Welcome to the first lesson of our course on building a RAG-powered chatbot with Java! In this course, we'll be creating a complete Retrieval-Augmented Generation (RAG) system that can intelligently answer questions based on your documents.

At the heart of any RAG system is the document processor. This component is responsible for taking your raw documents, processing them into a format that can be efficiently searched, and retrieving the most relevant information when a query is made. Think of it as the librarian of your RAG system — organizing information and fetching exactly what you need when you ask for it.

The document processing pipeline we'll build today consists of several key steps:

1. Loading documents from files (like PDFs)
2. Splitting these documents into smaller, manageable chunks
3. Creating vector embeddings for each chunk
4. Storing these embeddings in a vector database
5. Retrieving the most relevant chunks when a query is made

This document processor will serve as the foundation for our RAG chatbot. In later units, we'll build a chat engine that can maintain conversation history and then integrate both components into a complete RAG system. By the end of this course, you'll have a powerful chatbot that can answer questions based on your document collection with remarkable accuracy.

Let's start building our document processor!

First, we need to create a class that will handle all our document processing needs. This class will encapsulate the functionality for loading, processing, and retrieving information from documents.

Let's start by setting up the basic structure of our DocumentProcessor class in Java:

In this constructor, we're setting up several important parameters:

• chunkSize: This determines how large each document chunk will be (measured in characters). We're using 1000 characters as a default, which is a good balance between context size and specificity.
• chunkOverlap: This specifies how much overlap there should be between consecutive chunks. Overlap helps maintain context across chunk boundaries.
• embeddingModel: We're using OpenAI's embedding model to convert our text chunks into vector representations.
• vectorStore: This will hold our vector embeddings in an in-memory store.

These parameters can be adjusted based on your specific needs. For example, if you're working with technical documents where context is crucial, you might want to increase the chunk size and overlap.

Now that we have our class structure set up, let's implement the methods for loading documents and splitting them into chunks.

First, we'll create a method to load documents using LangChain4j's document loaders:

This method checks the file extension and uses the appropriate loader. Currently, we're only supporting PDF files, but you could easily extend this to support other file types by adding more document parsers.

Next, let's implement the method that will process a document and add it to our vector store:

This method does several important things:

1. It loads the document using our loadDocument method.
2. It creates a DocumentByParagraphSplitter with our specified chunk size and overlap.
3. It splits the loaded document into chunks.
4. It generates embeddings for each chunk using our embedding model.
5. It adds all the embeddings and their corresponding chunks to our vector store.

Now that we can process documents and store their embeddings, we need a way to retrieve relevant context when a query is made. This is where the "retrieval" part of RAG comes into play.

Let's implement a method to retrieve relevant document chunks for a given query:

This method takes a query string and a parameter k, which specifies how many chunks to retrieve. It then:

1. Generates an embedding for the query
2. Creates a search request for the top k matches
3. Performs the search in our vector store
4. Extracts the text from each matching segment
5. Returns the list of relevant text chunks

Finally, let's add a utility method to reset our document processor:

This method creates a new empty vector store, effectively clearing our knowledge base. This can be useful if you want to start fresh with a new set of documents.

Now that we've built all the components of our document processor, let's see how to use it in a complete RAG workflow. We'll create a simple example that:

1. Initializes our document processor
2. Processes a PDF document
3. Retrieves relevant context for a query
4. Uses a chat model to generate a response based on this context

Here's the complete example:

This example demonstrates the complete RAG workflow:

1. We process a document and store its chunks in our vector store.
2. We define a query about the document.
3. We retrieve the most relevant chunks for this query.
4. We create a prompt that includes both the query and the retrieved context.
5. We send this prompt to a language model to generate a response.

In this lesson, we've built a powerful document processor for our RAG chatbot using Java and LangChain4j. We've learned how to:

• Create a DocumentProcessor class that encapsulates document processing functionality
• Load documents from PDF files using LangChain4j's document loaders
• Split documents into manageable chunks with appropriate overlap
• Create embeddings for document chunks and store them in a vector store
• Retrieve relevant context for user queries using semantic search
• Integrate our document processor with a chat model for a basic RAG workflow

In the next unit, we'll build on this foundation by creating a chat engine that can maintain conversation history. This will allow our chatbot to have more natural, contextual conversations with users. Eventually, we'll integrate both components into a complete RAG system that can intelligently answer questions based on your documents while maintaining conversational context.

Get ready to practice what you've learned and take your RAG chatbot to the next level!