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 rag_chatbot.py and define our RAGChatbot class:

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1from document_processor import DocumentProcessor
2from chat_engine import ChatEngine
3
4class RAGChatbot:
5    def __init__(self):
6        self.document_processor = DocumentProcessor()
7        self.chat_engine = ChatEngine()

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 upload_document, which will handle document processing:

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1def upload_document(self, file_path):
2    """Upload and process a document"""
3    try:
4        self.document_processor.process_document(file_path)
5        return "Document successfully processed."
6    except ValueError as e:
7        return f"Error: {str(e)}"

This method serves as a wrapper around our document processor's process_document 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 raise a ValueError. Our upload_document method catches this exception and returns a user-friendly error message.

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

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1def reset_documents(self):
2    """Reset the document processor"""
3    self.document_processor.reset()
4    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.

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 send_message method:

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1def send_message(self, message):
2    """Send a message to the chatbot and get a response"""
3    # Retrieve relevant document chunks based on the user's query
4    relevant_docs = self.document_processor.retrieve_relevant_context(message)
5
6    # Initialize an empty string for the context
7    context = ""
8
9    # Loop through each relevant document
10    for doc in relevant_docs:
11        # Extract the source from metadata, defaulting to 'unknown' if not available
12        source = doc.metadata.get('source', 'unknown')
13        # Extract the content of the document
14        content = doc.page_content
15        # Append the source and content to the context string
16        context += f"Source: {source}\n{content}\n\n"
17
18    # Send the user's message along with the context to the chat engine
19    return self.chat_engine.send_message(message, context)

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 reset_documents, but we also need a way to reset the conversation history:

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1def reset_conversation(self):
2    """Reset the conversation history"""
3    self.chat_engine.reset_conversation()
4    return "Conversation history has been reset."

This method simply calls the reset_conversation 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:

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1def reset_all(self):
2    """Reset both conversation and documents"""
3    self.reset_conversation()
4    self.reset_documents()
5    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 reset_conversation and reset_documents, 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:

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1from rag_chatbot import RAGChatbot
2
3# Initialize the RAG chatbot
4chatbot = RAGChatbot()
5
6# Upload a document
7result = chatbot.upload_document("data/a_scandal_in_bohemia.pdf")
8print(result)
9
10# Send a message about the document
11query = "What is the main mystery in the story?"
12response = chatbot.send_message(query)
13print(f"\nQuestion: {query}")
14print(f"Answer: {response}")

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

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1Document successfully processed.
2
3Question: What is the main mystery in the story?
4Answer: 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 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:

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1# Reset everything
2result = chatbot.reset_all()
3print(result)
4
5# Try asking about Sherlock Holmes
6final_query = "Tell me about Sherlock Holmes."
7response = chatbot.send_message(final_query)
8print(f"\nQuestion: {final_query}")
9print(f"Answer: {response}")

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

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1Both conversation history and document knowledge have been reset.
2
3Question: Tell me about Sherlock Holmes.
4Answer: I don't have enough information in the provided context to answer this question.

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 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

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 implemenet and test our RAG chatbot.

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