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Praveen

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

#programming #javascript #ai #langchain

🔗 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.

✅ 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

🧱 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();
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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!

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