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:

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:

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:

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.

The core of your RAG chatbot is the message processing pipeline, which connects user queries to document retrieval and response generation. Let’s implement the sendMessage method with full type safety:

This method implements the core RAG workflow:

1. It takes a user message as input.
2. It uses the document processor to retrieve relevant document chunks based on the message.
3. It builds a context string that includes both the content of each document chunk and its source.
4. It sends the original message and the retrieved context to the chat engine.
5. It returns the response from the chat engine.

The magic of RAG happens in this method. When a user asks a question, the system automatically searches through all processed documents to find relevant information. This relevant context, along with source attribution, is then provided to the language model along with the user's question, allowing it to generate an informed response based on the document content. Including the source information enhances transparency by allowing the model to reference where the information came from. If no relevant documents are found, an empty context is provided. In this case, our chat engine (as we designed it in the previous lesson) will inform the user that it doesn't have enough information to answer the question.

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:

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:

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:

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

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:

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

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!