# Website RAG Knowledge Builder (`glowing_glove/website-rag-knowledge-builder`) Actor Crawl public website pages and build clean RAG-ready knowledge records with page summaries, key facts, FAQs, links, and retrieval chunks. - **URL**: https://apify.com/glowing\_glove/website-rag-knowledge-builder.md - **Developed by:** [Ushba Khan](https://apify.com/glowing_glove) (community) - **Categories:** Developer tools, AI, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 1,000 processed knowledge pages This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Website RAG Knowledge Builder This actor turns public websites into clean, RAG-ready knowledge records for AI assistants, support bots, search, documentation QA, and content audits. It crawls public internal pages, removes noisy layout text, extracts summaries, key facts, FAQ candidates, useful internal links, and retrieval chunks. ### What it is useful for - Use it to prepare website knowledge before embedding, vector indexing, chatbot ingestion, or documentation review. - Building cleaner research exports from messy public web surfaces. - Prioritizing prospects, pages, communities, or knowledge sources before manual review. - Feeding structured records into spreadsheets, CRMs, dashboards, or automation workflows. ### Input Provide one or more values in `startUrls`. ```json { "startUrls": [ "https://docs.apify.com/platform/actors" ], "maxItems": 8, "requestTimeoutSecs": 25, "maxConcurrency": 3 } ```` ### Output you get The dataset is intentionally compact and buyer-facing. Important fields include: - `sourceWebsite` - `pageUrl` - `pageTitle` - `knowledgeSummary` - `keyFacts` - `faqCandidates` - `internalLinks` - `ragChunks.chunkId` - `ragChunks.text` ### Practical workflow 1. Start with a small list of searches or URLs. 2. Review the first dataset rows and confirm the signals match your market. 3. Increase the input list for larger research batches. 4. Export the dataset to CSV, JSON, Google Sheets, or your automation pipeline. ### Notes and limitations - The actor uses public web data and does not bypass login walls, private content, CAPTCHA, or protected dashboards. - Public search snippets and pages can change, so results should be treated as research signals rather than legal, financial, or valuation advice. - The output avoids raw HTML and noisy debug fields so buyers can act on the data immediately. # Actor input Schema ## `startUrls` (type: `array`): Public website URLs to crawl into RAG-ready knowledge records. ## `maxItems` (type: `integer`): Maximum lead/results/chunks to keep per input. ## `requestTimeoutSecs` (type: `integer`): Maximum time to wait for each public web request. ## `maxConcurrency` (type: `integer`): How many inputs to process at the same time. ## `proxyConfiguration` (type: `object`): Optional Apify proxy configuration. ## `maxPagesPerWebsite` (type: `integer`): Maximum number of internal pages to crawl from each website URL. ## Actor input object example ```json { "startUrls": [ "https://docs.apify.com/platform/actors" ], "maxItems": 8, "requestTimeoutSecs": 25, "maxConcurrency": 3, "proxyConfiguration": { "useApifyProxy": true }, "maxPagesPerWebsite": 6 } ``` # Actor output Schema ## `results` (type: `string`): No description ## `summary` (type: `string`): No description # 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": [ "https://docs.apify.com/platform/actors" ] }; // Run the Actor and wait for it to finish const run = await client.actor("glowing_glove/website-rag-knowledge-builder").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": ["https://docs.apify.com/platform/actors"] } # Run the Actor and wait for it to finish run = client.actor("glowing_glove/website-rag-knowledge-builder").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": [ "https://docs.apify.com/platform/actors" ] }' | apify call glowing_glove/website-rag-knowledge-builder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=glowing_glove/website-rag-knowledge-builder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Website RAG Knowledge Builder", "description": "Crawl public website pages and build clean RAG-ready knowledge records with page summaries, key facts, FAQs, links, and retrieval chunks.", "version": "1.0", "x-build-id": "csO8vyTM7U01jj9Nf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/glowing_glove~website-rag-knowledge-builder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-glowing_glove-website-rag-knowledge-builder", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/glowing_glove~website-rag-knowledge-builder/runs": { "post": { "operationId": "runs-sync-glowing_glove-website-rag-knowledge-builder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/glowing_glove~website-rag-knowledge-builder/run-sync": { "post": { "operationId": "run-sync-glowing_glove-website-rag-knowledge-builder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Website URLs", "type": "array", "description": "Public website URLs to crawl into RAG-ready knowledge records.", "items": { "type": "string" } }, "maxItems": { "title": "Max items per input", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum lead/results/chunks to keep per input.", "default": 8 }, "requestTimeoutSecs": { "title": "Request timeout seconds", "minimum": 8, "maximum": 90, "type": "integer", "description": "Maximum time to wait for each public web request.", "default": 25 }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 8, "type": "integer", "description": "How many inputs to process at the same time.", "default": 3 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Optional Apify proxy configuration.", "default": { "useApifyProxy": true } }, "maxPagesPerWebsite": { "title": "Max pages per website", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum number of internal pages to crawl from each website URL.", "default": 6 } } }, "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 } } } } } } } } } } ```