dani3lrp

import { MongoAIOptimizer } from 'mongo-ai-optimizer';

const optimizer = new MongoAIOptimizer({
  claudeApiKey: process.env.CLAUDE_API_KEY,
  mongoUri: process.env.MONGO_URI
});

const results = await optimizer.analyzeAndOptimize('orders');
console.log(results.improvements);  // Shows all optimizations

dani3lrp

// Webhook triggers when query performance degrades
app.post('/webhook/slow-query', async (req, res) => {
  const { collection, query, executionTime } = req.body;

  if (executionTime > THRESHOLD) {
    const optimization = await mongoAgent.suggest(collection, query);

    // Create PR with suggested changes
    await createGitHubPR({
      title: `AI: Optimize ${collection} query`,

dani3lrp

// Same query now takes 0.4 seconds
db.orders.aggregate([
  { $match: {
    status: "pending",
    // AI suggested adding this filter early
    created: { $gte: new Date(Date.now() - 7*24*60*60*1000) }
  }},
  // AI reordered stages for efficiency
  { $sort: { created: -1 } },
  { $limit: 1000 },  // AI added early limit

dani3lrp

// Query taking 3.2 seconds
db.orders.aggregate([
  { $match: { status: "pending" } },
  { $lookup: {
      from: 'customers',
      localField: 'customerId',
      foreignField: '_id',
      as: 'customer'
  }},
  { $unwind: "$items" },

dani3lrp

async generateOptimizations(baseline) {
  const prompt = `
    Analyze these MongoDB query patterns and their execution stats:
    ${JSON.stringify(baseline, null, 2)}

    Consider:
    1. Index intersection opportunities
    2. Compound index ordering based on cardinality
    3. Partial indexes for subset queries
    4. Aggregation pipeline optimizations

dani3lrp

import { LangChain } from 'langchain';
import { MongoClient } from 'mongodb';
import { Claude } from '@anthropic-ai/sdk';

class MongoOptimizerAgent {
  constructor() {
    this.llm = new Claude({
      model: 'claude-3-opus-20240229',
      temperature: 0.2  // Low temperature for consistency
    });