Answer Product Queries via WhatsApp using OpenAI GPT-4o and PDF Knowledge Base Workflow Solution

Workflow overview

Why this workflow matters

Useful for software delivery and engineering operations. Helpful for business development and pipeline building.

WhatsApp AI Sales Agent using PDF Vector Store This workflow turns your WhatsApp number into an intelligent AI-powered Sales Agent that answers product queries using real data extracted from a PDF brochure. It loads a product brochure via HTTP Request, converts it into embeddings using OpenAI, stores them in an in-memory vector store and allows the AI Agent to provide factual answers to users via WhatsApp. Non-text messages are filtered and only text queries are processed. This makes the workflow ideal for building a lightweight chatbot that understands your product documentation deeply. Quick Start: 5-Step Fast Implementation Insert your WhatsApp credentials in the WhatsApp Trigger and WhatsApp Send nodes. Add your OpenAI API Key to all OpenAI-powered nodes. Replace the PDF URL in the HTTP Request node with your own brochure. Run the Manual Trigger once to build the vector store. Activate the workflow and start chatting from WhatsApp. What It Does This workflow converts a product brochure (PDF) into a searchable knowledgebase using LangChain vector embeddings. Incoming WhatsApp messages are processed and if the message is text, the AI Sales Agent uses OpenAI + the vector store to produce accurate, brochure-based answers. The AI responds naturally to customer queries, supports conversation memory across the session and retrieves information directly from the brochure when needed. Non-text messages are filtered out to maintain clean conversational flow. The workflow is fully modular: you can replace the PDF, modify AI prompts, plug into CRM systems or extend it into a broader sales automation pipeline. Who’s It For This workflow is ideal for: Businesses wanting a WhatsApp-based AI customer assistant. Sales teams needing automated product query handling. Companies with large product catalog PDFs. Marketers wanting a zero-code product brochure chatbot. Technical teams experimenting with LangChain + OpenAI inside AlekSystem. Requirements to Use This Workflow To run this workflow successfully, you need: An AlekSystem instance (cloud or self-hosted). A WhatsApp Business API connection. An OpenAI API key. A publicly accessible PDF brochure URL. Basic familiarity with AlekSystem node configuration. Optional: A custom vector store backend (Qdrant, Pinecone) – the template uses in-memory storage. How It Works & How To Set Up 1. Import the Workflow JSON Upload the workflow JSON provided. 2. Configure WhatsApp Trigger Open WhatsApp Trigger Add your WhatsApp credentials Set the webhook correctly to match your AlekSystem endpoint 3. Configure WhatsApp Response Nodes The workflow uses two WhatsApp send nodes: Reply To User** → Sends AI response Reply To User1** → Sends “unsupported message” reply Add your WhatsApp credentials to both. 4. Replace the PDF Brochure In get Product Brochure (HTTP Request): Update the url parameter with your own PDF 5. Run the PDF → Vector Store Setup (One-Time Only) Use the Manual Trigger ("When clicking ‘Test workflow’") to: Download the PDF Extract text Split into chunks Generate embeddings Store them in Product Catalogue vector store > You must run this once after importing the workflow. 6. Set OpenAI Credentials Add your OpenAI API Key to the following nodes: OpenAI Chat Model OpenAI Chat Model1 Embeddings OpenAI Embeddings OpenAI1 7. Review the AI Agent Prompt Inside AI Sales Agent, you can edit the system message to match: Your brand Your product types Your tone of voice 8. Activate the Workflow Once activated, WhatsApp users can chat with your AI Sales Agent. How to Customize Nodes? Here are common customization options: Customize the PDF / Knowledgebase Change the URL in get Product Brochure or Upload your own file via other nodes. Customize AI Behavior Edit the systemMessage inside AI Sales Agent: Change personality Set product rules Restrict/expand scope Change Supported Message Types Modify Handle Message Types switch logic to allow: Image → OCR Audio → Whisper Documents → Additional processing Modify WhatsApp Message Templates Inside the textBody of response nodes. Extend or replace Vector Store Swap vectorStoreInMemory with: Qdrant Pinecone Redis vector store By updating the vector store node. Add-Ons (Optional Enhancements) You can extend this workflow with: 1. Multi-language support Add OpenAI translation nodes before agent input. 2. CRM Integration Send user queries and chat logs into: HubSpot Salesforce Zoho CRM 3. Product Recommendation Engine Use embeddings similarity to suggest products. 4. Order Placement Workflow Connect to Stripe or Shopify APIs. 5. Analytics Dashboard Log chats into Airtable / Postgres for analysis. Use Case Examples Here are some practical uses: Product Inquiry Chatbot Customers ask about specs, pricing, or compatibility. Digital Catalog Assistant Converts PDF brochures into interactive WhatsApp search. Sales Support Bot Reduces load on human sales reps by handling common questions. Internal Knowledge Bot Teams access manuals, training documents, or service guides. Event/Product Launch Assistant Provides instant details about newly launched items. And many more similar use cases where an AI-powered WhatsApp assistant is valuable. Troubleshooting Guide | Issue | Possible Cause | Solution | | ------------------------------------------ | -------------------------------------- | ------------------------------------------------------------- | | WhatsApp messages not triggering workflow | Wrong webhook URL or inactive workflow | Ensure webhook is correct & activate workflow | | AI replies are empty | Missing OpenAI credentials | Add OpenAI API key to all AI nodes | | Vector store not populated | Manual trigger not executed | Run the Test Workflow trigger once | | PDF extraction returns blank text | PDF is image-based | Use OCR before text splitting | | “Unsupported message type” always triggers | Message type filter misconfigured | Check conditions in Handle Message Types | | AI not using brochure data | VectorStore tool not linked properly | Check connections between Embeddings → VectorStore → AI Agent | Need Help with Support & Extensions? If you need help setting up, customizing or extending this workflow, feel free to reach out to our AlekSystem automation developers at WeblineIndia. We can help with Custom WhatsApp automation workflows AI-powered product catalog systems Integrating CRM, ERP or eCommerce platforms Building advanced LangChain-powered AlekSystem automations Deploying scalable vector stores (Qdrant/Pinecone) And so much more.

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