Welcome to the first lesson of our course on "Scaling Up RAG with Vector Databases"! This is the third course in our series, building on the foundations laid in the previous courses. Course 1 introduced a basic RAG pipeline, providing a foundational understanding of how retrieval and generation can be combined. Course 2 focused on text representation, with a particular emphasis on embeddings.

In this course, we'll focus on scaling your Retrieval-Augmented Generation (RAG) system by building and querying a vector database. You'll learn to preprocess documents, store chunk embeddings in ChromaDB, retrieve relevant chunks using advanced techniques like compound metadata filters and weighting, and construct prompts that can handle multiple context chunks. Additionally, we'll cover managing updates to your collection and large-scale ingestion using batch strategies.

Our journey begins with document chunking, a crucial preprocessing step that enhances the efficiency and effectiveness of vector databases in RAG systems. By the end of this lesson, you'll be able to break down a lengthy document into discrete segments, each tagged with essential metadata, paving the way for robust retrieval and storage in vector databases.

When dealing with Retrieval-Augmented Generation, you typically feed chunks of text — along with certain metadata — into downstream components, like embedding models or vector search engines. This document chunking process is crucial for several reasons:

1. Manageability: Smaller segments are easier to process. Many models have a maximum context length, meaning they can only handle a certain number of tokens at a time. If the input text exceeds this limit, the model may become inefficient or unable to process the request, leading to errors or truncated outputs. Additionally, even if a model can technically handle larger contexts, performance may degrade, resulting in slower processing times and reduced accuracy.

2. Context Preservation: A well-sized chunk still retains enough local context to be meaningful. Chunks should be neither too large (leading to potential memory issues) nor too small (risking the loss of context).

3. Enhanced Retrieval: When text is split rationally, you can retrieve only the relevant segments instead of searching through entire documents — this shortens query times and boosts accuracy.

Think of a real-world example: If you have a large reference manual, you wouldn't read the whole thing to answer a single question. Instead, you'd look for the exact section (or chunk) that pertains to your query. The same principle carries into text retrieval on a computer.

To effectively manage large texts, we need to break them into smaller, manageable parts. The logic behind this involves dividing the text into segments based on a specified word count. This ensures that each segment is of a size that can be efficiently processed by language models. The process involves tokenizing the text into words and then grouping these words into chunks of a predetermined size. This method, while straightforward, may not account for punctuation or sentence boundaries, which can affect context preservation. For more advanced chunking, consider using natural language processing tools that respect sentence boundaries.

Here's a Java implementation of a simple chunking process:

This function splits a given text into chunks of a specified size, returning a list of chunk strings. It uses whitespace to tokenize the text into words and then groups these words into chunks.

Before moving on, let's briefly take a closer look at the dataset we'll be working with for chunking. Below is an example of two documents in JSON format:

Our dataset consists of an array of items, where each item has an id field that identifies the document and a content field containing the main text. After applying a chunking approach to each document, our data will be represented as smaller segments of text that are easier to process downstream, as we'll see later in the lesson.

In RAG systems, it's important to know the origin and representation of each text piece. The logic here involves associating each chunk with metadata that includes identifiers for both the document and the chunk itself. This metadata ensures that each segment of text is traceable back to its source, which is crucial for retrieval and filtering in a vector database. The process involves iterating over a dataset, chunking each document, and then tagging each chunk with its respective document and chunk identifiers.

Here's how you can load a dataset and chunk it while adding metadata:

This function loads a dataset from a JSON file, splits each document into smaller chunks, and includes metadata such as docId and category with each chunk.

To demonstrate the chunking process, we use a simple dataset of documents. The logic involves loading the dataset, applying the chunking method to each document, and then printing the results. This demonstration highlights both the benefits and limitations of our chunking approach. While the documents are successfully broken down into smaller pieces with metadata, some chunks may lack sufficient context due to their size or the way they intersect sentence boundaries. This underscores the importance of using more sophisticated chunking strategies in production systems to ensure effective context preservation.

Here's a complete example of how to use the chunking functions:

This code loads a dataset, chunks the documents, and prints each chunk with its metadata.

In this lesson, we focused on why and how to chunk larger text documents. We learned that chunking:

• Makes text blocks suitably sized for processing with language models.
• Preserves local context by grouping words carefully.
• Allows for the attachment of metadata that makes future retrieval more powerful.

Next, we'll embed these chunks and store them in a vector database so we can efficiently search within them. Feel free to practice splitting your own documents and confirm the results before moving on to the upcoming exercises. By mastering the chunking and metadata process, you're well on your way to creating scalable, retrieval-enhanced applications.