Welcome to the first lesson of our course on building a RAG-powered chatbot with LangChain and TypeScript! 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 question 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 question 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. In TypeScript, we can leverage type annotations and class property declarations to make our code more robust and maintainable. TypeScript's type safety helps us catch errors early and provides better tooling support.

Here's how we set up the basic structure of our DocumentProcessor class in TypeScript:

Let's break down what each variable does:

• 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 Faiss vector store, which we'll initialize later when we process our first document.

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. TypeScript allows us to specify the types of method parameters and return values, making our code more predictable and easier to maintain.

First, we'll create a method to load documents based on their file type:

• Parameter Types: The filePath parameter is explicitly typed as a string.
• Return Type: The method returns a Promise<Document[]>, ensuring that we always return an array of Document objects asynchronously.

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 with our specified chunk size and overlap.

Now that we can process documents and store their embeddings, we need a way to retrieve relevant context when a question 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 question:

This method takes a question string and an optional parameter k, which specifies how many chunks to retrieve. It then performs a similarity search in our vector store to find the k most relevant chunks. If we haven't processed any documents yet (this.vectorstore is null), we simply return an empty array.

Let's also add a utility method to reset our document processor:

This method simply sets this.vectorstore to null, 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 question
4. Uses a chat model to generate a response based on this context

Here's the complete example in TypeScript:

Let's highlight how TypeScript helps in this workflow:

• Type Annotations: We specify types for variables such as filePath, chunks, question, , and , making the code more readable and less error-prone.

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

• Create a DocumentProcessor class that encapsulates document processing functionality with strong type safety
• Load documents from PDF files with type-checked methods
• Split documents into manageable chunks with appropriate overlap, using explicit types
• Create and manage a vector store for efficient similarity search, leveraging TypeScript's type system
• Retrieve relevant context for user queries with predictable return types
• Integrate our document processor with a chat model for a basic RAG workflow, benefiting from TypeScript's tooling and error checking

TypeScript's type safety and tooling make it easier to build, maintain, and scale complex systems like a RAG-powered chatbot. 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!