal-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 '{ "startUrls": [ { "url": "https://docs.apify.com" } ] }' | apify call actums/universal-rag-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=actums/universal-rag-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Universal Knowledge Base Scraper (RAG Ready)", "description": "Turn any Help Center into LLM-ready Markdown. Supports Zendesk, Intercom, Docusaurus, and generic sites. Perfect for RAG and AI Agents.", "version": "0.0", "x-build-id": "x5QCTw1zGMZhvg5ig" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/actums~universal-rag-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-actums-universal-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/actums~universal-rag-scraper/runs": { "post": { "operationId": "runs-sync-actums-universal-rag-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/actums~universal-rag-scraper/run-sync": { "post": { "operationId": "run-sync-actums-universal-rag-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "startUrls" ], "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "List of Help Center URLs to scrape (e.g., https://support.zoom.us, https://help.intercom.com).", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxDepth": { "title": "Max Depth", "minimum": 0, "type": "integer", "description": "Maximum depth of crawling.", "default": 3 }, "maxPages": { "title": "Max Pages", "type": "integer", "description": "Maximum number of pages to scrape.", "default": 100 }, "outputFormat": { "title": "Output Format", "enum": [ "markdown", "json" ], "type": "string", "description": "Format of the output data.", "default": "markdown" }, "cleanNoise": { "title": "Clean Noise", "type": "boolean", "description": "Remove navigation, footers, and ads from the output.", "default": true }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Select proxies to be used by your crawler.", "default": { "useApifyProxy": 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 } } } } } } } } } } ```