The 5 Levels Of Text Splitting For Retrieval

Unlocking the Power of Language Models with Text Splitting.

1970-01-01T03:54:19.000Z

🌰 Wisdom in a Nutshell

Essential insights distilled from the video.

  1. Text splitting improves language model performance, with varying strategies.
  2. Text splitting optimizes language models, enhancing data usability.
  3. Recursive Character Text Splitter splits text based on structure, aiding writing.
  4. Splitting documents involves format-specific headers and PDF extraction.
  5. Agentic chunking simplifies complex information by breaking it into manageable chunks.
  6. Preparing data for language models involves text splitting, embedding extraction, and graph structure extraction.


📚 Introduction

Text splitting is a crucial technique in improving the performance of language model applications. It involves breaking down large data sets into smaller chunks, which enhances the signal-to-noise ratio and makes the data more useful for specific tasks. In this blog post, we will explore the different levels of text splitting and their strategies, as well as the importance of data chunking and the future of agentic chunking.


🔍 Wisdom Unpacked

Delving deeper into the key ideas.

1. Text splitting improves language model performance, with varying strategies.

The practice of text splitting, where large data is split into smaller chunks, can improve the performance of language model applications. There are five levels of text splitting, each with different strategies and considerations. The levels are character splitting, recursive character text splitting, document-specific text splitting, semantic splitting, and agentic splitting. The goal is to expose different strategies and considerations for splitting data, rather than prescribing the best method.

Dive Deeper: Source Material

This summary was generated from the following video segments. Dive deeper into the source material with direct links to specific video segments and their transcriptions.

Segment Video Link Transcript Link
Intro🎥📄


2. Text splitting optimizes language models, enhancing data usability.

Text splitting, or chunking, is a process of dividing data into smaller pieces to optimize it for language models. This helps increase the signal-to-noise ratio, making the data more usable for specific tasks. The goal is to prepare the data in a format that can be retrieved and used by the language model. Chunking is a crucial step in the retrieval process, and there is no one right way to do it. Evaluations are important for testing and improving performance.

Dive Deeper: Source Material

This summary was generated from the following video segments. Dive deeper into the source material with direct links to specific video segments and their transcriptions.

Segment Video Link Transcript Link
Theory🎥📄


3. Recursive Character Text Splitter splits text based on structure, aiding writing.

The Recursive Character Text Splitter is a powerful tool that splits text based on its physical structure, using separators like double new lines, new lines, and spaces. This method takes advantage of how humans naturally write text, separating ideas by paragraphs. By increasing the chunk size, we can take advantage of paragraph splits. This tool is a go-to for splitting text, especially for projects, and is easy to use and requires minimal processing.

Dive Deeper: Source Material

This summary was generated from the following video segments. Dive deeper into the source material with direct links to specific video segments and their transcriptions.

Segment Video Link Transcript Link
Level 1: Character Split🎥📄
Level 2: Recursive Character Split🎥📄


4. Splitting documents involves format-specific headers and PDF extraction.

The process of splitting documents involves considering the specific format and using appropriate headers, class, function, and indentation separators. For PDFs, extracting tables, pictures, and graphs can be helpful. When working with PDFs, it is important to extract information and use a language model like GPT-4 to generate a text of each image. Grouping similar items can be beneficial for question and answer tasks.

Dive Deeper: Source Material

This summary was generated from the following video segments. Dive deeper into the source material with direct links to specific video segments and their transcriptions.

Segment Video Link Transcript Link
Level 3: Document Specific Splitting🎥📄


5. Agentic chunking simplifies complex information by breaking it into manageable chunks.

Agentic chunking is a technique that involves breaking down a large piece of text into smaller, manageable chunks, often by identifying propositions, which are self-contained sentences that can stand on their own. This can be done using a language model to guide the decision-making process. The process involves determining if the proposition should belong to any of the existing chunks, and if not, creating a new chunk. This method is slow and expensive, but it can be useful if language models become faster and cheaper.

Dive Deeper: Source Material

This summary was generated from the following video segments. Dive deeper into the source material with direct links to specific video segments and their transcriptions.

Segment Video Link Transcript Link
Level 4: Semantic Splitting (With Embeddings)🎥📄
Level 5: Agentic Splitting🎥📄


6. Preparing data for language models involves text splitting, embedding extraction, and graph structure extraction.

When preparing data for a language model, it is crucial to consider how to split the texts. One approach is to get embeddings of the text and store them in a knowledge base. This can be done using summaries or hypothetical questions. Another approach is to use a parent document retriever, which subsets the document to improve semantic search. Additionally, you can extract a graph structure from the text to answer questions about entities and relationships.

Dive Deeper: Source Material

This summary was generated from the following video segments. Dive deeper into the source material with direct links to specific video segments and their transcriptions.

Segment Video Link Transcript Link
Bonus Level: Alternative Representation🎥📄



💡 Actionable Wisdom

Transformative tips to apply and remember.

When working with language model applications, consider implementing text splitting techniques to optimize data performance. Experiment with different strategies at various levels, such as character splitting and semantic splitting, to find the best approach for your specific task. Additionally, prioritize data chunking by breaking down large text pieces into manageable chunks, using tools like the Recursive Character Text Splitter. This will enhance the usability of your data and improve the overall performance of your language model application.


📽️ Source & Acknowledgment

Link to the source video.

This post summarizes Greg Kamradt (Data Indy)'s YouTube video titled "The 5 Levels Of Text Splitting For Retrieval". All credit goes to the original creator. Wisdom In a Nutshell aims to provide you with key insights from top self-improvement videos, fostering personal growth. We strongly encourage you to watch the full video for a deeper understanding and to support the creator.


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