# Klingai Category Extractor (`getdataforme/klingai-category-extractor`) Actor Automate scraping of category details from Kling.ai with the Apify Actor. Extract structured data on effects categories, tags, and media efficiently. Customize URL inputs and item limits to suit your needs. Ideal for market research, competitive analysis, content aggregation, and more.... - **URL**: https://apify.com/getdataforme/klingai-category-extractor.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results 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 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 --- ## README.md ### Klingai Category Extractor #### Introduction The Klingai Category Extractor is a powerful tool designed to scrape and extract detailed information about various categories from the Kling.ai platform. This Apify Actor automates data collection, providing high-quality insights into effects categories, tags, and associated media. #### Features - **Automated Data Extraction**: Seamlessly scrapes category details from specified URLs. - **High-Quality Data Output**: Ensures reliable and structured data for analysis. - **Customizable Limits**: Set item limits to control the volume of data extracted per run. - **Flexible URL Configuration**: Supports multiple URLs for comprehensive scraping. - **Efficient Performance**: Optimized for speed and accuracy in data retrieval. #### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|----------------------------------------------| | `Urls` | array | No | The URLs for the spider to scrape. Must be valid HTTP/HTTPS links. | `["https://kling.ai/document-api/quickStart/productIntroduction/effectsCenter"]` | | `item_limit`| integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | #### Example Usage ##### Input JSON ```json { "Urls": [ "https://kling.ai/document-api/quickStart/productIntroduction/effectsCenter" ], "item_limit": 10 } ```` ##### Output JSON ```json [ { "name": "Shrug Dance", "key": "shrug_dance", "category": "Soccer Fever", "tag": "red:Soccer Fever", "cover_image_url": "https://s15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_cover/shrug_dance_cover.png", "video_url": "https://s15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_video/shrug_dance.mp4", "resolution": "720p", "duration": "8s", "aspect_ratio": "same as the input image", "units": "4", "effect_scene": "shrug_dance", "sample_image_urls": [ "https://s15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_pic/shrug_dance.png" ], "api_example_code": "curl --location 'https://api-singapore.klingai.com/v1/videos/effects' \\\r\n--header 'Authorization: Bearer {please replace with your token}' \\\r\n--header 'Content-Type: application/json' \\\r\n--data '{\r\n \"effect_scene\": \"shrug_dance\",\r\n \"input\": {\r\n \"image\": \"https://s15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_pic/shrug_dance.png\"\r\n }\r\n}'", "actor_id": "MvuaBtI7di9rjnlVp", "run_id": "jvvkE8Wl3UUPnUTo6" }, { "name": "Laicai Dance", "key": "laicai_dance", "category": "AI Dance", "tag": "", "cover_image_url": "https://v15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_cover/laicai_dance.png", "video_url": "https://v15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_video/laicai_dance_2.mp4", "resolution": "720p", "duration": "13s", "aspect_ratio": "same as the input image", "units": "6.5", "effect_scene": "laicai_dance", "sample_image_urls": [ "https://v15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_raw_pic/laicai_dance.png" ], "api_example_code": "curl --location 'https://api-singapore.klingai.com/v1/videos/effects' \\\r\n--header 'Authorization: Bearer {please replace with your token}' \\\r\n--header 'Content-Type: application/json' \\\r\n--data '{\r\n \"effect_scene\": \"laicai_dance\",\r\n \"input\": {\r\n \"image\": \"https://v15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_raw_pic/laicai_dance.png\"\r\n }\r\n}'", "actor_id": "MvuaBtI7di9rjnlVp", "run_id": "jvvkE8Wl3UUPnUTo6" }, { "name": "Bra Hot Dance", "key": "bra_hot_dance", "category": "Soccer Fever", "tag": "red:Soccer Fever", "cover_image_url": "https://s15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_cover/bra_hot_dance_cover.png", "video_url": "https://s15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_video/bra_hot_dance.jpg.mp4", "resolution": "720p", "duration": "8s", "aspect_ratio": "9:16", "units": "4", "effect_scene": "bra_hot_dance", "sample_image_urls": [ "https://s15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_pic/bra_hot_dance.jpg" ], "api_example_code": "curl --location 'https://api-singapore.klingai.com/v1/videos/effects' \\\r\n--header 'Authorization: Bearer {please replace with your token}' \\\r\n--header 'Content-Type: application/json' \\\r\n--data '{\r\n \"effect_scene\": \"bra_hot_dance\",\r\n \"input\": {\r\n \"image\": \"https://s15-kling.klingai.com/kos/s101/nlav112372/kling-op/effects_pic/bra_hot_dance.jpg\"\r\n }\r\n}'", "actor_id": "MvuaBtI7di9rjnlVp", "run_id": "jvvkE8Wl3UUPnUTo6" } ] ``` #### Use Cases - **Market Research and Analysis**: Gain insights into trending categories and effects. - **Competitive Intelligence**: Monitor competitors' offerings and strategies. - **Price Monitoring**: Track pricing trends for various effects. - **Content Aggregation**: Compile comprehensive datasets for content platforms. - **Academic Research**: Support studies in digital media and technology. - **Business Automation**: Automate data-driven decision-making processes. #### Installation and Usage 1. Search for "Klingai Category Extractor" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). #### Output Format The output is a JSON array containing objects with fields such as `name`, `key`, `category`, `tag`, `cover_image_url`, `video_url`, `resolution`, `duration`, `aspect_ratio`, `units`, `effect_scene`, `sample_image_urls`, `api_example_code`, `actor_id`, and `run_id`. Each object represents a category with its associated details. #### Error Handling - **Invalid URLs**: Ensure all provided URLs are valid HTTP/HTTPS links. - **Exceeding Item Limit**: If the item limit is set, ensure it does not exceed available data. - **Network Issues**: Handle potential connectivity problems gracefully. #### Rate Limiting and Best Practices - Respect API rate limits to avoid being blocked. - Use appropriate delays between requests if necessary. - Monitor logs for any rate-limiting warnings or errors. #### Support # Actor input Schema ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "item_limit": 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/klingai-category-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("getdataforme/klingai-category-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 getdataforme/klingai-category-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/klingai-category-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Klingai Category Extractor", "description": "Automate scraping of category details from Kling.ai with the Apify Actor. Extract structured data on effects categories, tags, and media efficiently. Customize URL inputs and item limits to suit your needs. Ideal for market research, competitive analysis, content aggregation, and more....", "version": "0.0", "x-build-id": "xO62VtvR94WAVLOTz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~klingai-category-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-klingai-category-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~klingai-category-extractor/runs": { "post": { "operationId": "runs-sync-getdataforme-klingai-category-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~klingai-category-extractor/run-sync": { "post": { "operationId": "run-sync-getdataforme-klingai-category-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "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 } } } } } } } } } } ```