# URL List to RAG & Vector JSONL (`orbiscribe/url-list-to-vector-jsonl`) Actor Paste a curated URL list and get clean Markdown, document JSONL, vector chunks, ingest manifest, and failed URL report. - **URL**: https://apify.com/orbiscribe/url-list-to-vector-jsonl.md - **Developed by:** [Orbiscribe Labs](https://apify.com/orbiscribe) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $1.00 / 1,000 url converted to vector jsonls 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 ## URL List to RAG & Vector JSONL Use this Actor when you already know the URLs you want to ingest and need a controlled conversion step for a vector database or RAG pipeline. It fetches each public URL, extracts readable content, creates stable chunks, and writes JSONL artifacts plus a manifest so failed URLs are easy to inspect. It does not crawl around the site unless you explicitly provide more URLs. ### What you get - Dataset rows for document records and chunks. - Clean Markdown, main text, headings, links, canonical URL, content hash, and output preset metadata. - Key-value outputs: `RAG_CHUNKS_JSONL`, `VECTOR_CHUNKS_JSONL`, `DOCUMENTS_JSONL`, `INGEST_MANIFEST`, `FAILED_URLS`, `MARKDOWN_BUNDLE`, `BUYER_BRIEF`, and `RUN_SUMMARY`. ### Common workflows - Convert a curated URL export into vector-store JSONL. - Reprocess a known page list without a crawler wandering through the site. - Send failed URLs to a cleanup queue. - Keep document and chunk records side by side for debugging retrieval. ### Input Provide `urls` and choose an `outputPreset` such as `openai_vector_store`, `langchain`, `llamaindex`, `pinecone`, `qdrant`, or `generic_jsonl`. The preset is included in chunk metadata so downstream jobs can route or transform the JSONL. Use `includeUrlPatterns`, `excludeUrlPatterns`, `maxUrls`, `chunkSizeChars`, and `chunkOverlapChars` to control scope, cost, and chunk shape. The default run processes three public Apify docs URLs so a first Store run produces real Markdown and JSONL without extra setup. ```json { "urls": [ { "url": "https://docs.apify.com/academy/getting-started" }, { "url": "https://docs.apify.com/academy/web-scraping-for-beginners" }, { "url": "https://docs.apify.com/academy/actor-marketing-playbook/actor-basics/actor-description" } ], "outputPreset": "openai_vector_store", "includeUrlPatterns": ["/academy/"], "excludeUrlPatterns": [], "maxUrls": 3, "chunkSizeChars": 2500, "chunkOverlapChars": 250, "dryRun": false } ```` ### Pricing Recommended monetization: Pay per Event at **$0.001** per `vector-jsonl-url`. When pay-per-event pricing is enabled, dry runs are uncharged and free-plan callers get the first 25 processed sources without this Actor's custom event charge. Users should still set Apify spending limits before large batches. ### Limits and compliance Public URLs only. This Actor does not bypass logins, paywalls, robots policies, or access controls. It is intentionally a URL-list converter, not a broad crawler. # Actor input Schema ## `urls` (type: `array`): Public URLs to convert into Markdown, document JSONL, and vector-ready chunks. The default is a small live Apify docs sample so Run works immediately. ## `outputPreset` (type: `string`): Naming and metadata preset for downstream ingestion. ## `includeUrlPatterns` (type: `array`): Only process URLs containing at least one of these strings. Leave empty to allow every URL in the list. ## `excludeUrlPatterns` (type: `array`): Skip URLs containing any of these strings. ## `maxUrls` (type: `integer`): Maximum number of URLs from the input list to process in one run. ## `includeMarkdown` (type: `boolean`): Include clean Markdown content in page and document records. ## `chunkSizeChars` (type: `integer`): Target character length for generated text chunks. ## `chunkOverlapChars` (type: `integer`): Character overlap between adjacent chunks. ## `requestTimeoutMs` (type: `integer`): Per-request timeout in milliseconds. ## `dryRun` (type: `boolean`): Emit bundled demo URL records without fetching or charging events. Leave off for the live default sample. ## Actor input object example ```json { "urls": [ { "url": "https://docs.apify.com/academy/getting-started" }, { "url": "https://docs.apify.com/academy/web-scraping-for-beginners" }, { "url": "https://docs.apify.com/academy/actor-marketing-playbook/actor-basics/actor-description" } ], "outputPreset": "openai_vector_store", "includeUrlPatterns": [ "/academy/" ], "excludeUrlPatterns": [], "maxUrls": 3, "includeMarkdown": true, "chunkSizeChars": 2500, "chunkOverlapChars": 250, "requestTimeoutMs": 15000, "dryRun": false } ``` # Actor output Schema ## `results` (type: `string`): No description ## `ragChunksJsonl` (type: `string`): No description ## `vectorChunksJsonl` (type: `string`): No description ## `documentsJsonl` (type: `string`): No description ## `ingestManifest` (type: `string`): No description ## `failedUrls` (type: `string`): No description ## `markdownBundle` (type: `string`): No description ## `buyerBrief` (type: `string`): No description ## `runSummary` (type: `string`): No description # 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("orbiscribe/url-list-to-vector-jsonl").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("orbiscribe/url-list-to-vector-jsonl").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 orbiscribe/url-list-to-vector-jsonl --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=orbiscribe/url-list-to-vector-jsonl", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "URL List to RAG & Vector JSONL", "description": "Paste a curated URL list and get clean Markdown, document JSONL, vector chunks, ingest manifest, and failed URL report.", "version": "0.1", "x-build-id": "yJsxRdarruno5M8gw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/orbiscribe~url-list-to-vector-jsonl/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-orbiscribe-url-list-to-vector-jsonl", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/orbiscribe~url-list-to-vector-jsonl/runs": { "post": { "operationId": "runs-sync-orbiscribe-url-list-to-vector-jsonl", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/orbiscribe~url-list-to-vector-jsonl/run-sync": { "post": { "operationId": "run-sync-orbiscribe-url-list-to-vector-jsonl", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "urls": { "title": "URLs", "type": "array", "description": "Public URLs to convert into Markdown, document JSONL, and vector-ready chunks. The default is a small live Apify docs sample so Run works immediately.", "default": [ { "url": "https://docs.apify.com/academy/getting-started" }, { "url": "https://docs.apify.com/academy/web-scraping-for-beginners" }, { "url": "https://docs.apify.com/academy/actor-marketing-playbook/actor-basics/actor-description" } ], "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "outputPreset": { "title": "Output preset", "enum": [ "openai_vector_store", "langchain", "llamaindex", "pinecone", "qdrant", "generic_jsonl" ], "type": "string", "description": "Naming and metadata preset for downstream ingestion.", "default": "openai_vector_store" }, "includeUrlPatterns": { "title": "Include URL patterns", "type": "array", "description": "Only process URLs containing at least one of these strings. Leave empty to allow every URL in the list.", "items": { "type": "string" }, "default": [ "/academy/" ] }, "excludeUrlPatterns": { "title": "Exclude URL patterns", "type": "array", "description": "Skip URLs containing any of these strings.", "items": { "type": "string" }, "default": [] }, "maxUrls": { "title": "Max URLs", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of URLs from the input list to process in one run.", "default": 3 }, "includeMarkdown": { "title": "Include markdown", "type": "boolean", "description": "Include clean Markdown content in page and document records.", "default": true }, "chunkSizeChars": { "title": "Chunk size characters", "minimum": 500, "maximum": 12000, "type": "integer", "description": "Target character length for generated text chunks.", "default": 2500 }, "chunkOverlapChars": { "title": "Chunk overlap characters", "minimum": 0, "maximum": 2000, "type": "integer", "description": "Character overlap between adjacent chunks.", "default": 250 }, "requestTimeoutMs": { "title": "Request timeout ms", "minimum": 1000, "maximum": 60000, "type": "integer", "description": "Per-request timeout in milliseconds.", "default": 15000 }, "dryRun": { "title": "Dry run", "type": "boolean", "description": "Emit bundled demo URL records without fetching or charging events. Leave off for the live default sample.", "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 } } } } } } } } } } ```