# PDF Parser API (`george.the.developer/pdf-parser-api`) Actor Instant API that parses any PDF from a URL — extracts full text, page count, metadata (title, author, dates), and PDF version. Returns structured JSON. Perfect for document processing pipelines and AI agents. - **URL**: https://apify.com/george.the.developer/pdf-parser-api.md - **Developed by:** [George Kioko](https://apify.com/george.the.developer) (community) - **Categories:** Developer tools - **Stats:** 5 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 pdf-parseds 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 ## PDF Parser API - Extract Text & Metadata from PDF Files A fast, reliable **PDF parser API** that extracts text content, metadata, page count, and word count from any publicly accessible PDF file. Simply provide a PDF URL and get back structured JSON with the full text and document properties -- perfect for **RAG pipelines**, document processing, and AI training data preparation. Built as an always-on Standby API on [Apify](https://apify.com), it responds instantly with no cold starts, no queues, and no SDK required. ### Key Features - **Full text extraction** -- get every word from any PDF, ready for indexing or NLP - **Rich metadata** -- title, author, subject, creator, producer, creation/modification dates - **Page & word counts** -- instant document statistics without downloading the file yourself - **PDF version detection** -- know exactly what PDF spec the document uses - **GET and POST endpoints** -- use query parameters or JSON body, your choice - **CORS enabled** -- call directly from browser-based apps - **Magic-byte validation** -- rejects non-PDF files before wasting parse time - **Password-protected detection** -- returns a clear error instead of crashing - **Streaming size guard** -- enforces the 50 MB limit even when Content-Length is missing ### How It Works ```mermaid flowchart LR A["Client\n(curl / Python / JS)"] -->|HTTP GET or POST\nwith PDF URL| B["PDF Parser API\n(Apify Standby)"] B -->|Download PDF| C["Remote PDF\nServer"] C -->|PDF binary| B B -->|pdf-parse\nprocessing| D["Extracted Data"] D -->|JSON response| A style A fill:#e8f4fd,stroke:#2196F3 style B fill:#fff3e0,stroke:#FF9800 style C fill:#f3e5f5,stroke:#9C27B0 style D fill:#e8f5e9,stroke:#4CAF50 ```` ### Endpoints | Method | Path | Description | |--------|------|-------------| | `GET` | `/parse?url=` | Parse a PDF by passing the URL as a query parameter | | `POST` | `/parse` | Parse a PDF by sending `{"url": ""}` as JSON body | | `GET` | `/health` | Health check -- returns `{"status": "ok"}` | | `GET` | `/` | Service info with usage instructions | ### Input #### GET request Pass the PDF URL as a query parameter: ``` GET /parse?url=https://example.com/document.pdf ``` #### POST request Send a JSON body with the `url` field: ```json { "url": "https://example.com/document.pdf" } ``` ### Output A successful response returns structured JSON: ```json { "success": true, "pages": 12, "text": "Full extracted text content of the PDF document...", "metadata": { "title": "Annual Report 2025", "author": "Jane Smith", "subject": "Financial Summary", "creator": "Microsoft Word", "producer": "macOS Quartz PDFContext", "creationDate": "D:20250115102030Z", "modDate": "D:20250120083000Z" }, "pdfVersion": "1.7", "textLength": 48320, "wordCount": 7841, "processingTimeMs": 342 } ``` #### Error response ```json { "success": false, "error": "PDF is password-protected and cannot be parsed." } ``` ### How to Use #### Using curl (GET) ```bash curl "https://pdf-parser-api.apify.actor/parse?url=https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" ``` #### Using curl (POST) ```bash curl -X POST "https://pdf-parser-api.apify.actor/parse" \ -H "Content-Type: application/json" \ -d '{"url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"}' ``` #### Health check ```bash curl "https://pdf-parser-api.apify.actor/health" ``` ### Integration Examples #### Python ```python import requests response = requests.get( "https://pdf-parser-api.apify.actor/parse", params={"url": "https://example.com/report.pdf"} ) data = response.json() print(f"Pages: {data['pages']}") print(f"Words: {data['wordCount']}") print(f"Title: {data['metadata']['title']}") print(f"Text preview: {data['text'][:500]}") ``` #### Node.js ```javascript const response = await fetch("https://pdf-parser-api.apify.actor/parse", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ url: "https://example.com/report.pdf", }), }); const data = await response.json(); console.log(`Pages: ${data.pages}`); console.log(`Words: ${data.wordCount}`); console.log(`Text preview: ${data.text.slice(0, 500)}`); ``` #### RAG Pipeline (Python + LangChain) ```python import requests from langchain.text_splitter import RecursiveCharacterTextSplitter ## Extract text from PDF resp = requests.get( "https://pdf-parser-api.apify.actor/parse", params={"url": "https://example.com/knowledge-base.pdf"} ) pdf_data = resp.json() ## Chunk for vector store splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) chunks = splitter.split_text(pdf_data["text"]) ## Each chunk is ready for embedding and indexing print(f"Split {pdf_data['wordCount']} words into {len(chunks)} chunks") ``` ### Use Cases - **RAG pipelines** -- extract text from PDFs and chunk it for vector databases (Pinecone, Weaviate, Chroma) - **Document processing** -- batch-process invoices, contracts, and reports into structured data - **AI training data** -- convert PDF corpora into clean text for fine-tuning language models - **Legal & compliance** -- parse regulatory filings, court documents, and compliance reports at scale - **Academic research** -- extract text from research papers for citation analysis or literature reviews - **Content migration** -- pull text from legacy PDF archives into modern CMS platforms - **Search indexing** -- feed PDF content into Elasticsearch, Algolia, or Meilisearch ### Pricing | Event | Cost | |-------|------| | PDF parsed successfully | **$0.004** per PDF | You only pay when a PDF is successfully parsed. Failed requests (invalid URL, timeout, password-protected files) are not charged. ### Limitations | Constraint | Limit | |------------|-------| | Maximum file size | **50 MB** | | Download timeout | **60 seconds** | | Request body size | **1 MB** (for POST requests) | | Scanned PDFs | **No OCR** -- only digitally created PDFs with embedded text are supported | | Password-protected PDFs | **Not supported** -- returns a clear error message | | Protocols | **HTTP and HTTPS only** -- no local file paths or FTP | ### FAQ #### Does this API support scanned PDFs or images inside PDFs? No. This API extracts embedded text from digitally created PDFs. If a PDF was created by scanning paper documents and contains only images, the extracted text will be empty or minimal. For scanned PDFs, you would need an OCR service as a preprocessing step. #### What happens if the PDF is too large or the download times out? The API enforces a 50 MB file size limit and a 60-second download timeout. If either limit is exceeded, you will receive a clear error response with the appropriate HTTP status code (413 for size, 408 for timeout). You are not charged for failed requests. #### Can I parse PDFs that require authentication or are behind a login? The API fetches PDFs from the URL you provide using a standard HTTP request. If the PDF requires cookies, authentication headers, or is behind a login wall, the download will likely fail. The PDF must be publicly accessible or accessible via a direct URL with any required tokens embedded in the query string. #### What metadata fields are extracted? The API extracts seven metadata fields when available: **title**, **author**, **subject**, **creator** (the application that created the document), **producer** (the PDF library used), **creation date**, and **modification date**. Not all PDFs contain all metadata fields -- missing fields are returned as `null`. *** Built by [George The Developer](https://apify.com/george.the.developer) on Apify. # Actor input Schema ## `url` (type: `string`): Direct URL to a PDF file to parse. ## Actor input object example ```json { "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" } ``` # 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 = { "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" }; // Run the Actor and wait for it to finish const run = await client.actor("george.the.developer/pdf-parser-api").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 = { "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" } # Run the Actor and wait for it to finish run = client.actor("george.the.developer/pdf-parser-api").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 '{ "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" }' | apify call george.the.developer/pdf-parser-api --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=george.the.developer/pdf-parser-api", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PDF Parser API", "description": "Instant API that parses any PDF from a URL — extracts full text, page count, metadata (title, author, dates), and PDF version. Returns structured JSON. Perfect for document processing pipelines and AI agents.", "version": "1.0", "x-build-id": "ZevdWt8Q72QWPuvDI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/george.the.developer~pdf-parser-api/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-george.the.developer-pdf-parser-api", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/george.the.developer~pdf-parser-api/runs": { "post": { "operationId": "runs-sync-george.the.developer-pdf-parser-api", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/george.the.developer~pdf-parser-api/run-sync": { "post": { "operationId": "run-sync-george.the.developer-pdf-parser-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "url" ], "properties": { "url": { "title": "PDF URL", "type": "string", "description": "Direct URL to a PDF file to parse." } } }, "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 } } } } } } } } } } ```