This article provides a complete, practical walkthrough of the Extractfromfile Manual Create Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Advanced setup in 1-2 hours. One‑time purchase: €69.

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:  
   Building a WhatsApp AI Sales Assistant with n8n and OpenAI: A Step-by-Step Guide
   
   Meta Description:  
   Learn how to build an intelligent WhatsApp chatbot using n8n, OpenAI, and a product brochure PDF. This guide walks you through creating a Sales AI Agent that answers customer queries with document-backed accuracy.
   
   Keywords:  
   n8n workflow, WhatsApp chatbot, OpenAI GPT-4o, product brochure AI, vector store, PDF text extraction, AI assistant, AI sales agent, AI automation, LangChain, Yamaha loudspeakers, chat memory, AI-powered support
   
   Third-Party APIs Used:
   
   1. OpenAI API  
   2. WhatsApp Business API  
   3. HTTP Request (for downloading PDF)
   
   Article:
   
   How to Build a WhatsApp AI Sales Assistant Using n8n and OpenAI
   
   In a world driven by instant communication and intelligent automation, businesses are constantly seeking ways to scale customer support and sales operations with minimal overhead. With platforms like n8n—an open-source, no-code/low-code automation platform—combined with OpenAI’s powerful language models, creating sophisticated AI-powered assistants is easier than ever.
   
   In this guide, we’ll explore an n8n workflow that does just that: a fully functional WhatsApp-based Sales AI Agent designed to understand user messages, consult a product catalog, and deliver accurate, helpful replies in real time.
   
   Let’s dive into how it all comes together.
   
   📥 Step 1: Import the Product Brochure
   
   To provide meaningful and accurate information, the AI chatbot needs a well-structured knowledge base. For this demo, we use a PDF brochure from Yamaha highlighting their 2024 powered loudspeakers.
   
   This is accomplished with:
   - The HTTP Request node that fetches the PDF directly from Yamaha’s servers.
   - The Extract from File node that extracts text content from the downloaded brochure.
   
   This forms the foundation of the AI agent’s knowledge.
   
   🧠 Step 2: Create a Vector Store for Smart Text Retrieval
   
   The extracted brochure text is not yet useful until it’s indexed intelligently. Enter vector embeddings—a technique that represents text as numerical vectors so that semantic similarity searches can be performed.
   
   Here's how it's done:
   - The Recursive Character Text Splitter chunks the long brochure text into small, retrievable pieces.
   - OpenAI's Embeddings API (using the 'text-embedding-3-small' model) converts each chunk into vector representation.
   - The In-Memory Vector Store in n8n temporarily stores these vectors, allowing for fast and intelligent lookup by the AI agent.
   
   Optional but recommended for production setups: Use an external vector database like Pinecone or Qdrant for scalability and persistence.
   
   📲 Step 3: Set Up WhatsApp Trigger
   
   To make the Sales AI accessible via WhatsApp, we use:
   - The WhatsApp Trigger node to receive incoming text messages from customers.
   - A Switch node to filter incoming message types, only allowing text messages through.
   - Basic error handling via a WhatsApp node that responds with a polite message for unsupported message types (like images or voice notes).
   
   This ensures stability while keeping the chatbot experience smooth and professional.
   
   🤖 Step 4: Build the AI Agent with Chat Memory + Vector Tools
   
   Now comes the intelligence engine:
   - An n8n LangChain AI Agent node powered by OpenAI's gpt-4o model receives the customer’s question.
   - A prompt is defined to guide the AI's behavior: the assistant acts as a helpful Yamaha Sales Agent, referring to the 2024 loudspeakers brochure.
   - A LangChain Window Buffer Memory node adds per-user context so returning customers can ask follow-up questions naturally.
   - The Vector Store Tool allows the agent to pull from our embedded brochure data, giving fact-rich and precise responses.
   
   This blend of memory and custom tools is what makes the AI feel conversational and informed.
   
   ✉️ Step 5: Reply with the AI's Response
   
   Finally, whatever the AI agent determines as an appropriate response is sent back to the customer using the WhatsApp node, closing the loop of communication—even if the customer is halfway across the globe or chatting at midnight.
   
   📌 Why This Workflow Stands Out
   
   - All user interactions are automatic and personalized.
   - Only text messages are handled, making it simple and reducing chance of edge cases.
   - Follows a clear separation of responsibilities: document import, vector storage, AI conversation.
   - Easily scalable and upgradable by integrating with external vector stores or more advanced workflows in n8n.
   
   🎯 Real-World Use Cases
   
   - Customer service agents backed by product documentation.
   - Info bots that can handle brochures, FAQs, or manuals.
   - Internal support for sales teams accessing live product specs on the go.
   
   🛠 Tools & APIs Used
   
   - OpenAI API: For both text generation and vector embeddings.
   - WhatsApp Business API: To receive and send messages.
   - HTTP Request Node: To dynamically import brochure PDFs.
   - n8n LangChain Integration: For managing vector stores, memory, and AI helpers.
   
   🧪 Try It Out
   
   This project showcases how automation and AI can extend your customer touchpoints while staying scalable and cost-effective. Whether you're a solo founder, a media team, or an enterprise, this workflow is a game-changing starter to smarter customer engagement.
   
   Want to customize it further? Consider uploading multiple brochures, adding multilingual support, or using persistent storage for long-term memory.
   
   —
   
   Need help?
   
   Join the n8n community on Discord or the official Forum. The possibilities are endless—and so is the knowledge of your new Sales AI Agent.

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.