Zap Vendedor
No description available
Ask AI about Zap Vendedor
Powered by Claude · Grounded in docs
I know everything about Zap Vendedor. Ask me about installation, configuration, usage, or troubleshooting.
0/500
Reviews
Documentation
ZapVendedor - WhatsApp Sales Assistant
A WhatsApp chatbot application that uses local AI (Ollama) and MCP (Model Context Protocol) to provide product recommendations and answer customer questions.
Features
- WhatsApp Business API integration for sending and receiving messages
- Local AI using Ollama for natural language processing
- MCP (Model Context Protocol) for structured AI interactions with external data sources
- Product catalog management
- Conversation history tracking
- Admin dashboard for managing products and viewing conversations
Tech Stack
- Frontend/Backend: Next.js (React + Node.js)
- Database: In-memory storage (can be replaced with a real database)
- WhatsApp Integration: WhatsApp Business Cloud API
- AI: Ollama with LLaMA2 model (or similar)
- Protocol: Model Context Protocol (MCP)
Prerequisites
- Node.js (v18 or later)
- Ollama installed locally (https://ollama.ai/)
- WhatsApp Business API credentials
- LLaMA2 model or similar downloaded via Ollama
Setup
-
Clone the repository:
git clone https://github.com/yourusername/zap_vendedor.git cd zap_vendedor -
Install dependencies:
npm install -
Configure environment variables:
- Copy
.env.local.exampleto.env.local - Update the values with your WhatsApp Business API credentials
- Copy
-
Start Ollama:
ollama serve -
Pull the LLaMA2 model (or your preferred model):
ollama pull llama2-chat -
Start the development server:
npm run dev -
Open http://localhost:3000 in your browser to access the admin dashboard.
WhatsApp Business API Setup
- Create a Meta Developer account and set up a WhatsApp Business app
- Configure the webhook URL to point to your application's
/api/whatsapp-webhookendpoint - Set the verify token in your
.env.localfile to match the one you configured in the Meta Developer Portal - Add your WhatsApp Business phone number ID and access token to the
.env.localfile
Project Structure
/src/app- Next.js app router pages and API routes/src/app/api- API endpoints for WhatsApp webhook, products, and conversations/src/app/admin- Admin dashboard pages/src/components- React components/src/lib- Utility functions and services/src/models- Data models/src/mcp- MCP server implementations
MCP Servers
The application uses two MCP servers:
- Database Server: Provides tools for the AI to query the product database
- Web Server: Provides tools for the AI to search the web and scrape information
Mock MCP Implementation
This project includes a mock implementation of the Model Context Protocol (MCP) in src/lib/mcp-mock.ts. This mock implementation provides the core functionality needed for the AI to interact with external data sources:
- Server: Creates MCP servers that provide tools and resources
- Resource: Represents data sources that can be accessed by the AI
- Tool: Represents functions that can be executed by the AI
- Client: Connects to MCP servers and executes tools or fetches resources
The mock implementation is used because the official @anthropic-ai/mcp package is not publicly available yet. When the official package becomes available, you can replace the mock implementation with the real one.
Usage
- Start the application and configure the settings in the admin dashboard
- Connect your WhatsApp Business account
- Start the Ollama server and MCP servers
- Customers can now send messages to your WhatsApp Business number
- The AI will process the messages, search for relevant products, and respond with recommendations
License
MIT
