Tired of sifting through endless documentation to find answers? What if you could just ask your docs a question and get an instant, accurate response? That's exactly what a "Doc Chatbot" can do! In the age of AI, we can build smart assistants that understand your questions and pull answers directly from your product's documentation.
This guide will walk you through building your own Doc Chatbot using powerful yet accessible tools like OpenAI and Next.js.
We'll create a chatbot that can answer questions based on your specific documentation files (like Markdown or plain text). Here’s a quick look at the core technologies we'll use:
Let's break down how this smart assistant works!
Our chatbot uses a clever technique called Retrieval Augmented Generation (RAG). Imagine this: instead of the AI making up answers, it first looks up relevant information from your docs, and then uses only that information to give you a precise answer. This helps prevent the AI from "hallucinating" (making things up!).
Your documents (like Markdown files in a `docs` folder) are the heart of our chatbot's knowledge. We'll break these files into smaller pieces, called "chunks."
OpenAI's "embedding" models (like `text-embedding-ada-002`) are like magicians! They take a piece of text and turn it into a list of numbers (called a "vector"). Texts with similar meanings will have vectors that are "close" to each other in this number space. This is how we find relevant information!
import { OpenAI } from 'openai';
const openai = new OpenAI();
async function getEmbedding(text) {
const response = await openai.embeddings.create({
model: "text-embedding-ada-002",
input: text,
});
return response.data[0].embedding; // This is our 'smart' number list!
}
We need a special database to store these "smart" text numbers (vectors) and find them quickly.
Our database table for document chunks will look something like this:
import { pgTable, text, vector } from 'drizzle-orm/pg-core';
export const documentChunks = pgTable('document_chunks', {
id: text('id').primaryKey(),
content: text('content'), // The actual text chunk
embedding: vector('embedding', { dimensions: 1536 }), // Our 'smart' numbers!
sourcePath: text('source_path'), // Where did this text come from?
});
We use Next.js 14 for the entire website. It's great for building both the user interface (what you see) and the API (how the website talks to our AI logic). Tailwind CSS makes designing a modern, clean chat interface a breeze.
Before your chatbot can answer questions, it needs to "read" and understand your documentation.
// Simplified ingestion logic
import { OpenAI } from 'openai';
import { db } from '@/db'; // Our database connection
import { documentChunks } from '@/db/schema'; // Our schema defined above
const openai = new OpenAI();
async function ingestChunk(chunkText, chunkPath) {
const embedding = await getEmbedding(chunkText); // Get smart numbers
await db.insert(documentChunks).values({
id: crypto.randomUUID(), // Unique ID for each chunk
content: chunkText,
embedding: embedding,
sourcePath: chunkPath,
});
console.log(`Ingested: ${chunkPath}`);
}
// Imagine a loop here that reads your docs and calls ingestChunk for each piece.
When a user asks a question:
This process ensures your chatbot answers are always based on your actual documentation, making it very reliable!
This project is open-source and ready for you to try! You can find all the code and detailed setup instructions on GitHub.
Explore the `bruno-chatbot` GitHub repository
Imagine a world where finding information in your documentation is as simple as asking a question. By combining OpenAI's intelligence, Neon's powerful database, and Next.js's modern web capabilities, you can transform your static documents into a dynamic, smart resource.
Give it a try, explore the code, and make your documentation work harder for you!
Happy chatting!