AI Sales Agent Backend

A sophisticated Node.js application that implements an AI-powered sales agent using Together AI's LLM capabilities. The platform handles product inquiries, demo scheduling, and customer interactions with a focus on AWS product information.

🌟 Features

Core Capabilities

• 🤖 AI-powered conversational agent using Together AI's LLM (Meta-Llama-3.1-70B-Instruct-Turbo)
• 📝 RAG (Retrieval Augmented Generation) for accurate product information retrieval
• 📅 Automated demo scheduling with email confirmation
• 💾 MongoDB integration for persistent data storage
• 🔄 Real-time communication via WebSocket
• 📊 Sophisticated conversation state management
• 🔍 Vector search using Pinecone for semantic similarity
• 📝 Comprehensive logging system

Technical Features

• TypeScript-based architecture
• Express.js server with WebSocket support
• Modular service architecture
• Comprehensive error handling
• Structured logging with Winston
• Zod schema validation
• Environment-based configuration

🏗 Architecture

Core Components

1. WebSocket Manager: Handles real-time communication
2. Conversation Manager: Manages conversation flow and state
3. Node Service: Implements conversation flow logic
4. AI Service: Integrates with Together AI
5. Embedding Service: Manages vector embeddings with Pinecone
6. Product Service: Handles product-related queries

Data Models

• Customer
• Message
• Event
• Session

🚀 Prerequisites

• Node.js (v14 or higher)
• MongoDB (v4.4 or higher)
• npm or yarn package manager
• Pinecone account
• Together AI API key

⚙️ Environment Variables

Create a .env file with:

API Keys

TOGETHER_API_KEY=your_together_ai_key PINECONE_API_KEY=your_pinecone_key PINECONE_ENVIRONMENT=your_pinecone_environment

Database

MONGODB_URI=your_mongodb_connection_string

Server

PORT=3000 LOG_LEVEL=debug

📦 Installation

1. Clone the repository:

git clone https://github.com/gooduru-vineeth/sales-agent-backend.git

cd sales-agent-backend

2. Install dependencies:

npm install

3. Build the project:

npm run build

4. Load AWS product data:

npm run load-aws-data

5. Start the server:

npm start

🛠 Development

Available Scripts

• npm start: Run the production server
• npm run dev: Run development server with hot reload
• npm run build: Build the TypeScript project
• npm run lint: Run ESLint
• npm run format: Format code with Prettier
• npm run load-aws-data: Load AWS product data into Pinecone

Project Structure

src/
├── config/         # Configuration files
│   ├── database.ts
│   └── index.ts
│
├── models/         # MongoDB models
│   ├── Customer.ts
│   ├── Event.ts
│   └── Message.ts
│
├── providers/      # External service providers
│   ├── together/
│   └── pinecone/
│
├── repositories/   # Data access layer
│   ├── Customer.ts
│   ├── Event.ts
│   └── Message.ts
│
├── scripts/        # Utility scripts
│   └── loadAwsProductData.ts
│
├── services/       # Business logic
│   ├── AIService.ts
│   ├── ConversationManager.ts
│   ├── EmbeddingService.ts
│   └── NodeService.ts
│
├── types/          # TypeScript types
│   ├── ai-provider.ts
│   ├── analysis.ts
│   ├── customer.ts
│   ├── events.ts
│   └── message.ts
│
├── utils/          # Utility functions
│   └── logger.ts
│
└── websocket/      # WebSocket handling
    └── WebSocketManager.ts

🔄 Conversation Flow

The system implements a node-based conversation flow:

1. Welcome Node
2. Collect User Information
3. Product Information
4. Q&A Node
5. Demo Scheduling
6. End Conversation

📚 AWS Product Coverage

Currently supports information about:

• Amazon EC2
• Amazon S3
• Amazon RDS
• Amazon DynamoDB
• AWS Lambda

🔒 Security

• Secure WebSocket connections
• Environment-based configuration
• Input validation using Zod
• MongoDB security best practices
• API key management

📝 Logging

Comprehensive logging system using Winston with:

• Console output
• File-based logging
• Error tracking
• Request/Response logging
• Performance monitoring

📄 License

MIT License