🤖 Project Overview: What We’re Building

In this tutorial, we’re diving into the practical side of conversational AI by building a full-stack website chatbot using LangChain and OpenAI, entirely on the free tier. This project is designed to help you understand how to integrate large language models into real-world applications without relying on paid tools. By the end, you’ll have a fully functional chatbot that can hold multi-turn conversations directly on your website.

🎯 Chatbot Use Case: Conversational Website Assistant

The bot we’ll build acts like a customer-facing assistant capable of:

  • Holding a conversation (with memory)
  • Answering questions based on previous inputs
  • Responding naturally using an LLM like GPT-3.5 or open-source alternatives
  • Running entirely within a free deployment setup (Railway + Vercel)

Think of it as a starter template for:

  • Support bots for SaaS
  • Product FAQs bots
  • Personal portfolio site assistants
  • E-learning bots for documentation

🧱 Project Stack Breakdown

Here’s the tech stack we’ll use:

Backend

  • Node.js + Express — lightweight API server
  • LangChain (JS) — orchestrates the LLM + memory + chains
  • OpenAI / Hugging Face — for the LLM interface (we’ll support both)
  • BufferMemory — to track past user-bot interactions in memory

Frontend

  • React + TailwindCSS — clean, minimal UI for the chatbot
  • Fetch-based API calls to our backend

Deployment

  • Vercel (Frontend)
  • Railway (Backend)
  • GitHub for version control and CI/CD

💸 Free-Tier Strategy: Cost-Conscious from the Ground Up

This project is built for developers who want to:

  • Avoid vendor lock-in
  • Experiment without needing a credit card
  • Prototype with generous free usage limits

Here’s how we handle common cost-related blockers:

Concern Free-Tier Solution
LLM Usage OpenAI free-tier key or Hugging Face API (text-generation-inference)
Vector DB Chroma - local, embedded vector store
Deployment Vercel (free hobby plan) + Railway (free backend instance)

You’ll be able to run this bot end-to-end for $0/month.

🧩 Architecture Preview

The high-level flow looks like this:

User Input -> React Frontend -> Express API -> LangChain LLM Chain -> LLM Response -> Back to User
  • Memory is preserved in the backend per session (in-process for now)
  • Optional file upload uses LangChain’s RAG tools with Chroma (all local)

📁 Folder Structure (Planned)

We’ll structure our project clearly for modularity:

/chatbot-openai-langchain-starter
|-- client/            # React frontend
|-- server/            # Express + LangChain backend
|   |-- routes/
|   |-- llm/
|   |-- memory/
|-- .env               # For API keys
|-- README.md          # Setup instructions

🚀 Next Steps

Now that we know what we’re building and how everything fits together, it’s time to get our hands dirty. In the next section, we’ll:

  • Scaffold the backend
  • Set up LangChain and connect to an LLM
  • Write our first API route

Let’s go build a chatbot, shall we? 🛠️

⚙️ Setting Up the Backend: LangChain + Express API

Let’s kick things off by building the backend for our chatbot. We’ll use Node.js + Express as our server and plug in LangChain to handle language model interactions. This section walks you through the exact steps — code-first, no assumptions. Ready to build? Let’s go 🛠️

📁 Project Structure

Before diving into code, here’s how we’ll organize our project. Keeping a clean folder structure helps with scalability and debugging.

chatbot-openai-langchain-starter/
├── server/                 # Backend API
│   ├── index.js            # Main entry point
│   ├── routes/             # API routes
│   │   └── chat.js         # Chat endpoint logic
│   ├── llm/                # LangChain-related logic
│   │   └── langchain.js    # Chain + memory config
│   └── .env                # Environment variables
├── client/                 # Frontend (will be added later)
└── README.md               # Documentation

🧩 Step 1: Initialize the Project

Start by setting up your project folder and backend workspace:

mkdir chatbot-openai-langchain-starter && cd chatbot-openai-langchain-starter
mkdir server && cd server
npm init -y

This sets up a new Node.js project inside the server directory.

📦 Step 2: Install Dependencies

We need these packages:

  • express: Web server
  • dotenv: Manage environment variables
  • cors: Allow frontend to call backend
  • langchain: Power LLM chains and memory
  • openai: SDK for calling OpenAI models

Install everything in one go:

npm install express dotenv cors langchain openai

Enable ES module support by editing package.json:

{
  "type": "module"
}

This allows us to use import/export instead of require().

🛠️ Step 3: Basic Express Server

Now we’ll set up our basic API server that will act as a middleman between the frontend and the LLM.

Create server/index.js:

import express from "express";
import dotenv from "dotenv";
import cors from "cors";
import chatRoute from "./routes/chat.js";

dotenv.config();

const app = express();
const PORT = process.env.PORT || 3001;

app.use(cors());
app.use(express.json());

app.use("/chat", chatRoute);

app.get("/", (req, res) => {
  res.send("Chatbot backend is running");
});

app.listen(PORT, () => {
  console.log(`Server listening on http://localhost:${PORT}`);
});

This initializes the server and mounts our chatbot API at /chat.

🧠 Step 4: Create the Chat Route

We’ll now define the POST route where incoming user messages are received and passed to the LLM.

Create server/routes/chat.js:

import express from "express";
import { runChat } from "../llm/langchain.js";

const router = express.Router();

router.post("/", async (req, res) => {
  const { message } = req.body;
  if (!message) {
    return res.status(400).json({ error: "Message is required" });
  }
  try {
    const response = await runChat(message);
    res.json({ response });
  } catch (err) {
    console.error("Chat error:", err);
    res.status(500).json({ error: "Something went wrong" });
  }
});

export default router;

This is our /chat endpoint — it accepts a user message, passes it to LangChain, and returns the response.

🤖 Step 5: LangChain Integration

This is where we bring in the power of LLMs.

Create server/llm/langchain.js:

import { ChatOpenAI } from "langchain/chat_models/openai";
import { ConversationChain } from "langchain/chains";
import { BufferMemory } from "langchain/memory";

const model = new ChatOpenAI({
  temperature: 0.7,
  openAIApiKey: process.env.OPENAI_API_KEY,
});

const memory = new BufferMemory();
const chain = new ConversationChain({ llm: model, memory });

export async function runChat(input) {
  const result = await chain.call({ input });
  return result.response;
}

Here’s what this setup does:

  • Connects to OpenAI using ChatOpenAI
  • Adds short-term memory via BufferMemory
  • Wraps both in a ConversationChain, which is a dialogue loop
  • Exposes runChat() that accepts user input and returns a response

🔐 Step 6: Setup Environment Variables

Inside your server/ folder, create a .env file:

OPENAI_API_KEY=your_openai_key_here

This keeps sensitive credentials out of your source code.

✅ Step 7: Run and Test Your Backend

Start your server:

node index.js

Use Postman or curl to test the chat route:

curl -X POST http://localhost:3001/chat \
     -H "Content-Type: application/json" \
     -d '{"message": "Hello, who are you?"}'

You should receive a response from your bot 🧠💬

With this, your chatbot backend is fully functional. You now have:

  • An Express API that routes messages
  • LangChain hooked to OpenAI with memory
  • A clean project foundation to expand on

Next, we’ll look into improving context awareness by expanding how memory works in LangChain 🧠

🧠 Adding Memory with LangChain: Context Awareness

Most real conversations rely on memory. Without it, every new message would feel like talking to a goldfish 🐟. In this section, we’ll make our chatbot context-aware using LangChain’s memory modules.

By the end of this section, your bot will:

  • Understand follow-up questions
  • Refer back to previous user messages
  • Feel more natural and intelligent 🤖

🔍 What is BufferMemory?

BufferMemory is one of LangChain’s simplest memory classes. It keeps a running history of the chat in memory. This is suitable for most short-lived chatbot sessions.

It stores conversation as a list of alternating messages (user → bot → user → bot…) and passes the entire history into the prompt every time. No database needed.

🧩 Step 1: Refactor the LangChain Setup

Let’s update server/llm/langchain.js to make memory a bit more dynamic.

// server/llm/langchain.js
import { ChatOpenAI } from "langchain/chat_models/openai";
import { ConversationChain } from "langchain/chains";
import { BufferMemory } from "langchain/memory";

// 🧠 Create memory per session/user
const memoryStore = new Map();

function getChain(sessionId) {
  if (!memoryStore.has(sessionId)) {
    const memory = new BufferMemory();
    const model = new ChatOpenAI({
      temperature: 0.7,
      openAIApiKey: process.env.OPENAI_API_KEY,
    });
    const chain = new ConversationChain({ llm: model, memory });
    memoryStore.set(sessionId, chain);
  }
  return memoryStore.get(sessionId);
}

export async function runChat(input, sessionId = "default") {
  const chain = getChain(sessionId);
  const result = await chain.call({ input });
  return result.response;
}

🧠 Explanation:

  • We use a simple Map object to track memory by sessionId
  • Each session gets its own ConversationChain and BufferMemory
  • You can eventually replace this with Redis or DB-backed memory for production

🛠️ Step 2: Update the Chat Route to Use Sessions

Update server/routes/chat.js to accept a session ID:

// server/routes/chat.js
import express from "express";
import { runChat } from "../llm/langchain.js";

const router = express.Router();

router.post("/", async (req, res) => {
  const { message, sessionId } = req.body;
  if (!message) {
    return res.status(400).json({ error: "Message is required" });
  }
  try {
    const response = await runChat(message, sessionId);
    res.json({ response });
  } catch (err) {
    console.error("Chat error:", err);
    res.status(500).json({ error: "Something went wrong" });
  }
});

export default router;

Now, the API can track multiple conversations at once by passing different sessionIds.

You can test it like this:

curl -X POST http://localhost:3001/chat \
     -H "Content-Type: application/json" \
     -d '{"message": "Hello, who are you?", "sessionId": "abc123"}'

🔍 Debugging Memory State

Want to peek under the hood? Log the memory content by modifying runChat():

console.log("\n--- Chat History ---\n", memory.chatHistory);

This will help you debug and understand how memory evolves.

✅ Result: Multi-Turn Chatbot

You’ve now built a chatbot that:

  • Tracks conversation per session
  • Supports multiple concurrent users
  • Handles context like a pro

In the next section, we’ll switch gears and build a beautiful frontend interface in React to talk to our bot 💬

💬 Building the React Frontend Chat Interface

With the backend running smoothly, it’s time to give our chatbot a face! In this section, we’ll build a sleek and responsive React + TailwindCSS frontend that connects to our Express + LangChain backend.

By the end, you’ll have a fully functional chat UI with:

  • Chat history
  • Message input box
  • Bot typing indicator
  • API integration

🧩 Step 1: Create React App with Vite

Navigate to the project root and scaffold your frontend:

cd ..
npm create vite@latest client -- --template react
cd client
npm install

Then install Tailwind CSS:

npm install -D tailwindcss postcss autoprefixer
npx tailwindcss init -p

Update tailwind.config.js:

/** @type {import('tailwindcss').Config} */
export default {
  content: ["./index.html", "./src/**/*.{js,ts,jsx,tsx}"],
  theme: {
    extend: {},
  },
  plugins: [],
};

In index.css, replace with:

@tailwind base;
@tailwind components;
@tailwind utilities;

🛠️ Step 2: Build the Chat UI

Create a clean layout in src/App.jsx:

import { useState } from "react";

function App() {
  const [messages, setMessages] = useState([]);
  const [input, setInput] = useState("");
  const [loading, setLoading] = useState(false);
  const sessionId = "demo-session";

  const sendMessage = async () => {
    if (!input.trim()) return;
    const userMessage = { sender: "user", text: input };
    setMessages([...messages, userMessage]);
    setInput("");
    setLoading(true);

    try {
      const res = await fetch("http://localhost:3001/chat", {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({ message: input, sessionId }),
      });
      const data = await res.json();
      const botMessage = { sender: "bot", text: data.response };
      setMessages((prev) => [...prev, botMessage]);
    } catch (err) {
      alert("Something went wrong");
    } finally {
      setLoading(false);
    }
  };

  const handleKeyPress = (e) => {
    if (e.key === "Enter") sendMessage();
  };

  return (
    <div className="min-h-screen bg-gray-100 flex flex-col items-center justify-center p-4">
      <div className="w-full max-w-xl bg-white rounded-xl shadow p-4 space-y-4">
        <div className="overflow-y-auto h-96 border p-3 rounded">
          {messages.map((msg, i) => (
            <div
              key={i}
              className={`mb-2 ${
                msg.sender === "user" ? "text-right" : "text-left"
              }`}
            >
              <span
                className={`inline-block px-3 py-2 rounded-lg ${
                  msg.sender === "user" ? "bg-blue-200" : "bg-gray-200"
                }`}
              >
                {msg.text}
              </span>
            </div>
          ))}
          {loading && (
            <div className="text-sm italic text-gray-500">Bot is typing...</div>
          )}
        </div>
        <div className="flex items-center gap-2">
          <input
            className="flex-1 border rounded px-3 py-2"
            placeholder="Type a message..."
            value={input}
            onChange={(e) => setInput(e.target.value)}
            onKeyDown={handleKeyPress}
          />
          <button
            onClick={sendMessage}
            className="bg-blue-500 text-white px-4 py-2 rounded"
          >
            Send
          </button>
        </div>
      </div>
    </div>
  );
}

export default App;

🔌 Step 3: Connect to Backend API

Make sure your backend (localhost:3001) is running when you test this. We use the fetch() API to send the message and receive a response.

💡 Dev tip: You can use Vite proxy for CORS-free development by updating vite.config.js later.

✅ Result: Interactive Chat UI

You now have a frontend that:

  • Tracks messages in React state
  • Sends input to your chatbot API
  • Displays both user and bot replies
  • Shows loading feedback

In the next part, we’ll deploy this using Vercel and Railway so others can use your chatbot online 🚀

🚀 Free Deployment: Vercel (Frontend) + Railway (Backend)

Now that your chatbot works locally, let’s take it live — for free! We’ll use Vercel to host the frontend and Railway to host the backend. Both have generous free tiers and integrate smoothly with GitHub.

You’ll end up with:

  • A live website for your chatbot UI (Vercel)
  • A live API server that handles LangChain + OpenAI logic (Railway)

🧩 Step 1: Push Your Code to GitHub

First, commit and push your project to a GitHub repo:

git init
git add .
git commit -m "Initial chatbot app"
git remote add origin https://github.com/yourusername/chatbot-openai-langchain-starter.git
git push -u origin main

🛠️ Step 2: Deploy Backend to Railway

  1. Go to https://railway.app
  2. Click New Project → Deploy from GitHub Repo
  3. Select your repo and choose the server/ directory as root

📁 Railway Settings

  • Root Directory: server
  • Start Command: node index.js

🔐 Add Environment Variables

  • Key: OPENAI_API_KEY
  • Value: your OpenAI key

Railway will detect Express and auto-deploy it. After deployment, you’ll get a public URL like:

https://chatbot-api-production.up.railway.app

🎨 Step 3: Deploy Frontend to Vercel

  1. Go to https://vercel.com
  2. Click Add New Project and select your GitHub repo
  3. Choose the client/ folder as the project root

🛠 Vercel Build Settings

  • Framework Preset: Vite
  • Output Directory: dist

🔌 Add Environment Variable (Optional)

If you’re proxying API URLs, you can set:

  • VITE_API_URL=https://your-backend-url/chat

⚙️ Optional: Fix CORS on Backend

If your frontend can’t reach your backend, you might need to tweak CORS:

// server/index.js
app.use(cors({ origin: "https://your-frontend.vercel.app" }));

Or allow all (for testing only):

app.use(cors());

🌐 Result: Public Chatbot App

You now have:

  • Live frontend at https://your-chatbot.vercel.app
  • Live backend API at https://your-chatbot-api.railway.app

Test it in the browser, share with friends, or embed it in your portfolio!

Next up: we’ll add optional file upload + document Q&A using Retrieval-Augmented Generation 📄🧠

📄 Optional: Add File Upload + RAG (Retrieval-Augmented Generation)

Want your chatbot to answer questions based on a user-uploaded PDF? This is where RAG (Retrieval-Augmented Generation) comes in.

We’ll enable:

  • Uploading a PDF
  • Parsing its text
  • Embedding with OpenAI or Hugging Face
  • Storing + searching in a local Chroma vector store
  • Querying the doc contextually

All of this works on a local, free setup — no paid vector DBs needed! 🧠

📦 Step 1: Install Additional Dependencies

In your server/ folder:

npm install multer pdf-parse

These packages handle file uploads and PDF parsing.

📁 Step 2: Add Upload Endpoint

Create server/routes/upload.js:

import express from "express";
import multer from "multer";
import pdf from "pdf-parse";
import fs from "fs";
import path from "path";

const upload = multer({ dest: "uploads/" });
const router = express.Router();

router.post("/", upload.single("file"), async (req, res) => {
  const filePath = req.file.path;
  try {
    const dataBuffer = fs.readFileSync(filePath);
    const parsed = await pdf(dataBuffer);
    fs.unlinkSync(filePath); // delete uploaded file

    res.json({ text: parsed.text });
  } catch (err) {
    console.error("PDF parse error:", err);
    res.status(500).json({ error: "Failed to parse PDF" });
  }
});

export default router;

In index.js:

import uploadRoute from "./routes/upload.js";
app.use("/upload", uploadRoute);

🧠 Step 3: Create Vector Store + QA Chain

Create server/llm/retriever.js:

import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { Chroma } from "langchain/vectorstores/chroma";
import { RetrievalQAChain } from "langchain/chains";
import { ChatOpenAI } from "langchain/chat_models/openai";
import { TextLoader } from "langchain/document_loaders/fs/text";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import fs from "fs";

let vectorStore;

export async function createKnowledgeBase(text) {
  fs.writeFileSync("uploads/docs.txt", text);

  const loader = new TextLoader("uploads/docs.txt");
  const docs = await loader.load();

  const splitter = new RecursiveCharacterTextSplitter({
    chunkSize: 1000,
    chunkOverlap: 200,
  });
  const chunks = await splitter.splitDocuments(docs);

  const embeddings = new OpenAIEmbeddings();
  vectorStore = await Chroma.fromDocuments(chunks, embeddings);
}

export async function askDocQuestion(query) {
  if (!vectorStore) throw new Error("No knowledge base loaded");
  const model = new ChatOpenAI();
  const chain = RetrievalQAChain.fromLLM(model, vectorStore.asRetriever());
  const result = await chain.call({ query });
  return result.text;
}

🔌 Step 4: Create Chat Route for RAG

In server/routes/chat-doc.js:

import express from "express";
import { askDocQuestion } from "../llm/retriever.js";

const router = express.Router();

router.post("/", async (req, res) => {
  const { message } = req.body;
  if (!message) return res.status(400).json({ error: "Message required" });
  try {
    const response = await askDocQuestion(message);
    res.json({ response });
  } catch (err) {
    console.error("RAG chat error:", err);
    res.status(500).json({ error: "RAG failed" });
  }
});

export default router;

Add this to index.js:

import chatDocRoute from "./routes/chat-doc.js";
app.use("/chat-doc", chatDocRoute);

✅ Final Flow

  1. Upload PDF → Extract + embed text
  2. Store in Chroma (in-memory vector DB)
  3. Ask questions → Retrieve relevant chunks → LLM answers based on them

Next, we’ll polish UX with reset, typing indicators, and error boundaries 💬✨

✨ UX Polish: Typing Indicators, Reset Button, and Error Handling

Now that our chatbot is feature-rich, let’s take care of the user experience (UX) — the part that makes it feel truly alive.

We’ll improve:

  • ✅ Visual feedback (“Bot is typing…”)
  • 🗑️ Reset chat option
  • 🚨 Error messages

These polish points might seem small, but they dramatically enhance usability and make your bot feel professional.

🎨 Step 1: Add Typing Indicator

Update src/App.jsx to show a message while the bot is generating a response.

Here’s the modified React logic:

// src/App.jsx
const [loading, setLoading] = useState(false);

...

{messages.map((msg, i) => (
  <div key={i} className={`mb-2 ${msg.sender === 'user' ? 'text-right' : 'text-left'}`}>
    <span className={`inline-block px-3 py-2 rounded-lg ${msg.sender === 'user' ? 'bg-blue-200' : 'bg-gray-200'}`}>
      {msg.text}
    </span>
  </div>
))}
{loading && <div className="text-left text-sm italic text-gray-500">Bot is typing...</div>}

Now every time you send a message, the UI gives immediate feedback that something’s happening.

🔄 Step 2: Add Reset Button

Let’s allow users to clear the chat history:

// Inside return()
<button
  onClick={() => setMessages([])}
  className="text-sm text-red-500 underline ml-auto block mt-1"
>
  Clear Chat
</button>

Add this just below the chat history container. It simply resets the messages array.

🧯 Step 3: Add Error Handling

Wrap your fetch() call in a try/catch, and show alerts or inline error messages:

try {
  const res = await fetch("http://localhost:3001/chat", {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({ message: input, sessionId }),
  });
  if (!res.ok) throw new Error("API failed");
  const data = await res.json();
  const botMessage = { sender: "bot", text: data.response };
  setMessages((prev) => [...prev, botMessage]);
} catch (err) {
  console.error(err);
  setMessages((prev) => [
    ...prev,
    { sender: "bot", text: "⚠️ Something went wrong. Please try again." },
  ]);
}

This way, even if something breaks, the user isn’t left staring at a frozen screen.

💄 Result: A Smooth, Polished UX

With these enhancements:

  • The bot feels responsive
  • Users can reset the chat any time
  • Errors are caught and communicated clearly

In the final article sections, we’ll look at prompt engineering, LangChain debugging, and wrap up the repo and deployment ✨

🧪 Bonus Tips: LangChain Debugging + Prompt Engineering

To truly master chatbot development, you need two extra skills:

  • 🛠 Debugging LangChain chains
  • 🎨 Crafting effective prompts

These skills can help you build more reliable, intelligent, and tailored bots — especially when handling tricky customer use-cases.

🧭 Step 1: Enable LangChain Debug Mode

LangChain can print out internal chain activity using a built-in global flag.

Add this to your entry point (e.g. index.js):

process.env.LANGCHAIN_HANDLER = "console";

Alternatively, in code:

import { setHandler } from "langchain/callbacks";
setHandler("console");

Now when you run your bot, you’ll see:

  • Prompts being generated
  • Token counts
  • LLM call/response timings

Super useful for tracing what went wrong or tuning performance.

✍️ Step 2: Use Prompt Templates

LangChain lets you define reusable, fill-in-the-blank prompts.

Here’s how you could rewrite langchain.js using a custom system prompt:

import { PromptTemplate } from "langchain/prompts";
import { LLMChain } from "langchain/chains";
import { ChatOpenAI } from "langchain/chat_models/openai";
import { BufferMemory } from "langchain/memory";

const prompt = PromptTemplate.fromTemplate(`
You are a helpful website assistant. Always answer clearly.

Conversation so far:
{history}

User: {input}
Assistant:
`);

const memory = new BufferMemory();
const llm = new ChatOpenAI({
  temperature: 0.7,
  openAIApiKey: process.env.OPENAI_API_KEY,
});

const chain = new LLMChain({ prompt, llm, memory });

export async function runChat(input) {
  const result = await chain.call({ input });
  return result.text;
}

This gives you much more control over:

  • Personality
  • Format
  • Tone

👤 Step 3: Try Persona Prompts

Make your assistant adopt a role, like:

You are a sarcastic AI assistant who gives witty answers.
You remember the user's favorite color and always mention it.

Or for serious use cases:

You are a financial assistant who gives concise and factual answers.
If unsure, say "I don't know".

Use PromptTemplate.fromTemplate(...) to inject these styles into your chain.

🧠 Step 4: Mix with Memory

When using BufferMemory, LangChain automatically fills {history} with past turns. So your prompt templates can smoothly incorporate context-aware behavior.

If you want more control, you can manually access memory too:

console.log(await memory.loadMemoryVariables());

✅ Takeaway

With debugging and prompt design:

  • You can spot problems faster
  • Customize behavior precisely
  • Optimize tokens and cost

In the final section, we’ll wrap up the project and share the GitHub repo 🎉

📦 Final Thoughts + GitHub Repo Setup

You’ve now built a full-stack, context-aware, deployable chatbot with LangChain and OpenAI — and best of all, using only free-tier tools. Let’s wrap up the series by reviewing what you’ve built, finalizing the GitHub repo, and suggesting ways to expand your bot further.

🧱 What You’ve Built

✅ Backend API (Express + LangChain)

  • Session-based memory with BufferMemory
  • Optional RAG (Retrieval-Augmented Generation) with Chroma
  • Robust routing with error handling

✅ Frontend UI (React + Tailwind)

  • Live chat interface with typing indicators
  • Reset and error UX
  • API integration with session-based context

✅ Deployment

  • Backend live on Railway
  • Frontend live on Vercel
  • Connected via environment config

✅ Optional Features

  • Upload PDF → Embed → Ask questions
  • Prompt engineering + persona styling

📁 Recommended Repo Structure

If you haven’t already, organize your files like this:

chatbot-openai-langchain-starter/
├── client/                  # React frontend
│   ├── src/
│   └── ...
├── server/                 # Express + LangChain backend
│   ├── routes/
│   ├── llm/
│   ├── uploads/            # Optional file-based RAG
│   └── index.js
├── .env.example
├── README.md

Include a .env.example like:

OPENAI_API_KEY=your_openai_key_here

📝 Sample README.md Template

Create a friendly README for devs exploring your bot:

# 🤖 Chatbot Starter (LangChain + OpenAI)

A full-stack chatbot using React + Express + LangChain. Context-aware, memory-enabled, and free-tier deployable.

## 🧠 Features

- Chat with OpenAI (GPT-3.5)
- Context memory per session
- Optional: Ask questions on uploaded PDFs
- Free deployment (Vercel + Railway)

## 🚀 Quick Start

```bash
git clone https://github.com/yourusername/chatbot-openai-langchain-starter
cd server && npm install && node index.js
cd ../client && npm install && npm run dev
```

## 🔐 Env Variables

- `OPENAI_API_KEY` (backend)
- `VITE_API_URL` (optional, frontend)

## 📦 Built With

- LangChain JS
- OpenAI
- Express
- React + TailwindCSS
- Railway + Vercel

🌱 Ideas for Extensions

Here are a few ways to take this bot to the next level:

  • 🧑‍💻 Add authentication with Supabase or Clerk
  • 🗣 Use Whisper for voice input
  • 🌍 Make it multilingual with Google Translate API
  • 🧠 Switch to Hugging Face models for self-hosting
  • 📜 Save chat history to a DB (e.g., SQLite or Supabase)

📌 Final Repo Suggestion

Name: chatbot-openai-langchain-starter

Include a clean README.md, environment sample, and clear folder structure. You can even deploy a live demo and link it in your GitHub profile.

Congrats 🎉 — You’ve not just followed a tutorial, you’ve built a deployable, extensible chatbot foundation you can fork, scale, and customize as needed!

See you in the next series — where we’ll dive deeper into advanced memory, agent routing, and multi-modal input 👋

Hey, I’m Darshan Jitendra Chobarkar — a freelance full-stack web developer surviving the caffeinated chaos of coding from Pune ☕💻 If you enjoyed this article (or even skimmed through while silently judging my code), you might like the rest of my tech adventures.

🔗 Explore more writeups, walkthroughs, and side projects at dchobarkar.github.io
🔍 Curious where the debugging magic happens? Check out my commits at github.com/dchobarkar
👔 Let’s connect professionally on LinkedIn

Thanks for reading — and if you’ve got thoughts, questions, or feedback, I’d genuinely love to hear from you. This blog’s not just a portfolio — it’s a conversation. Let’s keep it going 👋

<
Previous Post
Conversational AI: Building Intelligent Chatbots Across Platforms - 02: Anatomy of a Chatbot: From Rules to Transformers
>
Next Post
Conversational AI: Building Intelligent Chatbots Across Platforms - 04: Multi-Turn Conversations and Context Memory