Welcome to the third unit of our course on building a RAG-powered chatbot! In the previous units, we've built two essential components: a document processor that handles the retrieval of relevant information and a chat engine that manages conversations with users. Now, it's time to bring these components together to create a complete Retrieval-Augmented Generation (RAG) system.

In this lesson, we'll integrate our document processor and chat engine into a unified RAGChatbot class. This integration will create a seamless experience where users can upload documents, ask questions about them, and receive informed responses based on the document content. By the end of this lesson, you'll have a fully functional RAG chatbot that can answer questions about any documents you provide. This represents the culmination of our work so far, bringing together retrieval and generation in a practical, user-friendly system.

Let's start building our integrated RAG chatbot!

The first step in our integration is to create a new class that will serve as the main interface for our RAG chatbot. This class will coordinate between the document processor and chat engine components we've already built.

Let's create a new file called RAGChatbot.java and define our RAGChatbot class:

This initialization is straightforward but powerful. We're creating instances of both our DocumentProcessor and ChatEngine classes, which we developed in the previous lessons. This class will serve as the coordinator between these components, handling the flow of information from document processing to context retrieval to conversation management. This design follows the principle of separation of concerns, where each component has a specific responsibility:

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

This architecture makes our system modular and maintainable. If we want to improve our document processing or chat capabilities in the future, we can update the respective components without affecting the overall system.

Now that we have our basic class structure, let's implement the document management functionality. The first method we'll add is uploadDocument, which will handle document processing:

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 IllegalArgumentException. Our uploadDocument method catches this exception and returns a user-friendly error message.

Let's also implement a method to reset the document knowledge:

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 heart of our RAG chatbot is the message processing pipeline, which connects user queries to document retrieval and response generation. Let's implement the sendMessage method:

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 segments based on the message.
3. It builds a context string that includes the content of each relevant segment.
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 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. 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 our RAG system successfully retrieves relevant context from the document and uses it to inform the language model's response.

To verify that our system management features work correctly, let's test what happens when we 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 our reset functionality works as expected, clearing both conversation history and document knowledge. The chatbot has returned to its initial state, ready to process new documents and start fresh conversations.

In this lesson, we've successfully integrated our document processor and chat engine to create a complete RAG chatbot system. We've built a RAGChatbot class that coordinates between these components, providing a unified interface for document upload, message processing, and system management.

Our integrated RAG chatbot can:

• Upload and process documents in various 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.

This represents the culmination of our work in the previous units. We've gone from building individual components to creating a complete, functional RAG system that can answer questions about any documents you provide. In the upcoming practice exercises, you'll have the opportunity to implement and test our RAG chatbot.

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