# Q\&A Knowledge Extractor (Stack Exchange) (`datahoeven/qa-knowledge-extractor`) Actor Extracts RAG-ready Q\&A pairs from the Stack Exchange network via the official API. Returns coupled question+answer records with full attribution, license metadata, and incremental diff support for growing datasets. - **URL**: https://apify.com/datahoeven/qa-knowledge-extractor.md - **Developed by:** [Daan Hoeven](https://apify.com/datahoeven) (community) - **Categories:** AI, Developer tools, Agents - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 q\&a pair extracteds 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 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 Knowledge Extractor (Stack Exchange) **Extract clean, production-ready Q&A datasets from Stack Overflow and the entire Stack Exchange network for RAG, fine-tuning, and AI agent development.** This Apify Actor fetches question-answer pairs from the Stack Exchange API and delivers them as **RAG-ready JSON records** with full licensing attribution, quality metadata, and incremental diff support. Each pair is coupled (question ↔ best/accepted answer), normalized for immediate use, and includes code examples intact. Perfect for building **retrieval-augmented generation (RAG) systems**, **fine-tuning language models**, **training AI agents**, and **growing proprietary knowledge bases** without maintenance overhead. --- ### What You Get - **Coupled Q&A pairs**: Each record contains one question and its best (accepted or highest-scoring) answer, ready to use as a training example or RAG context window. - **Code-safe formatting**: Markdown with fenced code blocks preserved intact — no corrupted Python snippets or mangled SQL. - **Full attribution**: Every record includes author names, profile URLs, and exact license version per content. Comply with CC BY-SA licensing automatically. - **Incremental extraction**: Run the Actor multiple times. Only new or updated Q&A pairs are fetched and charged — grow your dataset without re-processing old content. - **RAG chunking** (optional): Automatically split answer text into overlapping chunks on natural boundaries (paragraphs, code blocks) for vector embedding and retrieval. - **Quality filters**: Minimum score thresholds, tag filtering, and accepted-answer-only mode eliminate low-quality noise. --- ### Use Cases #### 1. **Retrieval-Augmented Generation (RAG)** Build knowledge-grounded chatbots and search systems that cite real Stack Overflow solutions. The Actor provides clean, pre-chunked context windows ready to embed. ```` User query: "How to parse JSON in Python?" ↓ RAG retrieval → Returns top-scoring Stack Overflow answers + metadata → LLM generates response citing sources ``` #### 2. **Fine-tuning Language Models** Create domain-specific instruction datasets by filtering on tags (Python, React, databases) and score thresholds. Each Q&A pair becomes a training example. **Example**: Fine-tune a model on production Docker best practices by filtering `tag: "docker"` and `minQuestionScore: 10`. #### 3. **AI Agents & Multi-Tool Learning** Equip agents with task-specific knowledge bases. The Actor outputs clean, parseable records agents can query during reasoning. ``` Agent: "I need to debug a Flask authentication issue." → Queries the local Q\&A dataset → Returns 5 relevant Stack Overflow answers → Incorporates into reasoning chain ```` #### 4. **Internal Knowledge Base** Populate a company knowledge base with Stack Overflow solutions relevant to your tech stack. Incremental mode keeps it fresh without re-scraping. #### 5. **Academic Research** Extract Q&A datasets for studying software engineering practices, API design patterns, or how developers solve real problems at scale. --- ### Key Features | Feature | Details | |---------|---------| | **Data source** | Stack Exchange API v2.3 (official, stable) | | **Supported sites** | Stack Overflow, Server Fault, Super User, Ask Ubuntu, and 200+ other Stack Exchange sites | | **Q&A coupling** | Automatic pairing of questions with best/accepted answers | | **Incremental mode** | Store a high-water-mark; next run only fetches new/updated pairs — save money & time | | **Filtering** | Tags (AND), score thresholds, date ranges, free-text search, accepted-answer-only | | **Output schema** | Structured JSON with question metadata, answer body, licensing, attribution, optional chunks | | **Code handling** | Markdown with fenced code blocks intact — never corrupts code samples | | **License compliance** | CC BY-SA attribution built into every record; seamless license version detection | | **RAG-ready** | Optional chunking on paragraph/code-block boundaries; overlap support for context preservation | | **Pricing** | Pay-per-result: $0.005 per new/updated Q&A pair; incremental mode means you only pay once | | **Error handling** | Graceful quota management, schema-drift detection, canary sanity checks | --- ### Example: Input & Output #### Input Configuration ```json { "site": "stackoverflow", "tags": ["python", "pandas"], "query": "how to merge dataframes", "minQuestionScore": 5, "minAnswerScore": 10, "acceptedOnly": true, "incremental": true, "enableChunking": false, "maxItems": 100 } ```` #### Output Record (JSON) ````json { "_schemaVersion": 1, "site": "stackoverflow", "questionId": 11227809, "question": { "title": "Why is processing a sorted array faster than an unsorted array?", "bodyMarkdown": "A... branch misprediction explanation... ```code block``` preserved.", "tags": ["c++", "performance", "cpu-cache"], "score": 27000, "viewCount": 1900000, "createdAt": "2011-06-27T13:51:36Z", "lastActivityAt": "2024-02-10T08:00:00Z", "url": "https://stackoverflow.com/q/11227809", "author": { "name": "GManNickG", "url": "https://stackoverflow.com/users/123456/gmannickG" } }, "answer": { "answerId": 11227902, "bodyMarkdown": "Excellent explanation of cache lines and branch predictors... ```code``` intact.", "score": 35000, "isAccepted": true, "createdAt": "2011-06-27T13:56:42Z", "url": "https://stackoverflow.com/a/11227902", "author": { "name": "Mysticial", "url": "https://stackoverflow.com/users/555555/mysticial" } }, "license": { "name": "CC BY-SA 4.0", "url": "https://creativecommons.org/licenses/by-sa/4.0/" }, "attribution": "Question by GManNickG (https://stackoverflow.com/users/123456/gmannickG) and answer by Mysticial (https://stackoverflow.com/users/555555/mysticial) on Stack Overflow, licensed under CC BY-SA 4.0.", "scrapedAt": "2024-06-07T10:00:00Z" } ```` *** ### Licensing & Attribution **This Actor respects CC BY-SA licensing.** Every output record includes: 1. **License metadata** (`license` object) with the correct CC version (4.0 for content created after May 2, 2018; 3.0 for older content). 2. **Attribution string** (`attribution` field) listing question author, answer author, Stack Overflow URL, and license version. #### Your Responsibilities - **Include the attribution** in any dataset you publish or distribute. - **Maintain the CC BY-SA** license on the Q\&A content when sharing your output dataset. (Your model, RAG application, or analysis doesn't have to be CC BY-SA — only the underlying Q\&A text does.) - **Do not use `nofollow`** or obfuscate links to the original questions. #### Stack Overflow & Stack Exchange Terms - Commercial use via the API is allowed per the [Stack Exchange API Terms of Service](https://api.stackexchange.com/docs) and [Attribution Required blog post](https://stackoverflow.blog/2009/06/25/attribution-required/). - The Actor respects API rate limits and quota: ~300 requests/day per IP without an API key, ~10,000/day with a free key (register at [stackapps.com](https://stackapps.com)). - See the [licensing discussion](#open-questions--decisions) in the PRD for full details. *** ### Getting Started #### 1. **Configure Your Input** Choose your data source and filters: | Parameter | Purpose | Example | |-----------|---------|---------| | `site` | Stack Exchange site to query | `"stackoverflow"`, `"serverfault"` | | `tags` | Filter by tags (AND) | `["python", "pandas"]` | | `query` | Free-text search | `"memory leak"` | | `minQuestionScore` | Minimum question score | `5` | | `minAnswerScore` | Minimum answer score | `10` | | `acceptedOnly` | Only paired with accepted answers | `true` | | `incremental` | Store high-water-mark for delta runs | `true` | | `enableChunking` | Split answers into RAG chunks | `false` (set `true` if embedding) | | `chunkSize` | Target chunk size (chars) | `1200` | | `maxItems` | Hard limit on results | `0` (no limit) | | `apiKey` | Optional Stack Exchange app key | — | #### 2. **Run the Actor** Use the Apify Console or CLI: ```bash apify run ``` Or via the Apify platform: click **"Start actor"** in the store listing. #### 3. **Retrieve Results** The Actor pushes Q\&A pairs to the Apify Dataset. Download as JSON, CSV, or access via API: ```bash apify dataset get-items ``` #### 4. **Use in Your Application** ##### RAG Example (pseudo-code) ```python ## Load the dataset qapairs = load_dataset('apify_results.json') ## Embed and index for retrieval for pair in qapairs: ## Chunk the answer if needed chunks = pair.get('chunks') or [pair['answer']['bodyMarkdown']] for chunk in chunks: embedding = embedding_model.encode(chunk) index.add(embedding, metadata={'pair_id': pair['questionId']}) ## At query time user_query = "How to optimize pandas merge?" query_embedding = embedding_model.encode(user_query) retrieved = index.search(query_embedding, top_k=5) ## Pass retrieved to LLM for RAG ``` *** ### FAQ #### Q: How much does it cost? **A:** $0.005 per new or updated Q\&A pair. With incremental mode enabled (default), subsequent runs only charge for new pairs — a dataset of 10,000 pairs costs ~$50 to build once, then additional updates cost only for the new pairs added. #### Q: Can I use this data commercially? **A:** Yes. The Stack Exchange content is CC BY-SA licensed; you may use it commercially in closed or open applications, provided you include attribution (which the Actor does automatically for each record). #### Q: What's the incremental mode? **A:** The Actor stores the latest activity date from each run. Next run fetches only Q\&A pairs updated after that date, deduplicates them, and charges only for new/changed pairs. Grow your dataset without re-processing. #### Q: Can I use multiple Stack Exchange sites in one run? **A:** Not yet — one site per run. Use multiple Actor runs with different `site` parameters to multi-source (or contact support for multi-site feature request). #### Q: How often does the Stack Exchange API update? **A:** Continuously. Questions and answers are updated in real-time; the Actor can refresh your dataset as often as you want (respecting the rate-limit quota). #### Q: Do code blocks stay intact? **A:** Yes. The Actor fetches `body_markdown` (not HTML), preserving fenced code blocks (` ```python ``` `) exactly as Stack Overflow displays them. HTML entities are decoded for readability. #### Q: What if there's no accepted answer? **A:** If `acceptedOnly=false`, the Actor pairs the question with its highest-scoring answer instead. If `acceptedOnly=true` (default), questions without accepted answers are skipped. #### Q: Can I chunk the Q\&A text for embeddings? **A:** Yes. Enable `enableChunking: true` and set `chunkSize` (default 1200 chars). The Actor splits text on paragraph and code-block boundaries, never mid-block. Each record gets a `chunks` array ready to embed. #### Q: What if the Stack Exchange API breaks? **A:** The Actor includes a canary check (validates against a known-good answer) and schema-drift detection. If something changes, you'll see a `[Canary] FAILED` warning in logs — not a silent failure. *** ### Technical Details - **Language:** Node.js 20+ (TypeScript) - **HTTP client:** `got-scraping` with automatic gzip decompression, retries, and quota awareness - **Tests:** 138+ unit + integration tests via Vitest; all fixtures use real Stack Exchange API responses - **Error handling:** Graceful quota exhaustion, schema-drift warnings, per-record isolation - **Build:** Multi-stage Docker (builder installs all deps, runtime installs prod deps only) *** ### Support & Issues - **Stack Exchange API docs:** https://api.stackexchange.com/docs - **Register a free API key:** https://stackapps.com/apps/oauth/register - **Attribution requirements:** https://stackoverflow.blog/2009/06/25/attribution-required/ - **CC BY-SA 4.0 license:** https://creativecommons.org/licenses/by-sa/4.0/ For Actor-specific issues or feature requests, open an issue on the project GitHub or contact the maintainer. *** ### Summary Use **Q\&A Knowledge Extractor** to build better RAG systems, fine-tune smarter models, and empower your AI agents with real, production-proven solutions. Clean data, transparent pricing, automatic licensing — from Stack Overflow to your application in minutes. **Start extracting now.** 🚀 # Actor input Schema ## `site` (type: `string`): The Stack Exchange network site to query. Use the site's subdomain (e.g. 'stackoverflow', 'serverfault', 'superuser', 'askubuntu'). Full list at stackexchange.com/sites. ## `tags` (type: `array`): Only return Q\&A pairs tagged with ALL of the specified tags. Leave empty to skip tag filtering. ## `query` (type: `string`): Free-text search term (passed as 'q' to /search/advanced). Can be combined with tags. ## `acceptedOnly` (type: `boolean`): When enabled, only questions with an accepted answer are returned. When disabled, questions without an accepted answer get their highest-scoring answer instead. ## `minQuestionScore` (type: `integer`): Only return Q\&A pairs where the question has at least this score (upvotes minus downvotes). Useful for filtering out low-quality content. ## `minAnswerScore` (type: `integer`): Only return Q\&A pairs where the linked answer has at least this score. Applied after fetching; answers below this threshold are excluded. ## `sort` (type: `string`): Sort questions by votes (highest-scoring first), activity (most recently active first), or creation (newest first). Note: incremental mode always overrides this to 'activity'. ## `fromDate` (type: `string`): Only return Q\&A pairs with activity on or after this date (ISO 8601, e.g. '2024-01-01'). Ignored when 'incremental' is enabled — the stored high-water-mark is used instead. ## `incremental` (type: `boolean`): When enabled, each run stores a high-water-mark (the latest activity date seen). Subsequent runs only fetch Q\&A pairs updated after that mark, so you only pay for new or changed content. ## `enableChunking` (type: `boolean`): When enabled, the combined Q\&A text is split into overlapping chunks on natural boundaries (paragraphs, code blocks). Each record gets a 'chunks' array ready to embed. Code blocks are never split mid-block. ## `chunkSize` (type: `integer`): Target chunk size in characters when RAG chunking is enabled. Actual chunks may be slightly larger to avoid splitting code blocks. ## `chunkOverlap` (type: `integer`): Number of characters to overlap between consecutive chunks when RAG chunking is enabled. Helps preserve context across chunk boundaries. ## `maxItems` (type: `integer`): Hard limit on the number of Q\&A pairs returned per run. Set to 0 for no limit. Use this to control costs on large queries. ## `apiKey` (type: `string`): Optional Stack Exchange application key. Without a key the daily quota is ~300 requests/IP; with a free key it rises to ~10,000/day. Register at stackapps.com. ## `proxyConfiguration` (type: `object`): Apify proxy settings. Datacenter proxies are sufficient for the Stack Exchange API. ## Actor input object example ```json { "site": "stackoverflow", "tags": [ "python", "pandas" ], "query": "how to merge two dataframes", "acceptedOnly": true, "minQuestionScore": 5, "minAnswerScore": 1, "sort": "votes", "fromDate": "2024-01-01", "incremental": true, "enableChunking": false, "chunkSize": 1200, "chunkOverlap": 100, "maxItems": 100, "apiKey": "your_app_key_here", "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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("datahoeven/qa-knowledge-extractor").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 = {} # Run the Actor and wait for it to finish run = client.actor("datahoeven/qa-knowledge-extractor").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 '{}' | apify call datahoeven/qa-knowledge-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datahoeven/qa-knowledge-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Q&A Knowledge Extractor (Stack Exchange)", "description": "Extracts RAG-ready Q&A pairs from the Stack Exchange network via the official API. Returns coupled question+answer records with full attribution, license metadata, and incremental diff support for growing datasets.", "version": "0.1", "x-build-id": "qjJYchNvXCakUinM5" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datahoeven~qa-knowledge-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datahoeven-qa-knowledge-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datahoeven~qa-knowledge-extractor/runs": { "post": { "operationId": "runs-sync-datahoeven-qa-knowledge-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datahoeven~qa-knowledge-extractor/run-sync": { "post": { "operationId": "run-sync-datahoeven-qa-knowledge-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "site": { "title": "Stack Exchange site", "type": "string", "description": "The Stack Exchange network site to query. Use the site's subdomain (e.g. 'stackoverflow', 'serverfault', 'superuser', 'askubuntu'). Full list at stackexchange.com/sites.", "default": "stackoverflow" }, "tags": { "title": "Tags (AND filter)", "type": "array", "description": "Only return Q&A pairs tagged with ALL of the specified tags. Leave empty to skip tag filtering.", "default": [], "items": { "type": "string" } }, "query": { "title": "Search query", "type": "string", "description": "Free-text search term (passed as 'q' to /search/advanced). Can be combined with tags.", "default": "" }, "acceptedOnly": { "title": "Accepted answers only", "type": "boolean", "description": "When enabled, only questions with an accepted answer are returned. When disabled, questions without an accepted answer get their highest-scoring answer instead.", "default": true }, "minQuestionScore": { "title": "Minimum question score", "minimum": 0, "type": "integer", "description": "Only return Q&A pairs where the question has at least this score (upvotes minus downvotes). Useful for filtering out low-quality content.", "default": 0 }, "minAnswerScore": { "title": "Minimum answer score", "minimum": 0, "type": "integer", "description": "Only return Q&A pairs where the linked answer has at least this score. Applied after fetching; answers below this threshold are excluded.", "default": 0 }, "sort": { "title": "Sort order", "enum": [ "votes", "activity", "creation" ], "type": "string", "description": "Sort questions by votes (highest-scoring first), activity (most recently active first), or creation (newest first). Note: incremental mode always overrides this to 'activity'.", "default": "votes" }, "fromDate": { "title": "From date (ISO 8601)", "pattern": "^(\\d{4}-\\d{2}-\\d{2})?$", "type": "string", "description": "Only return Q&A pairs with activity on or after this date (ISO 8601, e.g. '2024-01-01'). Ignored when 'incremental' is enabled — the stored high-water-mark is used instead.", "default": "" }, "incremental": { "title": "Incremental mode", "type": "boolean", "description": "When enabled, each run stores a high-water-mark (the latest activity date seen). Subsequent runs only fetch Q&A pairs updated after that mark, so you only pay for new or changed content.", "default": true }, "enableChunking": { "title": "Enable RAG chunking", "type": "boolean", "description": "When enabled, the combined Q&A text is split into overlapping chunks on natural boundaries (paragraphs, code blocks). Each record gets a 'chunks' array ready to embed. Code blocks are never split mid-block.", "default": false }, "chunkSize": { "title": "Chunk size (characters)", "minimum": 100, "maximum": 8000, "type": "integer", "description": "Target chunk size in characters when RAG chunking is enabled. Actual chunks may be slightly larger to avoid splitting code blocks.", "default": 1200 }, "chunkOverlap": { "title": "Chunk overlap (characters)", "minimum": 0, "maximum": 1000, "type": "integer", "description": "Number of characters to overlap between consecutive chunks when RAG chunking is enabled. Helps preserve context across chunk boundaries.", "default": 100 }, "maxItems": { "title": "Max Q&A pairs", "minimum": 0, "type": "integer", "description": "Hard limit on the number of Q&A pairs returned per run. Set to 0 for no limit. Use this to control costs on large queries.", "default": 0 }, "apiKey": { "title": "Stack Exchange API key", "type": "string", "description": "Optional Stack Exchange application key. Without a key the daily quota is ~300 requests/IP; with a free key it rises to ~10,000/day. Register at stackapps.com." }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify proxy settings. Datacenter proxies are sufficient for the Stack Exchange API.", "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 } } } } } } } } } } ```