# AI Training Data Generator (`second_coming/ai-training-data-generator`) Actor Convert web pages, documentation, and blog posts into structured AI training datasets in formats like Q\&A pairs, instruction-following examples, and cleaned raw text. - **URL**: https://apify.com/second\_coming/ai-training-data-generator.md - **Developed by:** [Richard P](https://apify.com/second_coming) (community) - **Categories:** Developer tools, AI - **Stats:** 2 total users, 0 monthly users, 100.0% 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 ## AI Training Data Generator [![Build](https://img.shields.io/badge/Apify-Actor-blue)](https://apify.com/) [![Python](https://img.shields.io/badge/python-3.13-blue)](https://www.python.org/) An Apify Actor that converts web pages, documentation, blog posts, and other web content into structured AI training datasets. Supports multiple output formats suitable for fine-tuning LLMs. ### Features - **Convert any web page** into structured training data - **Three output formats:** - **Q&A Pairs** - Headings become questions, content becomes answers. Ideal for training QA models. - **Instruction Following** - Each section becomes an instruction with expected completion. Perfect for instruction-tuning datasets. - **Raw Text** - Cleaned, structured text without instruction framing. Great for continued pre-training. - **Smart content extraction** - Automatically removes navigation, headers, footers, sidebars, and other non-content elements. - **Metadata enrichment** - Optionally includes source URL, section heading, and extraction method. - **Deduplication** - Automatically removes duplicate examples by content hash. - **Graceful abort** - Handles platform shutdown signals cleanly. ### Input | Field | Type | Required | Description | |-------|------|----------|-------------| | `sourceUrls` | `array[string]` | ✅ | URLs of web pages/documentation to convert | | `outputFormat` | `enum` | ❌ | `qa_pairs`, `instruction_following`, or `raw_text` (default: `qa_pairs`) | | `maxExamples` | `integer` | ❌ | Maximum examples per URL (default: `100`) | | `includeMetadata` | `boolean` | ❌ | Include source metadata in output (default: `true`) | | `webhookUrl` | `string` | ❌ | Optional webhook to call on completion | #### Example input ```json { "sourceUrls": [ "https://docs.example.com/getting-started", "https://docs.example.com/advanced-usage" ], "outputFormat": "qa_pairs", "maxExamples": 50, "includeMetadata": true } ```` ### Output Each dataset item has the following structure: | Field | Description | |-------|-------------| | `exampleId` | Unique content-hash identifier | | `exampleFormat` | The output format used | | `instruction` | Question, instruction, or empty (for raw\_text) | | `input` | Additional input context (currently empty, reserved for future use) | | `output` | The answer, completion, or structured text | | `source` | Source URL | | `generatedAt` | ISO timestamp of generation | When `includeMetadata` is true, each item also includes a `_metadata` field with `sourceUrl`, `sectionHeading`, and `extractionMethod`. ### Use Cases - **Fine-tuning LLMs** - Generate instruction-following datasets from documentation - **RAG training** - Create Q\&A pairs from knowledge base articles - **Domain adaptation** - Extract clean text from domain-specific websites - **Synthetic data generation** - Convert technical documentation into training examples - **Dataset augmentation** - Add web-sourced examples to existing training pipelines ### Pricing Pay-per-event: **$0.02 USD per run**. Covers up to 100 URLs per run. Each URL generates up to `maxExamples` training examples. # Actor input Schema ## `sourceUrls` (type: `array`): URLs of web pages, documentation, or blog posts to convert into training data. ## `outputFormat` (type: `string`): The format of the generated training data. ## `maxExamples` (type: `integer`): Maximum number of training examples to generate (per URL). ## `includeMetadata` (type: `boolean`): Whether to include source URL, section heading, and extraction method in output. ## `webhookUrl` (type: `string`): Optional webhook URL to call when the run completes. ## Actor input object example ```json { "sourceUrls": [ "https://example.com" ], "outputFormat": "qa_pairs", "maxExamples": 100, "includeMetadata": true } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sourceUrls": [ "https://example.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("second_coming/ai-training-data-generator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "sourceUrls": ["https://example.com"] } # Run the Actor and wait for it to finish run = client.actor("second_coming/ai-training-data-generator").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 '{ "sourceUrls": [ "https://example.com" ] }' | apify call second_coming/ai-training-data-generator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=second_coming/ai-training-data-generator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Training Data Generator", "description": "Convert web pages, documentation, and blog posts into structured AI training datasets in formats like Q&A pairs, instruction-following examples, and cleaned raw text.", "version": "0.0", "x-build-id": "9wPIXNedLsuNljyck" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/second_coming~ai-training-data-generator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-second_coming-ai-training-data-generator", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/second_coming~ai-training-data-generator/runs": { "post": { "operationId": "runs-sync-second_coming-ai-training-data-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/second_coming~ai-training-data-generator/run-sync": { "post": { "operationId": "run-sync-second_coming-ai-training-data-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "sourceUrls" ], "properties": { "sourceUrls": { "title": "Source URLs", "type": "array", "description": "URLs of web pages, documentation, or blog posts to convert into training data." }, "outputFormat": { "title": "Output Format", "enum": [ "qa_pairs", "instruction_following", "raw_text" ], "type": "string", "description": "The format of the generated training data.", "default": "qa_pairs" }, "maxExamples": { "title": "Max Examples", "type": "integer", "description": "Maximum number of training examples to generate (per URL).", "default": 100 }, "includeMetadata": { "title": "Include Metadata", "type": "boolean", "description": "Whether to include source URL, section heading, and extraction method in output.", "default": true }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Optional webhook URL to call when the run completes." } } }, "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 } } } } } } } } } } ```