This article provides a complete, practical walkthrough of the Extractfromfile Manual Automation 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: A No-Code Workflow for Extracting Data from PDFs with n8n
   
   Meta Description:  
   Discover how to effortlessly extract text data from PDF files using Google Drive, Claude 3.5 Sonnet, and Gemini 2.0 Flash in this no-code n8n workflow. Ideal for developers, analysts, and AI enthusiasts comparing LLM capabilities.
   
   Keywords:  
   n8n, Claude 3.5 Sonnet, Gemini 2.0 Flash, PDF Extraction, Google Drive, AI Workflow, Anthropic API, Google PaLM, base64 PDF parsing, no-code AI automation, generative AI, document parsing
   
   Third-Party APIs Used:
   
   - Google Drive API
   - Google Gemini (PaLM) API
   - Anthropic Claude API
   
   —
   
   Article:
   
   Extracting Structured Data from PDFs with AI: Side-by-Side Comparison Using Claude 3.5 Sonnet vs. Gemini 2.0 Flash in n8n
   
   Manual data extraction from PDFs is time-consuming and error-prone—especially when large volumes are involved or when precise structure extraction (like VAT numbers, addresses, or tabular data) is required. With the evolution of large language models (LLMs), we're beginning to see remarkable gains in document parsing accuracy, scalability, and performance.
   
   This article details a no-code n8n workflow that allows users to extract specific structured data from a PDF file stored in Google Drive. It harnesses the capabilities of two leading LLMs—Claude 3.5 Sonnet (Anthropic) and Gemini 2.0 Flash (Google)—to process and interpret PDFs via native PDF understanding (no OCR step required). Whether for developers, analysts, or AI researchers, this workflow is a practical tool for comparing performance, latency, and output quality between these two cutting-edge models.
   
   📌 Workflow Overview:
   The workflow is designed using n8n, an open-source node-based workflow automation platform. Here’s what it does:
   
   - Downloads a PDF file from Google Drive.
   - Converts the file into base64 format (required for LLM ingestion).
   - Sends both the file and a custom user prompt to Claude 3.5 Sonnet and Gemini 2.0 Flash via API calls.
   - Receives and compares summarized or structured data between both models.
   
   Let’s break the process down by the key steps.
   
   Step 1: Manual Trigger  
   The workflow starts with a Manual Trigger node, allowing the user to test the process instantly—ideal for iterative prompt testing.
   
   Step 2: Prompt Definition  
   A Set node called “Define Prompt” allows users to write their own instructional prompt. In this specific configuration, the prompt is:  
   "Extract the VAT numbers for each country",  
   but this can be customized depending on the use case—think invoice processing, legal entity extraction, resume parsing, etc.
   
   Step 3: File Download from Google Drive  
   The next node (“Google Drive”) fetches the selected PDF file based on its fileId. This requires a Google Drive OAuth2 connection set up in n8n.
   
   Step 4: File Conversion to Base64  
   The "Extract from File" node takes the downloaded PDF and converts it into a base64 string. Both Claude and Gemini require PDF data in this format to ingest it effectively.
   
   Step 5: Parallel API Calls to Claude and Gemini
   
   🧠 Claude 3.5 Sonnet  
   Using a POST request to Anthropic’s Claude API endpoint (api.anthropic.com/v1/messages), this node sends:
   
   - A base64 version of the PDF
   - The user-defined prompt from the earlier step
   
   Claude processes documents natively, allowing accurate parsing and contextual understanding of structured and unstructured data from within the PDF.
   
   🧠 Gemini 2.0 Flash  
   A parallel node sends the PDF and prompt to Google’s Gemini Flash model via the endpoint (generativelanguage.googleapis.com). Like Claude, Gemini Flash processes the file natively. There's also flexibility to define the response format—for example, JSON—with structured output definitions.
   
   Comparison-Friendly by Design  
   This workflow is optimized so users can compare:
   
   - Accuracy of data extraction
   - Latency of response
   - Token consumption and API usage costs (referenced via external dashboards)
   
   The workflow supports easy deactivation of either API branch, enabling focus on one model at a time if desired.
   
   🔍 Use Cases
   
   - Invoice data extraction (e.g., line items, totals, VAT, dates)
   - Compliance document parsing (e.g., legal clauses, party names)
   - Academic paper summarization
   - Receipt digitization
   
   You can tailor the prompt to any data you want extracted, making it extremely versatile across industries and roles.
   
   📋 Setup Requirements
   
   Before you run the workflow, ensure the following:
   
   - A connected Google Drive API credential in n8n
   - A Claude 3.5 Sonnet API Key (available from Claude Console)
   - A Gemini API Key (available via Google AI Studio)
   - A selected document on your Google Drive for testing
   
   📎 Bonus: Structured Output Tips  
   To enhance interpretability and reduce hallucinations, both Claude and Gemini APIs allow formatting their responses. For example:
   
   Claude: Use "prefill response format" to anchor the output consistency.  
   Gemini: Insert "generationConfig" with responseMimeType set to "application/json" for clean, typed data outputs.
   
   More tips are included in the sticky notes within the n8n workflow canvas.
   
   🏁 Final Thoughts
   
   In this era of generative AI, combining low-code automation tools with powerful LLMs offers businesses immense leverage in building scalable, automated document understanding pipelines. This workflow exemplifies how quickly and effectively you can prototype and deploy such systems—without needing to write extensive backend logic or maintain OCR pipelines.
   
   Using n8n as a glue layer, you can transform this workflow to work with other file sources (e.g., Dropbox, email attachments) or extend it to update spreadsheets, populate CRMs, or even send summaries via Slack.
   
   Whether you're a data scientist evaluating AI models or a business analyst needing fast answers from documents, this workflow provides a practical, extensible foundation.
   
   Start building smarter with Claude and Gemini—side by side, on your terms, in just a few clicks.
   
   —  
   Would you like this article formatted for publication on a technical blog or LinkedIn? Let me know!

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.