This article provides a complete, practical walkthrough of the Splitout Code Create 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

   Sure! Based on the detailed n8n workflow you provided, I’ve summarized and written an article that explains its purpose, how it works, and the value it brings. Below is the full content, including the requested components.
   
   ---
   
   📝 Title:
   Automating AI-Powered PDF Data Extraction with Baserow and n8n Workflows
   
   🔍 Meta Description:
   Learn how to automate data extraction from PDFs using OpenAI-powered prompts and store the information dynamically in Baserow tables via an n8n workflow. Perfect for low-code enthusiasts and data teams.
   
   🏷️ Keywords:
   n8n, Baserow, OpenAI, LangChain, PDF Data Extraction, automation, low-code, AI prompts, dynamic forms, webhook, intelligent automation, table schema, field mapping
   
   🔗 Third-party APIs Used:
   
   1. Baserow API (https://api.baserow.io)
   2. OpenAI API (via LangChain integration)
   3. n8n's LangChain & HTTPRequest Nodes
   
   ---
   
   ## Automating PDF Data Extraction to Baserow using AI and n8n
   
   When it comes to process-friendly automation and flexibility, the integration of low-code platforms like n8n with modern databases like Baserow and the intelligence of OpenAI proves to be a game-changer. In this article, we explore how a dynamic n8n workflow streamlines the extraction of structured data from PDFs and automates its entry into a Baserow table.
   
   ### The Use Case
   
   Imagine a scenario where a team uploads PDF documents with varying formats into a Baserow table. Each column (or "field") in the table is defined to extract a specific piece of information from the PDF. What if the column description itself could be turned into an extraction prompt? This workflow brings that scenario to life using LLMs (Large Language Models), webhook triggers, and smart file handling.
   
   ### How the Workflow Works
   
   This n8n workflow listens for changes on a Baserow table — specifically:
   
   - when a row is updated,
   - a field (i.e., column) is created,
   - or a field is modified.
   
   Baserow sends these changes as webhook events to n8n, where the automation magic begins.
   
   ---
   
   #### Step 1: Webhook Trigger from Baserow
   
   At the heart of this automation is the "Baserow Event" node using a POST webhook. This allows Baserow to notify n8n whenever a relevant event occurs. Whether it’s a new row, updated row, or new field, n8n is immediately aware and ready to respond accordingly.
   
   ---
   
   #### Step 2: Event Differentiation
   
   Using a “Switch” node named "Event Type", the workflow identifies whether the event is a row update or a field-level change (field created or updated). This is crucial because each type of event requires a different handling pattern.
   
   - Row updates affect only one record.
   - Field changes may require updating all rows under that column.
   
   ---
   
   #### Step 3: Fetching Table Schema and Input
   
   To extract data meaningfully, the workflow needs field-level metadata. The “Table Fields API” node pulls the schema of the affected table using Baserow's REST API. From this, it filters out the fields which have a ‘description’ — these act as dynamic prompts to an LLM for data extraction.
   
   Simultaneously, for updated rows, it fetches the full row data to determine which fields need to be populated.
   
   ---
   
   #### Step 4: Intelligent Row Filtering
   
   A filter node ensures that only rows with valid inputs (i.e., uploaded PDF in the "File" column) proceed further. This avoids unnecessary API calls or LLM interactions. Smart optimization!
   
   ---
   
   #### Step 5: File Handling and PDF Extraction
   
   For each qualifying row and field, the linked PDF is downloaded using its public URL (from the File field) by a standard HTTP Request node. The “Extract from File” node then reads the content from the PDF.
   
   ---
   
   #### Step 6: Generating Field Values using LLM
   
   This is the core AI step. The prompt (from field description) and the file content are combined and sent to the LangChain-powered LLM node (configured with OpenAI). The AI returns a short, precise response to populate the Baserow field.
   
   Two styles are handled:
   
   - Row-event-based updates (single-row mutation).
   - Field-event-based updates (bulk updates across applicable rows).
   
   ---
   
   #### Step 7: Updating the Table
   
   Once all necessary fields are populated with results from the LLM, the data is compiled back and PATCHed to the Baserow table using its API. This updates only the required fields, minimizing unnecessary data overwrites.
   
   For field-related changes, pagination is implemented elegantly using the HTTP Request options to loop through only rows that have a valid file and are missing the updated field.
   
   Batched updates are handled using `SplitInBatches`, ensuring that performance is maintained even on large datasets.
   
   ---
   
   ### Why This Matters
   
   - ✅ Scalable: Designed to handle both individual and bulk updates.
   - ✅ Dynamic: Field descriptions can be changed anytime for new prompt logic.
   - ✅ Intelligent: Uses AI to populate data instead of hardcoded rules.
   - ✅ Code-light: Entire logic implemented using n8n's visual interface.
   - ✅ Reusable: Works for any Baserow table and PDF format.
   
   This setup essentially transforms your Baserow table into a smart spreadsheet that updates itself based on uploaded documents and field-based prompts — without writing a single line of back-end code.
   
   ---
   
   ### Setting It Up
   
   1. Deploy this workflow in your n8n instance and make the webhook publicly accessible.
   2. In Baserow, configure a webhook that is triggered on “row updated,” “field created,” and “field updated” events.
   3. Ensure you use the field names instead of field IDs in the Baserow webhook settings.
   4. Add a “File” column to Baserow where users can upload PDFs.
   5. Add any number of fields and provide prompts as the field description to extract data from the PDF.
   
   > 💡 Pro Tip: Make sure your OpenAI API Key and n8n HTTP Header credentials are correctly set up in the workflow for smooth execution.
   
   ---
   
   ### Final Thoughts
   
   This powerful approach bridges the gap between human-readable prompts and structured machine data. It’s an elegant solution for automating data extraction at scale while staying completely flexible for the end user — all thanks to the modularity of n8n, the smart webhooks of Baserow, and the intelligence of OpenAI.
   
   🔗 Want to try the same with Airtable? Check out the alternative workflow here: https://n8n.io/workflows/2771-ai-data-extraction-with-dynamic-prompts-and-airtable/
   
   🎥 Watch the video demo of this workflow: https://www.youtube.com/watch?v=_fNAD1u8BZw
   
   ---
   
   Have questions or need support? Join the vibrant n8n community on Discord or visit the forums.
   
   Happy Flowgramming!
   
   --- 
   
   Let me know if you'd like the article exported as a markdown file or HTML snippet for your documentation or blog.

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.