WhatsApp Virtual Receptionist with Gemini AI - Handles Text & Voice with Knowledge Base

Overview
This project is an AI-powered WhatsApp virtual receptionist built using n8n, designed to handle both text and voice-based customer messages automatically.
The workflow integrates Google Gemini, Pinecone, and the WhatsApp Business API to provide intelligent, context-aware responses that feel natural and professional.

How It Works

Message Detection
The workflow begins when a message arrives on WhatsApp. It identifies whether the message is text or voice and routes it accordingly.

Voice Message Handling
Audio messages are securely downloaded from WhatsApp.
The files are converted to Base64 format and sent to the Gemini API for transcription.
The transcribed text is then passed to the AI Agent for further processing.

AI Agent Processing
The LangChain AI Agent acts as the brain of the system. It uses:
Google Gemini Chat Model** for natural language understanding and response generation.
Pinecone Vector Store** to retrieve company-specific information and product data.
Memory Buffer** to remember the last 20 user messages, ensuring context-aware responses.

The agent also follows a set of custom communication rules — replying only in approved languages, skipping greetings, and focusing on direct, helpful, and professional responses (e.g., product recommendations, support, or guidance).

Knowledge Retrieval
The AI Agent connects to a Pinecone database containing detailed company data, such as product catalogs or service FAQs. Using Gemini-generated embeddings, it retrieves the most relevant information for each user query.

Response Delivery
Once the AI Agent prepares the response, it is instantly sent back to the user via WhatsApp, completing the conversational loop.

Who It’s For
This system is ideal for businesses seeking to automate their customer communication through WhatsApp. It’s especially valuable for:
Product-based companies** with frequent customer inquiries.
Service providers** offering 24/7 customer assistance or quote requests.
SMBs** looking to scale their communication without hiring additional staff.

Tech Stack & Requirements

n8n** – Workflow automation and orchestration.
WhatsApp Cloud API** – For sending and receiving messages.
Google Gemini (PaLM)** – For LLM-based transcription and response generation.
Pinecone** – Vector database for product and service knowledge retrieval.
LangChain Integration** – For connecting memory, vector store, and reasoning tools.
Custom Business Rules** – Configurable within the AI Agent node to manage tone, style, and workflow behavior.

Key Features

Handles both text and voice messages seamlessly.
Responds in multiple languages, including English.
Maintains conversation memory per user session.
Retrieves accurate company-specific information using vector search.
Fully automated, with customizable behavior for different industries or use cases.

Setup Instructions

1. Prerequisites
   Before importing the workflow, ensure you have:
   An active n8n instance (self-hosted or n8n Cloud).
   WhatsApp Cloud API credentials** from Meta.
   Google Gemini API key** with model access (for chat and transcription).
   Pinecone API key** with a preconfigured vector index containing your company data.

2. Environment Setup
   Install all required credentials under Settings → Credentials in n8n.
   Add environment variables (if applicable) for keys like:
   GOOGLE_API_KEY=your_google_gemini_key
   PINECONE_API_KEY=your_pinecone_key
   WHATSAPP_ACCESS_TOKEN=your_whatsapp_token

3. Pinecone Configuration
   Create a Pinecone index named, for example, products-index.
   Upload company documents or product details as vector embeddings using Gemini or LangChain utilities.
   Adjust the retrieval limit in the Pinecone node settings for broader or narrower search responses.

4. WhatsApp API Configuration
   Set up a WhatsApp Business Account via Meta Developer Dashboard.
   Create a webhook endpoint URL (n8n’s public URL) to receive WhatsApp messages.
   Use the WhatsApp Trigger Node to capture messages in real time.

5. AI Agent Customization
   You can personalize how the AI behaves by editing the system prompt inside the AI Agent node:
   Modify tone, response length, or product focus.
   Add new “rules” for language preferences or conversation flow.
   Include links or custom text output (e.g., quotation formats, product catalog messages).

6. Handling Voice Messages
   Ensure your WhatsApp Business Account has media message permissions enabled.
   Verify the HTTP Request node that connects to the Gemini API for transcription is correctly authenticated.
   You can adjust the transcription model or prompt if you prefer shorter, keyword-based outputs.

7. Testing
   Send both text and voice messages from a test WhatsApp number.
   Check response time and message formatting.
   Use n8n’s execution logs to debug errors (especially for media downloads or API credentials).

Customization Options

🧩 AI Behavior
Modify the AI Agent’s system message to adapt tone and personality (e.g., sales-oriented, support-driven).
Update memory length (default: last 20 messages) for longer or shorter conversations.

🌍 Multi-language Support
Add or remove allowed languages in the rules section of the AI Agent node.
For multilingual businesses, duplicate the AI Agent path and route messages by language detection.

📦 Industry Adaptation
Swap the Pinecone dataset to suit different industries — retail, hospitality, logistics, etc.
Replace product data with FAQs, customer records, or support documentation.