# AI Dataset Converter - Website to Training Data (`boztek-ltd/ai-dataset-converter`) Actor Crawl websites and convert content into AI-ready formats: RAG chunks, fine-tuning JSONL, Q\&A pairs, clean Markdown. Token-aware chunking, quality scoring, deduplication. No external LLM API needed. - **URL**: https://apify.com/boztek-ltd/ai-dataset-converter.md - **Developed by:** [Boztek LTD](https://apify.com/boztek-ltd) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 83.3% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.008 / actor start 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 ## AI Dataset Converter — Website to AI Training Data Convert any website into AI-ready datasets for **RAG pipelines**, **LLM fine-tuning**, and **Q&A training**. Token-aware chunking, quality scoring, content deduplication — all without external API calls. ### What does AI Dataset Converter do? AI Dataset Converter crawls websites and transforms their content into structured, token-aware datasets optimized for AI/ML workflows: - **RAG Chunks** — Embedding-ready JSON with configurable chunk size and overlap - **Fine-tuning JSONL** — OpenAI-compatible `messages[]` format - **Q&A Pairs** — Automatically extracted from FAQ pages and heading structures - **Clean Markdown** — Boilerplate-free content with full page metadata Every chunk includes the cl100k_base (GPT-4 compatible) token count, a 0.0–1.0 quality score, source URL, language, and canonical URL — ready to ingest into Pinecone, Qdrant, Weaviate, LangChain, LlamaIndex, or any vector store. ### Why AI Dataset Converter? | Feature | Website Content Crawler | **AI Dataset Converter** | |---|---|---| | Output | Raw Markdown / text | **Structured AI-ready formats** | | Chunking | Manual | **Token-aware, configurable** | | Token counting | — | **cl100k_base (GPT-4)** | | Q&A extraction | — | **5 rule-based strategies** | | Quality scoring | — | **0.0–1.0 per page** | | Deduplication | URL-based | **Content fingerprinting** | | Fine-tuning format | — | **OpenAI JSONL** | | External LLM cost | None | **None** | ### How much does it cost? AI Dataset Converter uses **pay-per-event** pricing at approximately **$0.002 per output item** (chunk, Q&A pair, or page). Platform compute units are included. | Use case | Pages | Output items | Estimated cost | |---|---|---|---| | Small docs site | 50 | ~250 chunks | ~$0.50 | | Medium blog | 500 | ~2,500 chunks | ~$5.00 | | Large docs + FAQ | 2,000 | ~12,000 items | ~$24.00 | Apify's free plan provides $5 of platform credit per month — enough to test on small sites. ### Output formats #### 1. RAG Chunks (`rag-chunks`) One JSON item per chunk with embedding-ready text plus rich metadata: ```json { "chunk_id": "550e8400-e29b-41d4-a716-446655440000", "source_url": "https://docs.example.com/getting-started", "canonical_url": "https://docs.example.com/getting-started", "text": "Getting started with Example SDK...", "markdown": "## Getting Started\n\nWelcome to...", "chunk_index": 0, "total_chunks": 3, "token_count": 487, "char_count": 1843, "page_title": "Getting Started", "page_description": "Quick start guide", "page_language": "en", "page_author": "Docs Team", "page_date": "2026-04-12T00:00:00.000Z", "quality_score": 0.85, "content_type": "documentation", "crawled_at": "2026-05-12T08:30:00.000Z", "actor_version": "1.0.0" } ```` #### 2. Fine-tuning JSONL (`fine-tuning-jsonl`) OpenAI-compatible `messages[]` format. Prompts are synthesized rule-based (no LLM): ```json { "messages": [ { "role": "system", "content": "You are a helpful assistant that provides information about Example Documentation." }, { "role": "user", "content": "What is the chunk size?" }, { "role": "assistant", "content": "The chunk size is the target number of tokens per output chunk..." } ], "_metadata": { "source_url": "https://docs.example.com/chunking", "chunk_id": "...", "token_count": 412, "quality_score": 0.81 } } ``` #### 3. Q\&A Pairs (`qa-pairs`) Extracted from FAQ pages using five rule-based strategies: ```json { "question": "Can I cancel my subscription?", "answer": "Yes, you can cancel anytime from the billing settings page in your account.", "source_url": "https://example.com/help/faq", "extraction_method": "faq_html", "confidence": 0.95, "token_count": 28, "page_title": "FAQ" } ``` Extraction strategies (in confidence order): 1. **`faq_schema`** — JSON-LD `FAQPage` schema (confidence 1.0) 2. **`faq_html`** — `
` elements (0.95) 3. **`dt_dd`** — Definition lists `
/
/
` (0.90) 4. **`accordion`** — `aria-controls` / `data-toggle` patterns (0.85) 5. **`heading_paragraph`** — `

/

` + following content (0.5–0.9) #### 4. Clean Markdown (`markdown`) Full-page Markdown with boilerplate removed and complete metadata. ### Input options | Option | Type | Default | Description | |---|---|---|---| | `startUrls` | array | **required** | Initial URLs to crawl | | `maxPages` | integer | 100 | Maximum number of pages (0 = unlimited) | | `maxDepth` | integer | 5 | Link-follow depth from start URLs | | `crawlerType` | string | `adaptive` | `adaptive` / `cheerio` / `playwright` | | `includeGlobs` / `excludeGlobs` | array | `[]` | URL pattern filters | | `outputFormat` | string | `rag-chunks` | `rag-chunks` / `fine-tuning-jsonl` / `qa-pairs` / `markdown` / `all` | | `chunkSize` | integer | 512 | Target tokens per chunk | | `chunkOverlap` | integer | 50 | Token overlap between chunks | | `extractQAPairs` | boolean | `true` | Run Q\&A extraction strategies | | `language` | string | `""` | ISO 639-1 code language filter | | `minContentLength` | integer | 100 | Skip pages shorter than this (chars) | | `minQualityScore` | number | 0.3 | Skip pages below this score (0.0–1.0) | | `removeDuplicates` | boolean | `true` | Content-fingerprint deduplication | | `removeBoilerplate` | boolean | `true` | Strip nav/footer/cookie banners | | `proxyConfiguration` | object | Apify Proxy | Proxy settings | | `maxConcurrency` | integer | 10 | Parallel page processing | ### Use cases 1. **Build RAG chatbots** — Crawl documentation → chunk → embed in Pinecone/Qdrant/Weaviate 2. **Fine-tune LLMs** — Convert knowledge bases to OpenAI training format 3. **Create Q\&A datasets** — Extract FAQ data for customer-support AI 4. **Feed AI agents** — Provide structured web knowledge to autonomous agents ### Integrations Output is plain JSON / JSONL and works with LangChain, LlamaIndex, Pinecone, Qdrant, Weaviate, Milvus, MongoDB Atlas, OpenAI fine-tuning, and any tool that accepts JSON. ### Quality scoring (heuristic, no LLM) Each page receives a 0.0–1.0 score computed from: - **Content length** (25%) — Pages between 500 and 10000 chars score highest - **Text density** (25%) — Ratio of extracted text to original HTML - **Paragraph count** (15%) — ≥3 paragraphs preferred - **Heading presence** (10%) — At least one `

`–`

` - **Link density** (10%) — Low anchor-text ratio preferred - **Repetition** (15%) — Unique-sentence ratio Pages scoring below `minQualityScore` are filtered out before token usage. ### Token-aware chunking Chunks are produced with a recursive splitter that respects natural boundaries: 1. Split by paragraph (`\n\n`) 2. If a paragraph exceeds `chunkSize`, split by sentence 3. If a sentence exceeds `chunkSize`, split by token 4. Apply `chunkOverlap` by prepending the last N tokens of the previous chunk Token counts are computed with **js-tiktoken** using the `cl100k_base` encoding — identical to GPT-4 / `text-embedding-3-*`. ### Limitations - No LLM-based extraction (by design — keeps cost predictable) - Q\&A extraction works best on structured pages (FAQ, docs with headings) - Login-protected content not supported without cookie injection - JavaScript-heavy SPAs may need `crawlerType: "playwright"` for full rendering # Actor input Schema ## `startUrls` (type: `array`): Initial URLs to crawl. Sub-pages are automatically discovered and crawled based on depth and limit settings. ## `maxPages` (type: `integer`): Maximum number of pages to crawl. Set to 0 for unlimited. ## `maxDepth` (type: `integer`): How many link hops from a start URL the crawler will follow. 0 = only start URLs. ## `crawlerType` (type: `string`): Crawler engine. Cheerio (default) is fast and cheap; switch to Playwright or Adaptive only for JavaScript-heavy single-page apps. ## `includeGlobs` (type: `array`): Only URLs matching these glob patterns will be crawled. Leave empty to crawl all discovered URLs. ## `excludeGlobs` (type: `array`): URLs matching these glob patterns will be skipped. ## `outputFormat` (type: `string`): Primary output format. Choose 'All formats' to produce all four into separate named datasets. ## `chunkSize` (type: `integer`): Target token count per chunk (cl100k\_base / GPT-4 compatible). Applies to RAG and fine-tuning formats. ## `chunkOverlap` (type: `integer`): Token overlap between consecutive chunks. Helps preserve context across boundaries. ## `extractQAPairs` (type: `boolean`): Automatically extract question-answer pairs from FAQ pages and heading+paragraph structures. ## `language` (type: `string`): ISO 639-1 code (e.g. 'en', 'tr', 'de'). Only pages in this language are included. Leave empty to allow all. ## `minContentLength` (type: `integer`): Pages with cleaned content shorter than this are skipped. ## `minQualityScore` (type: `number`): Pages scoring below this heuristic threshold are excluded from output. ## `removeDuplicates` (type: `boolean`): Detect and skip near-duplicate pages using content fingerprinting. ## `removeBoilerplate` (type: `boolean`): Strip navigation, footers, cookie banners, and other non-content elements. ## `proxyConfiguration` (type: `object`): Apify Proxy settings. Residential proxy recommended for geo-aware sites. ## `maxConcurrency` (type: `integer`): Maximum number of pages processed in parallel. Browser crawler: 1-5; HTTP crawler: 10-50. ## Actor input object example ```json { "startUrls": [ { "url": "https://docs.apify.com/" } ], "maxPages": 100, "maxDepth": 5, "crawlerType": "cheerio", "includeGlobs": [], "excludeGlobs": [ { "glob": "**/*.pdf" }, { "glob": "**/*.zip" }, { "glob": "**/login*" } ], "outputFormat": "rag-chunks", "chunkSize": 512, "chunkOverlap": 50, "extractQAPairs": true, "language": "", "minContentLength": 100, "minQualityScore": 0.3, "removeDuplicates": true, "removeBoilerplate": true, "proxyConfiguration": { "useApifyProxy": true }, "maxConcurrency": 10 } ``` # 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/" } ], "includeGlobs": [], "excludeGlobs": [ { "glob": "**/*.pdf" }, { "glob": "**/*.zip" }, { "glob": "**/login*" } ], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("boztek-ltd/ai-dataset-converter").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/" }], "includeGlobs": [], "excludeGlobs": [ { "glob": "**/*.pdf" }, { "glob": "**/*.zip" }, { "glob": "**/login*" }, ], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("boztek-ltd/ai-dataset-converter").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/" } ], "includeGlobs": [], "excludeGlobs": [ { "glob": "**/*.pdf" }, { "glob": "**/*.zip" }, { "glob": "**/login*" } ], "proxyConfiguration": { "useApifyProxy": true } }' | apify call boztek-ltd/ai-dataset-converter --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=boztek-ltd/ai-dataset-converter", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Dataset Converter - Website to Training Data", "description": "Crawl websites and convert content into AI-ready formats: RAG chunks, fine-tuning JSONL, Q&A pairs, clean Markdown. Token-aware chunking, quality scoring, deduplication. No external LLM API needed.", "version": "1.0", "x-build-id": "JCdi7bWCVJOC3Vluv" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/boztek-ltd~ai-dataset-converter/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-boztek-ltd-ai-dataset-converter", "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/boztek-ltd~ai-dataset-converter/runs": { "post": { "operationId": "runs-sync-boztek-ltd-ai-dataset-converter", "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/boztek-ltd~ai-dataset-converter/run-sync": { "post": { "operationId": "run-sync-boztek-ltd-ai-dataset-converter", "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": "Initial URLs to crawl. Sub-pages are automatically discovered and crawled based on depth and limit settings.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxPages": { "title": "Max pages to crawl", "minimum": 0, "maximum": 100000, "type": "integer", "description": "Maximum number of pages to crawl. Set to 0 for unlimited.", "default": 100 }, "maxDepth": { "title": "Max crawl depth", "minimum": 0, "maximum": 50, "type": "integer", "description": "How many link hops from a start URL the crawler will follow. 0 = only start URLs.", "default": 5 }, "crawlerType": { "title": "Crawler type", "enum": [ "cheerio", "adaptive", "playwright" ], "type": "string", "description": "Crawler engine. Cheerio (default) is fast and cheap; switch to Playwright or Adaptive only for JavaScript-heavy single-page apps.", "default": "cheerio" }, "includeGlobs": { "title": "Include URL patterns", "type": "array", "description": "Only URLs matching these glob patterns will be crawled. Leave empty to crawl all discovered URLs.", "default": [], "items": { "type": "object", "required": [ "glob" ], "properties": { "glob": { "type": "string", "title": "Glob of a web page" } } } }, "excludeGlobs": { "title": "Exclude URL patterns", "type": "array", "description": "URLs matching these glob patterns will be skipped.", "default": [], "items": { "type": "object", "required": [ "glob" ], "properties": { "glob": { "type": "string", "title": "Glob of a web page" } } } }, "outputFormat": { "title": "Output format", "enum": [ "rag-chunks", "fine-tuning-jsonl", "qa-pairs", "markdown", "all" ], "type": "string", "description": "Primary output format. Choose 'All formats' to produce all four into separate named datasets.", "default": "rag-chunks" }, "chunkSize": { "title": "Chunk size (tokens)", "minimum": 64, "maximum": 8192, "type": "integer", "description": "Target token count per chunk (cl100k_base / GPT-4 compatible). Applies to RAG and fine-tuning formats.", "default": 512 }, "chunkOverlap": { "title": "Chunk overlap (tokens)", "minimum": 0, "maximum": 2048, "type": "integer", "description": "Token overlap between consecutive chunks. Helps preserve context across boundaries.", "default": 50 }, "extractQAPairs": { "title": "Extract Q&A pairs", "type": "boolean", "description": "Automatically extract question-answer pairs from FAQ pages and heading+paragraph structures.", "default": true }, "language": { "title": "Content language filter", "type": "string", "description": "ISO 639-1 code (e.g. 'en', 'tr', 'de'). Only pages in this language are included. Leave empty to allow all.", "default": "" }, "minContentLength": { "title": "Minimum content length (characters)", "minimum": 0, "type": "integer", "description": "Pages with cleaned content shorter than this are skipped.", "default": 100 }, "minQualityScore": { "title": "Minimum quality score (0.0-1.0)", "minimum": 0, "maximum": 1, "type": "number", "description": "Pages scoring below this heuristic threshold are excluded from output.", "default": 0.3 }, "removeDuplicates": { "title": "Remove duplicate content", "type": "boolean", "description": "Detect and skip near-duplicate pages using content fingerprinting.", "default": true }, "removeBoilerplate": { "title": "Remove boilerplate", "type": "boolean", "description": "Strip navigation, footers, cookie banners, and other non-content elements.", "default": true }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify Proxy settings. Residential proxy recommended for geo-aware sites.", "default": { "useApifyProxy": true } }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of pages processed in parallel. Browser crawler: 1-5; HTTP crawler: 10-50.", "default": 10 } } }, "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 } } } } } } } } } } ```