# Group Trip Data Extractor (`saadithya/group-trip-data-extractor`) Actor Extract structured trip information from multiple group trip URLs and enrich data using AI - **URL**: https://apify.com/saadithya/group-trip-data-extractor.md - **Developed by:** [Aadhithya](https://apify.com/saadithya) (community) - **Categories:** AI, Agents, Travel - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.05 / result 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 ## Group Trip Data Extractor A production-ready Apify Actor that extracts structured trip information from multiple group trip URLs and enriches data using AI. ### Project Structure ```` AI Group trip extractor/ ├── .actor/ │ ├── actor.json ## Actor configuration │ ├── input\_schema.json ## Input parameter definitions │ ├── output\_schema.json ## Output schema for API/Console │ └── dataset\_schema.json ## Dataset field definitions ├── src/ │ ├── main.js ## Core actor logic │ ├── scraper.js ## Web scraping (Cheerio/Playwright) │ ├── ai-enricher.js ## OpenAI integration │ └── schema.js ## Schema validation utilities ├── Dockerfile ## Docker build configuration ├── package.json ## Dependencies & scripts └── INPUT.json ## Sample input for testing ```` ### Features - **Multi-URL Processing**: Process multiple trip URLs in a single run - **Intelligent Scraping**: Uses Cheerio (fast) or Playwright (JavaScript-heavy pages) - **AI Enrichment**: Uses OpenAI to infer missing data (coordinates, country, city, trip type) - **Strict Schema**: Fixed 17-field output schema for Excel compatibility - **Error Handling**: Never breaks schema - returns empty structured rows on errors - **Test Mode**: Quick testing with mock data ### Output Schema Every output item contains exactly these 17 fields: | Field | Description | |-------|-------------| | `title` | Trip/tour name | | `destination` | Main destination name | | `country` | Country name (AI-inferred if missing) | | `state` | State/province/region (AI-inferred if missing) | | `city` | City name (AI-inferred if missing) | | `latitude` | Latitude coordinate (AI-inferred from destination) | | `longitude` | Longitude coordinate (AI-inferred from destination) | | `provider` | Travel company/organizer name | | `price` | Numeric price value | | `currency` | Currency code (INR, USD, EUR, etc.) | | `start_date` | Start date (YYYY-MM-DD format) | | `end_date` | End date (YYYY-MM-DD format) | | `trip_type` | Trip category (trek, backpacking, weekend, etc.) | | `description` | Brief trip description | | `images` | Comma-separated image URLs | | `inclusions` | What's included (comma-separated) | | `booking_url` | URL to book the trip | ### Input Configuration ```json { "tripUrls": [ "https://example-travel.com/trip/himalayan-trek", "https://example-travel.com/trip/goa-weekend" ], "openaiApiKey": "sk-...", "model": "gpt-4o-mini", "testMode": false, "maxConcurrency": 5, "requestTimeout": 60000, "usePlaywright": false } ```` #### Input Parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `tripUrls` | array | Yes | - | List of trip URLs to extract | | `openaiApiKey` | string | Yes | - | OpenAI API key for enrichment | | `model` | string | No | `gpt-4o-mini` | OpenAI model to use | | `testMode` | boolean | No | `false` | Return mock data without scraping | | `maxConcurrency` | integer | No | `5` | Max concurrent page loads | | `requestTimeout` | integer | No | `60000` | Page load timeout (ms) | | `usePlaywright` | boolean | No | `false` | Use Playwright for JS-heavy pages | ### How It Works #### Step 1: Fetch Data For each URL, the actor loads the page and extracts: - Title, description, destination - Price, dates, duration - Itinerary, inclusions - Provider name, images, booking link #### Step 2: Clean Data - Remove HTML tags - Normalize whitespace - Keep meaningful content only #### Step 3: AI Enrichment Send extracted content to OpenAI to: - Map data to required schema - Infer missing fields (country, state, city, trip\_type) - Generate coordinates from destination - Normalize currency and dates #### Step 4: Output Each dataset item contains all 17 fields with missing values as empty strings. ### Error Handling - **Scraping fails**: Returns empty structured row with booking\_url - **AI fails**: Returns partially mapped data - **Invalid URL**: Returns empty structured row - **Schema never breaks**: All outputs have exactly 17 fields ### Performance - Handles multiple URLs efficiently - Configurable concurrency - Timeout under 5 minutes per URL - Test mode for quick validation ### Local Development ```bash ## Install dependencies npm install ## Run locally npm start ## Or with Apify CLI apify run ``` ### Deployment to Apify ```bash ## Login to Apify apify login ## Push to Apify apify push ``` ### Cost Estimation - **Scraping**: ~$0.01 per URL (Apify compute) - **AI Enrichment**: ~$0.001-0.01 per URL (depends on model) - gpt-4o-mini: Most cost-effective - gpt-4o: Higher quality, higher cost ### License ISC # Actor input Schema ## `tripUrls` (type: `array`): List of group trip URLs to extract data from ## `openaiApiKey` (type: `string`): Your OpenAI API key for AI enrichment of extracted data ## `model` (type: `string`): The OpenAI model to use for data enrichment ## `testMode` (type: `boolean`): When enabled, returns mock data without scraping or AI calls ## `maxConcurrency` (type: `integer`): Maximum number of pages to process simultaneously ## `requestTimeout` (type: `integer`): Maximum time to wait for page load in milliseconds ## `usePlaywright` (type: `boolean`): Use Playwright for JavaScript-heavy pages (slower but more reliable) ## Actor input object example ```json { "tripUrls": [ "https://example-travel.com/trip/himalayan-trek", "https://example-travel.com/trip/goa-backpacking" ], "model": "gpt-4o-mini", "testMode": false, "maxConcurrency": 5, "requestTimeout": 60000, "usePlaywright": false } ``` # Actor output Schema ## `tripData` (type: `string`): Extracted and AI-enriched trip information in structured format with exactly 17 fields: title, destination, country, state, city, latitude, longitude, provider, price, currency, start\_date, end\_date, trip\_type, description, images, inclusions, booking\_url ## `tripDataCSV` (type: `string`): Trip data in CSV format ready for Excel import ## `tripDataExcel` (type: `string`): Trip data in Excel format # 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 = { "tripUrls": [] }; // Run the Actor and wait for it to finish const run = await client.actor("saadithya/group-trip-data-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 = { "tripUrls": [] } # Run the Actor and wait for it to finish run = client.actor("saadithya/group-trip-data-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 '{ "tripUrls": [] }' | apify call saadithya/group-trip-data-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=saadithya/group-trip-data-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Group Trip Data Extractor", "description": "Extract structured trip information from multiple group trip URLs and enrich data using AI", "version": "1.0", "x-build-id": "HFMl5sIPEdas7UTbh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/saadithya~group-trip-data-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-saadithya-group-trip-data-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/saadithya~group-trip-data-extractor/runs": { "post": { "operationId": "runs-sync-saadithya-group-trip-data-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/saadithya~group-trip-data-extractor/run-sync": { "post": { "operationId": "run-sync-saadithya-group-trip-data-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "tripUrls", "openaiApiKey" ], "properties": { "tripUrls": { "title": "Trip URLs", "type": "array", "description": "List of group trip URLs to extract data from", "items": { "type": "string" } }, "openaiApiKey": { "title": "OpenAI API Key", "type": "string", "description": "Your OpenAI API key for AI enrichment of extracted data" }, "model": { "title": "OpenAI Model", "enum": [ "gpt-4o-mini", "gpt-4o", "gpt-4-turbo", "gpt-3.5-turbo" ], "type": "string", "description": "The OpenAI model to use for data enrichment", "default": "gpt-4o-mini" }, "testMode": { "title": "Test Mode", "type": "boolean", "description": "When enabled, returns mock data without scraping or AI calls", "default": false }, "maxConcurrency": { "title": "Max Concurrency", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum number of pages to process simultaneously", "default": 5 }, "requestTimeout": { "title": "Request Timeout (ms)", "minimum": 10000, "maximum": 300000, "type": "integer", "description": "Maximum time to wait for page load in milliseconds", "default": 60000 }, "usePlaywright": { "title": "Use Playwright", "type": "boolean", "description": "Use Playwright for JavaScript-heavy pages (slower but more reliable)", "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 } } } } } } } } } } ```