Building an E-Commerce Support Chatbot: Part 4 - Creating the LangChain Pipeline

🔗 Part 4: Creating the LangChain Pipeline

In this part, we’ll build the LangChain-powered backend pipeline that connects your chatbot to MongoDB and Pinecone, handles data chunking, generates embeddings, and retrieves relevant responses.

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✅ What We'll Cover

• Setting up document loading from MongoDB
• Splitting order data into chunks
• Creating embeddings from chunks
• Storing vectors in Pinecone
• Retrieving relevant chunks for user queries

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🧱 1. Load Data from MongoDB

We’ll load order data to feed into LangChain's document processing tools.

// backend/langchain/loadOrders.js
const { connectToDatabase } = require('../database/connection');

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

  return orders.map(order => ({
    pageContent: `
      Order ID: ${order.orderId}
      Customer: ${order.customerName}
      Email: ${order.email}
      Items: ${order.items.map(i => \`\${i.productName} x\${i.quantity}\`).join(', ')}
      Total: $\${order.totalAmount}
      Status: \${order.status}
      Date: \${order.orderDate.toDateString()}
    `,
    metadata: { orderId: order.orderId },
  }));
}

module.exports = { loadOrderDocuments };

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✂️ 2. Split Data into Chunks

We use LangChain's text splitter to break content into manageable pieces.

// backend/langchain/splitter.js
const { RecursiveCharacterTextSplitter } = require('@langchain/community/text_splitter');

async function splitDocuments(documents) {
  const splitter = new RecursiveCharacterTextSplitter({
    chunkSize: 500,
    chunkOverlap: 50,
  });

  return await splitter.splitDocuments(documents);
}

module.exports = { splitDocuments };

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🔁 3. Embed & Store in Pinecone

Now we’ll process and store the chunks as vectors.

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

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

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

  await PineconeStore.fromDocuments(chunks, embeddings, {
    pineconeIndex: index,
  });

  console.log("Chunks stored in Pinecone.");
}

module.exports = { storeChunksInPinecone };

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🧪 4. Pipeline Runner

Let’s put it all together:

// backend/langchain/pipeline.js
const { loadOrderDocuments } = require('./loadOrders');
const { splitDocuments } = require('./splitter');
const { storeChunksInPinecone } = require('./storeChunks');

async function runLangChainPipeline() {
  const docs = await loadOrderDocuments();
  const chunks = await splitDocuments(docs);
  await storeChunksInPinecone(chunks);
}

runLangChainPipeline();

Run the pipeline:

node backend/langchain/pipeline.js

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✅ Next Steps (Part 5)

In the next part, we will:

• Design prompt templates for order-related queries
• Handle multi-turn conversations
• Implement memory using LangChain for context retention

🚀 Stay tuned for Part 5: Designing Conversational Logic!