Farm2Table MVP

A modern farm-to-table marketplace connecting local producers with consumers, featuring AI-powered recommendations and intelligent conversation management.

Features

• 🌱 Producer dashboard for managing produce listings
• 🛒 Consumer marketplace with AI-powered recommendations
• 🤖 OpenAI integration for intelligent produce suggestions
• 💬 AI conversation storage and analytics
• 📚 Knowledge base management with embeddings
• 🔍 Semantic search with vector embeddings
• 📱 Mobile-friendly responsive design
• 🔐 Secure authentication with JWT
• 💾 MongoDB database with Prisma ORM

Tech Stack

• Frontend: Next.js 14, React, TypeScript, Tailwind CSS
• Backend: Next.js API Routes, Prisma ORM
• Database: MongoDB
• Authentication: JWT with bcrypt
• AI: OpenAI GPT-3.5-turbo, text-embedding-3-small
• UI Components: shadcn/ui, Radix UI

Getting Started

Prerequisites

• Node.js 18+
• MongoDB database (local or MongoDB Atlas)
• OpenAI API key

Installation

1. Clone the repository ```bash git clone cd farm2table-mvp ```

2. Install dependencies ```bash npm install ```

3. Set up environment variables ```bash cp .env.example .env ```

Fill in your environment variables:

• DATABASE_URL: Your MongoDB connection string
• JWT_SECRET: A secure random string for JWT signing
• OPENAI_API_KEY: Your OpenAI API key

4. Set up the database ```bash

Generate Prisma client

npm run db:generate

Push schema to database

npm run db:push

Seed with sample data

npm run db:seed

Seed knowledge base

npm run db:seed-knowledge ```

5. Start the development server ```bash npm run dev ```

Visit http://localhost:3000 to see the application.

Sample Accounts

After seeding, you can use these accounts:

Producers:

• Email: juan@farm.com / Password: password123
• Email: maria@farm.com / Password: password123

Consumer:

• Email: customer@example.com / Password: password123

Database Schema

The application uses the following main models:

• User: Stores user accounts (producers and consumers)
• Produce: Product listings from producers with AI-generated descriptions and embeddings
• Order: Customer orders
• OrderItem: Individual items within orders
• AIConversation: Enhanced conversation logs with context and metadata
• KnowledgeBase: Structured knowledge entries with embeddings for AI responses

AI Features

Conversation Management

• Session-based conversations: Track user interactions across sessions
• Context awareness: AI considers user preferences, location, and conversation history
• Metadata tracking: Store response times, recommended categories, and user interactions
• Analytics: View conversation insights, popular questions, and user engagement

Knowledge Base

• Semantic search: Find relevant knowledge using vector embeddings
• Category organization: Organize knowledge by farming, nutrition, recipes, storage, etc.
• Tag system: Flexible tagging for better categorization
• Producer management: Producers can add and manage knowledge entries

Enhanced Recommendations

• Semantic similarity: Find produce using natural language queries
• Context-aware suggestions: Consider user location, season, and preferences
• Knowledge integration: AI responses include relevant farming and nutrition information
• Real-time learning: System improves recommendations based on conversation patterns

API Endpoints

Authentication

• POST /api/auth/register - User registration
• POST /api/auth/login - User login

Produce

• GET /api/produce - Get all active produce
• POST /api/produce - Create new produce (producers only)

AI Assistant

• POST /api/ai/recommend - Get AI recommendations with context
• GET /api/conversations - Get conversation history
• GET /api/knowledge - Search knowledge base
• POST /api/knowledge - Create knowledge entry

Development

Database Management

```bash

View database in Prisma Studio

npm run db:studio

Reset database and reseed

npm run db:push --force-reset npm run db:seed npm run db:seed-knowledge ```

Project Structure

``` ├── app/ # Next.js app directory │ ├── api/ # API routes │ │ ├── ai/ # AI recommendation endpoints │ │ ├── conversations/ # Conversation history │ │ └── knowledge/ # Knowledge base management │ ├── consumer/ # Consumer pages │ ├── producer/ # Producer pages │ └── globals.css # Global styles ├── components/ # React components │ ├── auth/ # Authentication components │ ├── consumer/ # Consumer-specific components │ ├── producer/ # Producer-specific components │ │ ├── knowledge-base-manager.tsx # Knowledge base management │ │ └── conversation-analytics.tsx # Conversation insights │ ├── shared/ # Shared components │ └── ui/ # UI components (shadcn) ├── lib/ # Utility libraries │ ├── auth.ts # Authentication utilities │ ├── openai.ts # OpenAI integration with conversation management │ ├── embeddings.ts # Vector embedding utilities │ └── prisma.ts # Prisma client ├── prisma/ # Database schema and migrations │ ├── seed.ts # Sample produce data │ └── seed-knowledge.ts # Knowledge base seeding └── public/ # Static assets ```

AI Conversation Features

For Consumers

• Natural language queries: Ask questions like "What's good for juicing?"
• Contextual responses: AI remembers your preferences and previous questions
• Semantic search: Find produce using natural descriptions
• Personalized recommendations: Based on location, season, and dietary preferences

For Producers

• Knowledge management: Add farming tips, nutrition info, and recipes
• Conversation analytics: See what customers are asking about
• Market insights: Understand customer preferences and trends
• AI-enhanced listings: Automatically generate rich product descriptions

Deployment

1. Set up a MongoDB database (MongoDB Atlas recommended)
2. Deploy to Vercel or your preferred platform
3. Set environment variables in your deployment platform
4. Run database migrations: npm run db:push
5. Seed with sample data: npm run db:seed && npm run db:seed-knowledge

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

This project is licensed under the MIT License.