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.

To complete our RAG chatbot, let's add some system management features that will help users control the state of the chatbot. We've already implemented resetDocuments, but we also need a way to reset the conversation history:

resetConversation(): string {
    // Reset the conversation history
    this.chatEngine.resetConversation();
    return "Conversation history has been reset.";
}

This method simply calls the resetConversation method of our chat engine, which clears the conversation history while preserving the system message. This is useful when users want to start a new conversation without affecting the document knowledge.

Finally, let's add a method to reset both the conversation history and document knowledge:

resetAll(): string {
    // Reset both conversation and documents
    this.resetConversation();
    this.resetDocuments();
    return "Both conversation history and document knowledge have been reset.";
}

This method provides a convenient way to completely reset the chatbot's state. It calls both resetConversation and resetDocuments, effectively returning the chatbot to its initial state.

Now that we've built our integrated RAG chatbot, let's test it by uploading a document and asking a question about it:

import RAGChatbot from './RAGChatbot';

// Initialize the RAG chatbot
const chatbot = new RAGChatbot();

// Upload a document
const result: string = await chatbot.uploadDocument("data/the_adventure_of_the_blue_carbuncle.pdf");
console.log(result);

// Send a message and get a response
const query: string = "What is the main mystery in the story?";
const response: string = await chatbot.sendMessage(query);
console.log(`\nQuestion: ${query}`);
console.log(`Answer: ${response}`);

When you run this code, you’ll see output similar to:

Document successfully processed.

Question: What is the main mystery in the story?
Answer: The main mystery in the story is the identity and intentions of the gentleman who is set to visit the character at a quarter to eight o'clock.

This demonstrates how your RAG system retrieves relevant context from the document and uses it to inform the language model’s response.

To verify that your system management features work correctly, let’s test what happens when you reset the chatbot and try to ask about documents that are no longer in its knowledge base:

// Reset everything
const resetResult: string = chatbot.resetAll();
console.log(`\n${resetResult}`);

// Try asking about Sherlock Holmes
const finalQuery: string = "Tell me about Sherlock Holmes.";
const finalResponse: string = await chatbot.sendMessage(finalQuery);
console.log(`\nQuestion: ${finalQuery}`);
console.log(`Answer: ${finalResponse}`);

When you run this code, you’ll see output similar to:

Both conversation history and document knowledge have been reset.

Question: Tell me about Sherlock Holmes.
Answer: I don't have enough information in the provided context to answer this question.

This confirms your reset functionality works as expected, clearing both conversation history and document knowledge. The chatbot is now ready to process new documents and start fresh conversations.

In this lesson, you’ve successfully integrated your document processor and chat engine to create a complete, type-safe RAG chatbot system in TypeScript. You’ve built a RAGChatbot class that coordinates between these components, providing a unified interface for document upload, message processing, and system management.

Your integrated RAG chatbot can:

• Upload and process documents in supported formats.
• Retrieve relevant context from documents based on user queries.
• Generate informed responses using the retrieved context.
• Maintain conversation history for natural interactions.
• Reset conversation history or document knowledge as needed.

By leveraging TypeScript’s type safety, access modifiers, and compile-time error checking, your chatbot is robust, maintainable, and ready for further extension. In the upcoming practice exercises, you’ll have the opportunity to implement and test your RAG chatbot.

Get ready to put your TypeScript knowledge into practice and take your RAG chatbot to the next level!