This article provides a complete, practical walkthrough of the Extractfromfile Manual Process Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Comparing Claude 3.5 Sonnet and Gemini 2.0 Flash for PDF Data Extraction Using n8n
   
   Meta Description:  
   Learn how to use n8n to extract and process data from PDF files using Claude 3.5 Sonnet and Gemini 2.0 Flash in a single automated workflow. Compare the outputs of both LLMs and streamline your document processing.
   
   Keywords:  
   n8n workflow, Claude 3.5 Sonnet, Gemini 2.0 Flash, PDF data extraction, AI document processing, Google Drive API, Anthropic AI, Google Gemini, OCR alternative, automation
   
   Third-party APIs Used:
   
   1. Google Drive API (for file retrieval and download)
   2. Google Gemini API (PaLM/Gemini 2.0 Flash model)
   3. Anthropic Claude API (Claude 3.5 Sonnet model)
   
   Article:
   
   Comparing Claude 3.5 Sonnet and Gemini 2.0 Flash for PDF Data Extraction Using n8n
   
   Natural Language Processing models have revolutionized the way organizations extract data from documents. Tools like ChatGPT, Claude, and Gemini offer impressive capabilities, but how do you decide which works best for your specific use case? With this goal in mind, a powerful n8n workflow has been created to not only automate PDF data extraction but also give users the ability to compare the performance of Claude 3.5 Sonnet (by Anthropic) and Gemini 2.0 Flash (by Google) side-by-side.
   
   Let’s explore how this workflow functions and why it’s a game-changer for modern document processing.
   
   The Problem: Extracting Data from PDFs in a Smart Way
   
   Traditionally, extracting structured data from PDF files involves multiple steps. You might use Optical Character Recognition (OCR) to interpret the text and then pass the output to an LLM for further analysis. This results in inefficiencies and increased processing time.
   
   This new approach streamlines that by sending the PDF file directly (in base64 format) to LLMs that have built-in multimodal or document-capable capabilities. This not only simplifies the pipeline but also improves accuracy and end-to-end automation.
   
   Workflow Overview
   
   The n8n workflow in question focuses on extracting information—specifically VAT numbers for each country—from a PDF invoice stored on Google Drive. Here's how the workflow operates:
   
   Step 1: Manual Trigger
   
   The workflow starts with a “Manual Trigger” node that allows the user to test or manually run the workflow at any time. This is ideal during development or when testing new prompts or documents.
   
   Step 2: Define Prompt
   
   The next step is a “Set” node named Define Prompt. This prompt is what gets passed along to both Claude and Gemini. In this example, the prompt is:
   
   "Extract the VAT numbers for each country"
   
   However, the user can easily modify this to ask for a summary, list of items, totals, dates, or any structured information relevant to their needs.
   
   Step 3: Load the PDF from Google Drive
   
   Using the Google Drive API, the workflow locates and downloads the target PDF file. The file is identified by its unique file ID and retrieved in binary format.
   
   Step 4: Convert PDF to Base64
   
   Next, the binary file is converted into a base64 string using the "Extract from File" node. This format is compatible with both Claude and Gemini’s PDF processing capabilities, allowing the file to be sent directly to their APIs without a separate OCR process.
   
   Step 5: Call Gemini 2.0 Flash
   
   The Google Gemini 2.0 Flash model is called via an HTTP Request node using the Google Gemini API. The base64 PDF data is embedded in the request alongside the user-defined prompt. With options for structured output included, the response can even be formatted as JSON for easier post-processing.
   
   Helpful tip: The documentation suggests setting a generationConfig to get structured responses from Gemini. This makes it easier to parse outputs programmatically.
   
   Step 6: Call Claude 3.5 Sonnet
   
   In parallel, the same input (base64 PDF + prompt) is sent to Claude 3.5 Sonnet via the Anthropic API. Like Gemini, Claude understands how to interpret PDF documents natively, thanks to its enhanced multimodal input support.
   
   You can also force Claude to return structured JSON by predefining response formats using Anthropic's recommended guardrail techniques.
   
   Why This Workflow Matters
   
   Here are some advantages of this setup:
   
   1. One-Step Document Processing: Skip OCR and go from raw PDF to structured data in a single HTTP request.
   2. Model Comparison: Directly compare Claude and Gemini’s outputs in speed, accuracy, and cost so you can choose the right tool for your organization.
   3. Custom Prompts: Tailor the workflow to almost any document processing task, from extracting invoice items to reading legal documents or summarizing reports.
   4. Modular & Reusable: You can deactivate either API and use the workflow with only Claude or only Gemini depending on your needs or API usage tiers.
   5. Scalable: This workflow can easily be expanded to handle multiple files, incorporate logic for parsing or validation, or trigger from external systems.
   
   Getting Started: Prerequisites
   
   To use this workflow, you’ll need:
   
   - A Google Drive account connected to n8n
   - A valid Claude API Key from Anthropic (get one from console.anthropic.com)
   - A Gemini API Key from Google (available at aistudio.google.com)
   - A PDF document uploaded to your Google Drive (ensure permissions are set, or the file is publicly accessible)
   
   Final Thoughts
   
   By intelligently leveraging the power of LLMs like Claude 3.5 Sonnet and Gemini 2.0 Flash, this workflow demonstrates how document automation can be drastically simplified—and more powerful. Whether you're an AI enthusiast, a business automator, or a developer in the LLM space, this workflow provides a practical, scalable solution to get the most out of multilanguage model APIs for document intelligence.
   
   With little configuration, a customizable prompt, and built-in comparability, it's never been easier to test, iterate, and deploy intelligent PDF data extraction pipelines.
   
   Want to adapt it further? Add error-handling paths, send results via Slack or Email, or automatically store extracted data in your favorite database. The possibilities with n8n + LLMs are endless.
   
   Give it a try! 🧠📄✨

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.