Integrating Components for a Complete RAG Chatbot

Welcome to the third unit of our course on building a RAG-powered chatbot! In the previous units, you learned how to build two essential components in TypeScript: a document processor for retrieving relevant information and a chat engine for managing user conversations. Now, it’s time to bring these components together and create a complete Retrieval-Augmented Generation (RAG) system.

In this lesson, you’ll integrate your document processor and chat engine into a unified RAGChatbot class. This integration will provide a seamless experience where users can upload documents, ask questions, and receive informed responses based on the document content. By the end of this lesson, you’ll have a fully functional, type-safe RAG chatbot that can answer questions about any documents you provide.

Let’s get started!

The first step is to create a new class that serves as the main interface for your RAG chatbot. This class will coordinate between the document processor and chat engine components you’ve already built.

Let’s create a new file called RAGChatbot.ts and define our RAGChatbot class using TypeScript best practices:

import DocumentProcessor from './document_processor';
import ChatEngine from './chat_engine';

class RAGChatbot {
    private documentProcessor: DocumentProcessor;
    private chatEngine: ChatEngine;

    constructor() {
        this.documentProcessor = new DocumentProcessor();
        this.chatEngine = new ChatEngine();
    }
}

export default RAGChatbot;

We use the private access modifier to encapsulate the documentProcessor and chatEngine properties, ensuring they can only be accessed within the class. This approach leverages TypeScript’s type safety and encapsulation, making your code more robust and maintainable.

This architecture follows the principle of separation of concerns:

• The DocumentProcessor handles document loading, chunking, embedding, and retrieval.
• The ChatEngine manages conversation flow and language model interactions.
• The RAGChatbot coordinates between these components and provides a unified, type-safe interface.

With this structure, you can easily update or extend individual components without affecting the overall system.

Now that you have your class structure, let’s implement document management functionality. The first method to add is uploadDocument, which will handle document processing. In TypeScript, you’ll add type annotations to method parameters and return types for additional safety:

async uploadDocument(filePath: string): Promise<string> {
    // Upload and process a document
    try {
        await this.documentProcessor.processDocument(filePath);
        return `Document successfully processed.`;
    } catch (error) {
        // Type guard to ensure error is an instance of Error
        return `Error: ${error instanceof Error ? error.message : String(error)}`;
    }
}

This method serves as a wrapper around our document processor's processDocument method, but with added error handling. If the document processor encounters an issue (such as an unsupported file format, a corrupted file, or a file that is too large to process), it will throw an error. Our uploadDocument method catches this exception and returns a user-friendly error message.

Let’s also implement a method to reset the document knowledge, again using TypeScript’s type annotations:

resetDocuments(): string {
    // Reset the document processor
    this.documentProcessor.reset();
    return "Document knowledge has been reset.";
}

This method calls the reset method of our document processor, which clears the vector store. This is useful when users want to start fresh with a new set of documents or when they want to remove previously processed documents from the chatbot's knowledge.