Chunking and Storing Text for Efficient LLM Processing with Chroma DB

Welcome to the final lesson of our course on data processing for Large Language Models (LLMs). In previous lessons, you've learned about chunking text, advanced chunking techniques, and storing text chunks in JSONL format. Now, we'll focus on using the Chroma DB library to efficiently store and retrieve text chunks. This lesson will integrate these concepts, allowing you to handle text data effectively in LLM applications.

LLMs have a limitation when processing long text due to their fixed token window. Chunking text into smaller parts helps maintain context and ensures that important information isn't lost. In this lesson, we'll explore how to use vector embeddings and the Chroma DB library to store and retrieve these chunks efficiently.

Before we dive into the new material, let's briefly recall what text embeddings are. Text embeddings are numerical representations of text that capture semantic meaning. They allow us to perform operations like similarity searches, which are crucial for retrieving relevant information from large datasets.

In previous lessons, you learned about tokenization and basic vector operations. These concepts are foundational for understanding how embeddings work. Remember, embeddings transform text into a format that LLMs can process more efficiently.

To work with text embeddings, we need an embedding model. We'll use the SentenceTransformer library, which provides pre-trained models for generating embeddings.

First, let's load the "all-MiniLM-L6-v2" model:

from sentence_transformers import SentenceTransformer

# Load embedding model
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")

In this code snippet, we import the SentenceTransformer class and load the "all-MiniLM-L6-v2" model. This model is pre-trained to generate embeddings for sentences, making it ideal for our task.

Chunking is the process of splitting long documents into smaller, manageable parts. This is crucial for maintaining context when processing text with LLMs.

Consider the following text:

documents = [
    "Large language models process text within a limited token window.",
    "If the input text is too long, models may truncate information.",
    "Splitting long documents into smaller chunks helps maintain relevant context.",
]

Here, we have a list of sentences, each representing a chunk of text. By breaking down the text into these smaller parts, we ensure that each chunk can be processed without losing important context.