🧭 قسمت 3: اجرای جستجوی بردار با Pinecone

npm install @pinecone-database/pinecone

PINECONE_API_KEY=your_pinecone_api_key
PINECONE_ENVIRONMENT=your_pinecone_environment

// backend/langchain/config.js
const { PineconeClient } = require('@pinecone-database/pinecone');

const initPinecone = async () => {
  const client = new PineconeClient();
  await client.init({
    apiKey: process.env.PINECONE_API_KEY,
    environment: process.env.PINECONE_ENVIRONMENT,
  });
  return client;
};

// backend/langchain/embedOrders.js
const { OpenAIEmbeddings } = require('@langchain/openai');
const { initPinecone } = require('./config');
const { connectToDatabase } = require('../database/connection');

async function embedOrders() {
  const db = await connectToDatabase();
  const orders = await db.collection('orders').find().toArray();

  const embeddings = new OpenAIEmbeddings({
    openAIApiKey: process.env.OPENAI_API_KEY,
  });

  const pinecone = await initPinecone();
  const index = pinecone.Index("ecommerce-orders");

  for (const order of orders) {
    const orderSummary = `
      Order ID: ${order.orderId}
      Customer: ${order.customerName}
      Items: ${order.items.map(i => i.productName).join(', ')}
      Status: ${order.status}
    `;

    const [embedding] = await embeddings.embedQuery(orderSummary);

    await index.upsert([
      {
        id: order.orderId,
        values: embedding,
        metadata: {
          orderId: order.orderId,
          customerName: order.customerName,
          status: order.status,
        },
      },
    ]);
  }

  console.log("All orders embedded and stored in Pinecone.");
}

embedOrders();

node backend/langchain/embedOrders.js

// backend/langchain/searchOrder.js
const { OpenAIEmbeddings } = require('@langchain/openai');
const { initPinecone } = require('./config');

async function searchOrders(userQuery) {
  const embeddings = new OpenAIEmbeddings({
    openAIApiKey: process.env.OPENAI_API_KEY,
  });

  const pinecone = await initPinecone();
  const index = pinecone.Index("ecommerce-orders");

  const [queryVector] = await embeddings.embedQuery(userQuery);

  const results = await index.query({
    vector: queryVector,
    topK: 3,
    includeMetadata: true,
  });

  return results.matches;
}

module.exports = { searchOrders };