# Q\&A Dataset Extractor for LLM Fine-Tuning (`deniz_schloesser/qa-dataset-extractor`) Actor Crawl any website, documentation or FAQ and turn it into clean, deduplicated question-answer pairs in OpenAI / Alpaca / plain JSONL format - ready for fine-tuning and RAG. - **URL**: https://apify.com/deniz\_schloesser/qa-dataset-extractor.md - **Developed by:** [Deniz SchlΓΆsser](https://apify.com/deniz_schloesser) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 q\&a pairs 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 ## Q&A Dataset Extractor for LLM Fine-Tuning **Turn any website, documentation portal, or FAQ into clean, deduplicated question-answer pairs β€” ready to fine-tune a model or power a RAG / support chatbot.** Most scrapers stop at raw HTML or Markdown and leave the hard part to you: turning a pile of text into actual *training examples*. This Actor goes the whole way. It crawls your source, extracts the real content (no nav, ads, or boilerplate), splits it into context-preserving chunks, and uses Claude to generate grounded, self-contained **Q&A pairs** in the exact JSONL format your training pipeline expects. You bring a source URL. You get back a fine-tuning-ready dataset. --- ### Why this Actor - 🎯 **Training-ready output, not raw text.** Get `{question, answer}` pairs in OpenAI, Alpaca, or plain JSONL β€” drop them straight into a fine-tuning job. - 🧹 **Clean extraction.** Mozilla Readability strips navigation, sidebars, cookie banners, and ads. The model never sees junk, so your dataset doesn't either. - πŸ”’ **Grounded answers, no hallucination.** Every answer is constrained to the crawled content β€” the prompt forbids inventing facts. - ♻️ **Automatic deduplication.** Near-identical questions across pages are collapsed, so you don't pay to train on the same thing twice. - πŸ’Έ **Bring your own Claude key (BYOK).** You control model choice and token spend. Default `claude-haiku-4-5` keeps costs at roughly **$1 per 1,000 pairs** in tokens. - πŸ§ͺ **Free dry run.** Preview crawling and chunking with zero LLM cost before you spend anything. --- ### What it does ```` Start URL(s) β”‚ 1. Crawl β†’ follows same-domain links up to your page limit β–Ό Clean Markdown β†’ Mozilla Readability + Turndown (main content only) β”‚ 2. Chunk β†’ paragraph-aware, with overlap to preserve context β–Ό Content chunks β”‚ 3. Generate β†’ Claude produces up to N grounded Q\&A pairs per chunk β–Ό β”‚ 4. Deduplicate β†’ collapses repeated questions β–Ό JSONL dataset β†’ OpenAI / Alpaca / plain, with source\_url + source\_title ```` --- ### Example **Input:** ```json { "startUrls": [{ "url": "https://docs.apify.com/academy/web-scraping-for-beginners" }], "maxPagesToCrawl": 5, "maxQuestionsPerChunk": 3, "outputFormat": "openai", "model": "claude-haiku-4-5", "anthropicApiKey": "sk-ant-..." } ```` **Output (one dataset item, `openai` format):** ```json { "messages": [ { "role": "user", "content": "What is the main project you'll build in this JavaScript web scraping course?" }, { "role": "assistant", "content": "In this course, you'll create an application for watching prices. It will be able to scrape all product pages of an e-commerce website and record prices. Data from several runs of such a program would be useful for seeing trends in price changes, detecting discounts, and more." } ], "source_url": "https://docs.apify.com/academy/web-scraping-for-beginners", "source_title": "Web scraping basics for JavaScript devs | Academy | Apify Documentation" } ``` Every item carries `source_url` and `source_title` so you can trace, filter, or cite each example. *** ### Output formats Pick the shape your training pipeline expects with the `outputFormat` setting: | Format | Shape | Use it for | |--------|-------|------------| | `openai` | `{ "messages": [{ "role": "user", ... }, { "role": "assistant", ... }] }` | OpenAI fine-tuning, chat-format SFT | | `alpaca` | `{ "instruction": "...", "input": "", "output": "..." }` | Llama / Mistral / open-model instruction tuning | | `plain` | `{ "question": "...", "answer": "..." }` | RAG eval sets, custom pipelines, embeddings | > The output format is independent of the model that generates the pairs β€” Claude produces pairs in whichever training shape you choose. *** ### Use cases **πŸ€– Build a support chatbot from your docs.** Point it at your help center or product docs and generate a Q\&A set to fine-tune or seed a RAG index β€” so the bot answers in your product's own words. **πŸŽ“ Fine-tune a domain expert model.** Turn a knowledge base, wiki, or set of guides into thousands of instruction examples for a specialized model, without hand-writing them. **πŸ“š Create RAG evaluation sets.** Generate grounded question-answer pairs to benchmark retrieval quality β€” does your RAG system actually find the right answer? **🌍 Localize training data.** Use `customInstructions` (e.g. *"Write all questions and answers in German"*) to produce datasets in any language your source covers. *** ### Input reference | Field | Description | Default | |-------|-------------|---------| | `startUrls` | Pages to crawl (follows same-domain links) | β€” (required) | | `maxPagesToCrawl` | Hard limit on pages crawled | `10` | | `maxQuestionsPerChunk` | Q\&A pairs generated per content chunk | `3` | | `chunkSize` / `chunkOverlap` | Chunking in characters (overlap auto-capped at half) | `4000` / `200` | | `outputFormat` | `openai` | `alpaca` | `plain` | `openai` | | `model` | `claude-haiku-4-5` | `claude-sonnet-4-6` | `claude-opus-4-8` | `claude-haiku-4-5` | | `anthropicApiKey` | Your Anthropic (Claude) API key β€” `sk-ant-...` (secret) | β€” | | `customInstructions` | Extra guidance, e.g. *"answer in German"* or *"focus on pricing"* | β€” | | `dryRun` | Skip the LLM and just output chunks β€” **free preview** | `false` | *** ### Choosing a model | Model | Token cost (your key) | Best for | |-------|----------------------|----------| | **Claude Haiku 4.5** (default) | ~$1 / 1,000 pairs | High-volume datasets, the cost-conscious default | | **Claude Sonnet 4.6** | ~$3 / 1,000 pairs | Nuanced answers, technical or complex sources | | **Claude Opus 4.8** | ~$5 / 1,000 pairs | Highest quality where it matters most | *** ### Quick start 1. Add one or more **Start URLs** (e.g. your docs site). 2. Paste your **Anthropic (Claude) API key** (`sk-ant-...`). Get one at [console.anthropic.com](https://console.anthropic.com). 3. *(Optional)* Set `dryRun: true` first to preview crawling and chunking for free. 4. Run, then download the dataset as **JSONL** and feed it to your fine-tuning job. *** ### Notes & responsible use - **Bring your own key.** This Actor calls the Claude API with the key you provide; you are billed by Anthropic for token usage directly. - **Answers are grounded.** The prompt forbids inventing facts β€” answers are constrained to the crawled content. - **Respect each site's rules.** Only crawl content you are permitted to use. Honor the target site's Terms of Service and `robots` directives, and respect copyright and data-protection law (e.g. GDPR) for any personal data you process. *** *Questions or a source that doesn't extract cleanly? Open an issue on the Actor page β€” feedback shapes the roadmap.* # Actor input Schema ## `startUrls` (type: `array`): Pages to crawl. The crawler follows links on the same domain until 'Max pages' is reached. ## `maxPagesToCrawl` (type: `integer`): Hard limit on the number of pages crawled. ## `maxQuestionsPerChunk` (type: `integer`): How many question-answer pairs the model should generate per content chunk. ## `chunkSize` (type: `integer`): Target size of each content chunk fed to the model. ## `chunkOverlap` (type: `integer`): Characters of overlap between consecutive chunks to preserve context. Automatically capped at half the chunk size. ## `outputFormat` (type: `string`): Schema of each dataset item. ## `model` (type: `string`): Claude model used to generate the Q\&A pairs. ## `anthropicApiKey` (type: `string`): Your Anthropic API key (sk-ant-...). Required unless 'Dry run' is enabled. ## `customInstructions` (type: `string`): Extra guidance for the model, e.g. 'Write answers in German' or 'Focus on pricing and setup questions'. ## `dryRun` (type: `boolean`): Skip the LLM step and just output the extracted chunks. Use this to test crawling/chunking for free. ## Actor input object example ```json { "startUrls": [ { "url": "https://docs.apify.com/academy/web-scraping-for-beginners" } ], "maxPagesToCrawl": 10, "maxQuestionsPerChunk": 3, "chunkSize": 4000, "chunkOverlap": 200, "outputFormat": "openai", "model": "claude-haiku-4-5", "dryRun": 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 = { "startUrls": [ { "url": "https://docs.apify.com/academy/web-scraping-for-beginners" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("deniz_schloesser/qa-dataset-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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/academy/web-scraping-for-beginners" }] } # Run the Actor and wait for it to finish run = client.actor("deniz_schloesser/qa-dataset-extractor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://docs.apify.com/academy/web-scraping-for-beginners" } ] }' | apify call deniz_schloesser/qa-dataset-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=deniz_schloesser/qa-dataset-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Q&A Dataset Extractor for LLM Fine-Tuning", "description": "Crawl any website, documentation or FAQ and turn it into clean, deduplicated question-answer pairs in OpenAI / Alpaca / plain JSONL format - ready for fine-tuning and RAG.", "version": "0.1", "x-build-id": "Ihb6D6ErOnF7lb5qm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/deniz_schloesser~qa-dataset-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-deniz_schloesser-qa-dataset-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/deniz_schloesser~qa-dataset-extractor/runs": { "post": { "operationId": "runs-sync-deniz_schloesser-qa-dataset-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/deniz_schloesser~qa-dataset-extractor/run-sync": { "post": { "operationId": "run-sync-deniz_schloesser-qa-dataset-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Pages to crawl. The crawler follows links on the same domain until 'Max pages' is reached.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxPagesToCrawl": { "title": "Max pages to crawl", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Hard limit on the number of pages crawled.", "default": 10 }, "maxQuestionsPerChunk": { "title": "Max Q&A pairs per chunk", "minimum": 1, "maximum": 20, "type": "integer", "description": "How many question-answer pairs the model should generate per content chunk.", "default": 3 }, "chunkSize": { "title": "Chunk size (characters)", "minimum": 500, "maximum": 16000, "type": "integer", "description": "Target size of each content chunk fed to the model.", "default": 4000 }, "chunkOverlap": { "title": "Chunk overlap (characters)", "minimum": 0, "maximum": 4000, "type": "integer", "description": "Characters of overlap between consecutive chunks to preserve context. Automatically capped at half the chunk size.", "default": 200 }, "outputFormat": { "title": "Output format", "enum": [ "openai", "alpaca", "plain" ], "type": "string", "description": "Schema of each dataset item.", "default": "openai" }, "model": { "title": "Model", "enum": [ "claude-haiku-4-5", "claude-sonnet-4-6", "claude-opus-4-8" ], "type": "string", "description": "Claude model used to generate the Q&A pairs.", "default": "claude-haiku-4-5" }, "anthropicApiKey": { "title": "Anthropic (Claude) API key", "type": "string", "description": "Your Anthropic API key (sk-ant-...). Required unless 'Dry run' is enabled." }, "customInstructions": { "title": "Custom instructions (optional)", "type": "string", "description": "Extra guidance for the model, e.g. 'Write answers in German' or 'Focus on pricing and setup questions'." }, "dryRun": { "title": "Dry run (no LLM, no cost)", "type": "boolean", "description": "Skip the LLM step and just output the extracted chunks. Use this to test crawling/chunking for free.", "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 } } } } } } } } } } ```