Welcome to the first lesson of our course on building a RAG-powered chatbot with Go! In this course, we'll create 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 (such as 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 struct that will handle all our document processing needs. This struct will encapsulate the functionality for loading, processing, and retrieving information from documents using LangChain Go packages.

Let's start by setting up the basic structure of our DocumentProcessor struct:

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

• ChunkSize: This determines how large each document chunk will be (measured in characters). A common starting point is around 1000 characters, 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.
• Embedder: This will hold our embedder instance from LangChain Go, which converts text into vector representations.
• VectorStore: This will hold our vector store from the memstore package, which efficiently stores and retrieves document embeddings.

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 struct set up, let's implement the methods for loading documents and splitting them into chunks. We'll use LangChain Go's document loaders and text splitters for this purpose.

First, we'll create a method to load documents from PDF files:

This method uses LangChain Go's documentloaders.NewPDF to load PDF files. The loader automatically extracts text from the PDF and returns it as a slice of schema.Document objects. You could easily extend this to support other file types by using different loaders like documentloaders.NewText for plain text files.

Next, let's implement the method that will process a document and add it 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 performs a similarity search in our vector store using the SimilaritySearch method from the memstore package. This method:

1. Converts the query into an embedding using our embedder
2. Computes similarity scores between the query embedding and all stored document embeddings
3. Returns the k most similar document chunks

If we haven't processed any documents yet, we simply return an empty slice.

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

This method simply sets VectorStore to nil, effectively clearing our knowledge base. This can be useful if you want to start fresh with a new set of documents or when testing different document sets.

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

1. Initializes our document processor with an embedder
2. Processes a PDF document
3. Retrieves relevant context for a query
4. Displays the retrieved chunks

Here's the complete example:

This example demonstrates the complete document processing workflow:

1. We initialize an OpenAI LLM client configured for embeddings using the text-embedding-3-small model
2. We create an embedder by wrapping the LLM client
3. We initialize our DocumentProcessor with the embedder and our desired chunk settings
4. We process a PDF document, which loads it, splits it into chunks, and stores the embeddings

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

• Create a DocumentProcessor struct that encapsulates document processing functionality
• Load documents from PDF files using LangChain Go's document loaders
• Split documents into manageable chunks with appropriate overlap using text splitters
• Generate embeddings and store them in a vector store using the memstore package
• Retrieve relevant context for user queries using similarity search

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!