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RAG Backend API Setup Guide

Quick guide to set up and run your RAG backend API for local testing with your portfolio chatbot frontend.

Prerequisites

  • Node.js (v16 or higher)
  • Google AI API key (for Gemini)
  • Pinecone account and API key

Setup Steps

1. Navigate to API Directory

cd portfolio-chatbot-api

2. Install Dependencies

npm install

3. Configure Environment Variables

# Copy the example file
cp .env.example .env

# Edit .env file with your API keys

Your .env file should contain:

# Google AI API Key for Gemini LLM
GOOGLE_AI_API_KEY=your_gemini_api_key_here

# Pinecone Vector Database Configuration
PINECONE_API_KEY=your_pinecone_api_key_here
PINECONE_INDEX=your_pinecone_index_name_here

# Server Configuration
PORT=3000
NODE_ENV=development

# Frontend URL for CORS
FRONTEND_URL=http://localhost:3000

4. Seed Your Portfolio Content

# Update with your personal content
npm run update-content

# Seed the vector database
npm run seed

5. Start the Development Server

npm run dev

The API will be running on http://localhost:3000

API Endpoints

Main Chat Endpoint

POST http://localhost:3000/api/chat
Content-Type: application/json

Body:
{
  "message": "Tell me about your projects"
}

Response:
{
  "response": "AI-generated response about your portfolio..."
}

Health Check

GET http://localhost:3000/health

Response:
{
  "status": "healthy",
  "timestamp": "2025-01-07T06:17:26.000Z",
  "service": "portfolio-chatbot-api"
}

Search Endpoint (for testing)

POST http://localhost:3000/api/search
Content-Type: application/json

Body:
{
  "query": "projects",
  "topK": 5
}

Response:
{
  "results": [...]
}

Frontend Integration

To connect your frontend, make HTTP requests to the chat endpoint:

const response = await fetch('http://localhost:3000/api/chat', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    message: 'Your user message here'
  })
});

const data = await response.json();
console.log(data.response); // AI response

Testing the API

Quick Test with curl

# Health check
curl http://localhost:3000/health

# Test chat
curl -X POST http://localhost:3000/api/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello"}'

Using PowerShell (Windows)

# There's already a test script included
.\test-api.ps1

CORS Configuration

The API accepts requests from any origin by default. For production, update FRONTEND_URL in your .env file to your specific domain.

Troubleshooting

  • Port 3000 in use: Change PORT in .env file
  • CORS errors: Verify FRONTEND_URL setting
  • API key errors: Double-check your Gemini and Pinecone keys
  • Empty responses: Make sure you've run the seeding steps

Ready for Integration

Once the server is running and you see:

🚀 Portfolio Chatbot API server running on port 3000
📍 Health check: http://localhost:3000/health
💬 Chat endpoint: http://localhost:3000/api/chat

Your API is ready to integrate with your frontend chatbot!