How to Build a Chatbot With Your Own Data: A Practical Guide to Chatbot System Design

So, your team just asked you to build a chatbot that actually knows about your company's internal data — and you're staring at a blank screen wondering where to begin?

Been there. And in this post, I’ll walk you through how I would approach building a chatbot with your own data, step-by-step — using a simple but powerful chatbot system design.

🔍 What Are We Building?

We’re building a chatbot that not only understands general knowledge, but can also answer company-specific questions using your internal data: documents, product info, processes, FAQs, and more.

🧠 High-Level Chatbot System Design

Here’s what makes up our AI chatbot:

• Client (React UI) – Where users chat with the bot.
• Server (Express) – The middleman, orchestrating the data flow.
• Vector Database (Weaviate) – Where your company’s data lives, in a searchable format.
• LLM API (OpenAI) – The “brain” that crafts responses.

So how do these parts work together to let users query your data?

Let’s start simple.

🤖 Step 1: Build a Chatbot Without Your Own Data

To get the basics down, let’s begin with a general chatbot — one that uses only the LLM’s built-in knowledge.

The flow is simple:

1. The user asks a question.
2. The client sends it to the server using WebSocket (we use sockets so responses can stream in real-time).
3. The server forwards the message to the LLM (e.g. OpenAI).
4. The LLM replies, chunk by chunk.
5. The client displays the answer as it arrives — creating that “typing as you go” feel.

🤔 What’s an LLM?

LLM stands for Large Language Model. Think of it as the brain 🧠 of your chatbot.

You give it some input — like a user question — and it responds with natural-sounding text. The magic is in how it predicts the next word based on the context you give it.

When you're building a chatbot without your own data, the LLM responds using its pre-trained knowledge, like a supercharged autocomplete.

But there’s a catch: it only knows what it was trained on. So if you ask it something specific about your company — like details about the new product you are working on — it won't have the answer.

That's where your own data comes in.

📁 Step 2: Build a Chatbot With Your Own Data

Let’s say your user asks about Product P — a top-secret project you just launched internally. The LLM has zero knowledge of it.

That’s when we bring in the magic sauce: the Vector Database.

🧬 What’s a Vector Database?

Think of it as your company’s smart memory bank. It stores all kinds of internal data — PDFs, Notion docs, videos, you name it — but in a way that can be searched semantically, not just by keywords.

Here’s how it works:

1. Convert your internal data into embeddings using OpenAIEmbeddings.
2. Store those embeddings in Weaviate (or another vector DB).
3. When a user asks something, convert their question into an embedding.
4. Search the vector DB for documents that are semantically similar.

For example, if a user asks about “Project Management,” and you have a PDF called SCRUM Best Practices, the database will likely return it — because the two are conceptually close.

Then you:

• Take those matching documents.
• Add them to your prompt.
• And send that enhanced prompt to the LLM.

This gives the LLM the context it needs to answer accurately — using your own data.

🧪 Tech Stack to Build a Chatbot With Own Data

📦 Tools:

• Client - React
• Server - Express
• Vector DB - Weaviate
• Embeddings - OpenAIEmbeddings
• LLM - OpenAI API

Let’s make this real with a solid tech stack.

🛠 Setup:

1. Use OpenAIEmbeddings to convert your internal files (PDFs, web pages, etc.) to embeddings.
2. Store those embeddings in Weaviate.
3. Establish a WebSocket connection between your React client and Express server.
4. Get an OpenAI API key.

🔁 Flow:

1. User submits a question in the React chat.
2. Express receives it via WebSocket.
3. Express queries Weaviate for relevant documents.
4. It then sends both the user’s question and those docs to OpenAI API (using stream: true).
5. Each token (piece of the answer) is streamed back to React over WebSocket for real-time typing.

🧵 Wrapping Up

We’ve covered the journey from building a basic chatbot to designing a smart one that can:

• Search your internal data using semantic embeddings,
• Incorporate that data into the LLM’s context,
• And deliver accurate, contextual answers to your users.

This is the essence of how to build a chatbot with your own data — and the core of modern chatbot system design.

Thanks for reading! 🙌 If you found this helpful, you might also like:

Ever Heard of This Mysterious Thing Called RAG?

Until next time — happy building! 🚀