# Web to LLM Spanish — RAG-ready content extractor (`fanciful_geode/web-to-llm-es`) Actor Crawls Spanish-language websites and extracts clean markdown content optimized for LLM RAG pipelines. Handles Spanish character encoding, removes boilerplate, and outputs structured chunks ready for embedding. - **URL**: https://apify.com/fanciful\_geode/web-to-llm-es.md - **Developed by:** [Ernesto de Armas](https://apify.com/fanciful_geode) (community) - **Categories:** Agents, AI, Automation - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.00025 / 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 ## Web to LLM Spanish 🇪🇸 Convierte sitios web en español en contenido limpio listo para **RAG pipelines, LLM training y fine-tuning**. ### 🚀 Características - ✅ Crawlea sitios web en español con manejo correcto de encoding - ✅ Extrae contenido limpio (markdown, texto plano o HTML) - ✅ Elimina automáticamente navegación, ads, footers y boilerplate - ✅ Divide contenido en chunks optimizados para RAG - ✅ Detecta idioma automáticamente - ✅ Incluye metadatos (título, descripción, URL fuente) ### 📋 Input | Campo | Tipo | Default | Descripción | |-------|------|---------|-------------| | startUrls | Array | — | URLs semilla para comenzar el crawl | | maxPages | Integer | 100 | Máximo de páginas a crawlear | | outputFormat | String | "markdown" | Formato: markdown, text, o html | | chunkSize | Integer | 2000 | Tamaño de chunk en caracteres (0 = sin split) | | chunkOverlap | Integer | 200 | Superposición entre chunks | | removeBoilerplate | Boolean | true | Eliminar navegación y ads | | includeMetadata | Boolean | true | Incluir metadatos en output | ### 📊 Output Cada registro incluye: - `url` — URL fuente - `title` — Título de la página - `content` — Contenido extraído en el formato elegido - `language` — Idioma detectado - `chunkIndex` / `totalChunks` — Posición del chunk - `charCount` / `wordCount` — Estadísticas - `crawledAt` — Timestamp ISO ### 💰 Pricing **Pay-Per-Event (PPE):** $0.25 por cada 1,000 páginas procesadas. Sin costo adicional por cómputo. ### 🎯 Casos de uso - Alimentar pipelines RAG con contenido en español - Preparar datasets para fine-tuning de LLMs - Monitorear contenido de sitios web hispanos - Investigación académica y de mercado en LatAm --- *Creado con Hermes Agent 🤖* # Actor input Schema ## `startUrls` (type: `array`): List of URLs to crawl. Can be single pages or sitemaps. ## `maxPages` (type: `integer`): Maximum number of pages to crawl (0 = unlimited, max 10000) ## `outputFormat` (type: `string`): Format of the extracted content ## `chunkSize` (type: `integer`): Split content into chunks of this size for RAG ingestion. 0 = no splitting. ## `chunkOverlap` (type: `integer`): Overlap between consecutive chunks to maintain context ## `removeBoilerplate` (type: `boolean`): Remove navigation, ads, footers, and other non-content elements ## `includeMetadata` (type: `boolean`): Include page title, description, and URL in output ## Actor input object example ```json { "startUrls": [ { "url": "https://ejemplo.com" } ], "maxPages": 100, "outputFormat": "markdown", "chunkSize": 2000, "chunkOverlap": 200, "removeBoilerplate": true, "includeMetadata": 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 = { "startUrls": [ { "url": "https://ejemplo.com" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("fanciful_geode/web-to-llm-es").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://ejemplo.com" }] } # Run the Actor and wait for it to finish run = client.actor("fanciful_geode/web-to-llm-es").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://ejemplo.com" } ] }' | apify call fanciful_geode/web-to-llm-es --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fanciful_geode/web-to-llm-es", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Web to LLM Spanish — RAG-ready content extractor", "description": "Crawls Spanish-language websites and extracts clean markdown content optimized for LLM RAG pipelines. Handles Spanish character encoding, removes boilerplate, and outputs structured chunks ready for embedding.", "version": "0.1", "x-build-id": "vHWAeBkrAwY60Fhrf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fanciful_geode~web-to-llm-es/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fanciful_geode-web-to-llm-es", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/fanciful_geode~web-to-llm-es/runs": { "post": { "operationId": "runs-sync-fanciful_geode-web-to-llm-es", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/fanciful_geode~web-to-llm-es/run-sync": { "post": { "operationId": "run-sync-fanciful_geode-web-to-llm-es", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "List of URLs to crawl. Can be single pages or sitemaps.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxPages": { "title": "Max pages to crawl", "maximum": 10000, "type": "integer", "description": "Maximum number of pages to crawl (0 = unlimited, max 10000)", "default": 100 }, "outputFormat": { "title": "Output format", "enum": [ "markdown", "text", "html" ], "type": "string", "description": "Format of the extracted content", "default": "markdown" }, "chunkSize": { "title": "Chunk size (characters)", "maximum": 10000, "type": "integer", "description": "Split content into chunks of this size for RAG ingestion. 0 = no splitting.", "default": 2000 }, "chunkOverlap": { "title": "Chunk overlap (characters)", "maximum": 1000, "type": "integer", "description": "Overlap between consecutive chunks to maintain context", "default": 200 }, "removeBoilerplate": { "title": "Remove boilerplate", "type": "boolean", "description": "Remove navigation, ads, footers, and other non-content elements", "default": true }, "includeMetadata": { "title": "Include metadata", "type": "boolean", "description": "Include page title, description, and URL in output", "default": 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 } } } } } } } } } } ```