Database + AI
Integration

import os
from fastapi import FastAPI, Header, HTTPException
from anthropic import Anthropic
from pydantic import BaseModel
import asyncpg
from dotenv import load_dotenv

load_dotenv()

app            = FastAPI()
anthropic_client = Anthropic(api_key=os.getenv('ANTHROPIC_API_KEY'))
API_KEY        = os.getenv('API_KEY')


class IncomingMessage(BaseModel):
    type:      str
    sender:    str
    name:      str = None
    message:   str
    timestamp: str


async def fetch_customer_context(phone: str) -> str:
    """Query YOUR PostgreSQL database for customer and order data."""
    conn = await asyncpg.connect(
        host=os.getenv('DB_HOST'),
        port=int(os.getenv('DB_PORT', '5432')),
        user=os.getenv('DB_USER'),
        password=os.getenv('DB_PASSWORD'),
        database=os.getenv('DB_NAME')
    )
    try:
        # Look up customer by phone number
        customer = await conn.fetchrow(
            'SELECT id, name, status, membership FROM customers WHERE phone = $1',
            phone
        )
        if not customer:
            return 'No customer record found for this phone number.'

        # Fetch last 3 orders
        orders = await conn.fetch(
            '''SELECT order_id, status, total
                 FROM orders
                WHERE customer_id = $1
                ORDER BY created_at DESC
                LIMIT 3''',
            customer['id']
        )

        order_lines = '\n'.join(
            f"  - Order #{o['order_id']}: {o['status']} ({o['total']} RON)"
            for o in orders
        ) or '  No recent orders.'

        return (
            f"Customer : {customer['name']}\n"
            f"Status   : {customer['status']}\n"
            f"Membership: {customer['membership']}\n"
            f"Recent orders:\n{order_lines}"
        )
    finally:
        await conn.close()


@app.post('/api/autoreply')
async def handle_message(data: IncomingMessage, x_api_key: str = Header(None)):

    # ── 1. Verify AutoReply Mate API key ─────────────────────────────────
    if x_api_key != API_KEY:
        raise HTTPException(status_code=403, detail='Invalid API key')

    # ── 2. Pull context from YOUR database ───────────────────────────────
    db_context = await fetch_customer_context(data.sender)
    print(f'[{data.type}] From {data.sender}: {data.message}')
    print(f'DB context: {db_context}')

    # ── 3. Generate reply with YOUR Claude key ────────────────────────────
    try:
        response = anthropic_client.messages.create(
            model='claude-3-5-sonnet-20241022',
            max_tokens=150,
            system=(
                'You are a helpful customer support agent.\n'
                'Reply in maximum 2 short sentences. Be friendly and specific.\n\n'
                'Customer data from the database:\n' + db_context
            ),
            messages=[{'role': 'user', 'content': data.message}]
        )
        reply = response.content[0].text
        print(f'Reply: {reply}')
        return {'text': reply, 'status': 'success'}

    except Exception as e:
        print(f'AI Error: {e}')
        return {'text': "We'll respond shortly!", 'status': 'success'}


if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)

require('dotenv').config();
const express     = require('express');
const { MongoClient } = require('mongodb');
const OpenAI      = require('openai');

const app = express();
app.use(express.json());

const mongoClient = new MongoClient(process.env.MONGODB_URI);
const openai      = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const API_KEY     = process.env.API_KEY;

let db;

// Connect to YOUR MongoDB on startup
mongoClient.connect()
  .then(client => {
    db = client.db(process.env.DB_NAME || 'autoreply');
    console.log('MongoDB connected');
  })
  .catch(err => {
    console.error('MongoDB connection error:', err);
    process.exit(1);
  });

app.post('/api/autoreply', async (req, res) => {

  // ── 1. Verify AutoReply Mate API key ─────────────────────────────────────
  if (req.headers['x-api-key'] !== API_KEY) {
    return res.status(403).json({ error: 'Forbidden', status: 'forbidden' });
  }

  const { type, sender, name, message } = req.body;

  try {
    // ── 2. Query YOUR MongoDB for customer context ────────────────────────
    const customer = await db.collection('customers').findOne({ phone: sender });

    let dbContext = 'No customer record found for this phone number.';

    if (customer) {
      const orders = await db.collection('orders')
        .find({ customerId: customer._id })
        .sort({ createdAt: -1 })
        .limit(3)
        .toArray();

      const orderLines = orders.length > 0
        ? orders.map(o => `  - Order #${o.orderId}: ${o.status} (${o.total} RON)`).join('\n')
        : '  No recent orders.';

      dbContext =
        `Customer : ${customer.name}\n` +
        `Status   : ${customer.status}\n` +
        `Plan     : ${customer.plan}\n` +
        `Notes    : ${customer.notes || 'None'}\n` +
        `Recent orders:\n${orderLines}`;
    }

    // ── 3. Generate AI reply with YOUR OpenAI key ─────────────────────────
    const completion = await openai.chat.completions.create({
      model: 'gpt-4',
      messages: [
        {
          role: 'system',
          content:
            'You are a helpful customer support agent.\n' +
            'Reply in maximum 2 short sentences. Be friendly and specific.\n\n' +
            'Customer data from the database:\n' + dbContext
        },
        { role: 'user', content: message }
      ],
      max_tokens:  150,
      temperature: 0.7
    });

    const reply = completion.choices[0].message.content;
    console.log(`[${type}] Reply to ${sender}: ${reply}`);

    // ── 4. Log conversation to MongoDB (optional) ─────────────────────────
    await db.collection('conversations').insertOne({
      phone:      sender,
      messageIn:  message,
      messageOut: reply,
      type,
      createdAt:  new Date()
    });

    res.json({ text: reply, status: 'success' });

  } catch (err) {
    console.error('Error:', err.message);
    res.json({ text: "We'll get back to you shortly!", status: 'success' });
  }
});

app.listen(process.env.PORT || 3000, () => console.log('Server started'));