Transcript: Chunking Strategies Explained

Version 1: Source Transcript

This document represents the raw lecture transcript recorded from the video lecture. For architectural study notes and system design documentation, see Version 2.

• Video URL: https://www.youtube.com/watch?v=ZTOtxiWb2bE
• Video ID: ZTOtxiWb2bE
• Word Count: 1,075 words

Full Transcript

[Music] Okay. Um, bring up everything you have on Spider-Man. Yeah. Open that. Okay. No, no, no. What? Nothing. I am just in awe about how quickly you could find the exact chunks of data about Spider-Man with such a simple prompt. And a matter of fact, I would like to take this opportunity to explain to our friends here the importance of chunking data. If you are building LLM applications that interact with vector databases, one of the most crucial decisions you will make is to how to break it down your tax data into manageable pieces. This process is called chunking and it directly affects the quality, accuracy and relevance of your applications responses. In this video, I will walk you through and what chunking is, why does it matter and the different strategies you can implement to optimize your LLM applications. Are you ready for this? Let's do it. So, what is chunking? Chunking is simply breaking down large pieces of text into smaller segments before embedding them and storing them into a vector database. But why people would do junk texts in the first place? The main reason is to ensure we're embedding pieces of context with minimal noise and maintaining semantic relevance. For example, in semantic search applications, a proper chunking ensures your search results accurately capture the what users are looking for. In conversational agents, good chunking ensures the retrieved context applies to the user's query and fits within the token limitation of your LLM. One very important aspect to consider while you're embedding data is the length of content being embedded because it significantly affect the created factors. Therefore, it is important to talk about embedding short versus long content. When embedding short content like sentences, here's what you need to consider. Vectors, they focus on very specific meanings. They excel at precise matching. However, it may miss broader context. With longer context like paragraphs, here's what you need to consider. Embattings capture overall themes and relationships. It provides more comprehensive representation. However, it may delude the significancy of specific details. When it comes to size, here's a tip for you. Your query length should ideally match with your chunk size. Short queries work better with sentence level embeddings while longer, more complex queries align better with paragraph level embeddings. Before choosing a chunking strategy, these are the factors that you need to consider. Number one, consider the content nature. Are you working with long documents like research papers? Or perhaps you are working with short textes like tweets. Number two, consider the embedding model characteristics. Different models perform optionally with different chunk sizes. For instance, sentence transformer modules work well on individual sentences. Meanwhile, text embedding ADA 002 performs better with 256 or 512 token chunks. Number three, consider the query expectations. Will your users ask short, very specific questions or perhaps they're asking more complex ones? Number four, also consider the application purpose. Will you use the retrieved results? You are going to use them for semantic search, going to use them for question answering, or you're going to use them for summarization. Answering these questions will guide you toward the right chunking approach for your use case. Let's now talk about the chunking approaches you can use in your applications. Let's start with the fixed size chunking. This is the most straightforward approach. You simply decide how many tokens each chunk should contain and optionally you can add an overlap between chunks to preserve context. Fixed side chunking is computationally inexpensive and works well for most common use cases. Another approach is contextaware chunking. This method takes advantage of the natural structury of your content. For example, you can implement sentence level splitting. Many embedding models are optimized for sentence level content. On the application coding side, frameworks like link chain provide integrations with NLP libraries. You can also leverage recursive chunking. Recursive chunkers intelligently divide text using a set of separators until they reach the desired chunk size. Another approach for structured content like markdown or latex, you can leverage specialized splitters because of the uniqueness of the structury of the content. This approach is known as specialized chunking. Finally, you can implement semantic chunking to identify natural topic boundaries. This produces chunks that maintain thematic coherence rather than arbitrary token counts. As you can imagine, this approach is a bit more computationally expensive when compared to the other approaches. Now that we've discussed the chunking approaches you can use, let's talk about how you can decise the optional chunk size. As you can imagine, this is going to be one of the most important tasks for your projects. To determine the best chunk size for your application, you should follow these three steps. First pre-process your data by removing all the noise and irrelevant content from it. Second, you should select a chunk sizes and a range of them to test basin on your content type and your embedding model. First, you have to try smaller sizes like 128 until 256 tokens to accommodate more granular information. And perhaps you should try larger sizes for 512 until 1,024 tokens to accommodate more context. And the third step is to evaluate the performance. You should create embeddings using different chunk sizes, store them into a vector database, and then run representative queries to evaluate their performance. In this evaluation, you should compare metrics like relevancy, accuracy, and retrieval speed. While this process look tedious and if you do it manually, you can automate all of this by creating task and workflows with frameworks like lingraph to handle this evaluation process across different chunking parameters. To wrap up, chunking is a critical but often overlook aspect of building effective LLM applications with vector databases. This is what you should always remember. There's no oneizefits-all solution. Consider your specific content, language model, and use case. Match the chunk size to the user squares expectations. Be familiar with the multiple chunking approaches that are available. Don't be afraid to experiment with other approaches if you feel needed. The right chunking approach will dramatically improve the relevancy and quality of your LLM applications, creating a better experience for your users who will benefit from more accurate results. Thanks for watching and if you find this content helpful, please hit the like button. Also, please consider subscribing to this channel for more content about building effective AI applications. [Music]

Timestamped Segments

• [00:00] [Music] Okay. Um, bring up everything you have on Spider-Man. Yeah. Open that. Okay. No, no, no.
• [00:29] What? Nothing. I am just in awe about how quickly you could find the exact chunks of data about Spider-Man with such a simple prompt. And a matter of fact, I would like to take this opportunity to explain to our friends here the importance of chunking data. If you are building LLM applications that interact with vector databases, one of the most crucial decisions you will make is to how to break it down your tax data into manageable pieces. This process is called chunking and it directly affects the quality, accuracy and relevance of your applications
• [01:03] responses. In this video, I will walk you through and what chunking is, why does it matter and the different strategies you can implement to optimize your LLM applications. Are you ready for this? Let's do it. So, what is chunking? Chunking is simply breaking down large pieces of text into smaller segments before embedding them and storing them into a vector database. But why people would do junk texts in the first place?
• [01:29] The main reason is to ensure we're embedding pieces of context with minimal noise and maintaining semantic relevance. For example, in semantic search applications, a proper chunking ensures your search results accurately capture the what users are looking for. In conversational agents, good chunking ensures the retrieved context applies to the user's query and fits within the token limitation of your LLM.
• [01:53] One very important aspect to consider while you're embedding data is the length of content being embedded because it significantly affect the created factors. Therefore, it is important to talk about embedding short versus long content. When embedding short content like sentences, here's what you need to consider.
• [02:14] Vectors, they focus on very specific meanings. They excel at precise matching. However, it may miss broader context. With longer context like paragraphs, here's what you need to consider. Embattings capture overall themes and relationships.
• [02:35] It provides more comprehensive representation. However, it may delude the significancy of specific details. When it comes to size, here's a tip for you. Your query length should ideally match with your chunk size. Short queries work better with sentence level embeddings while longer, more complex queries align better with paragraph level embeddings.
• [03:03] Before choosing a chunking strategy, these are the factors that you need to consider. Number one, consider the content nature. Are you working with long documents like research papers? Or perhaps you are working with short textes like tweets. Number two, consider the embedding model characteristics. Different models perform optionally with different chunk sizes. For instance, sentence transformer modules work well on individual sentences. Meanwhile, text embedding ADA 002 performs better with 256 or 512 token chunks. Number three,
• [03:42] consider the query expectations. Will your users ask short, very specific questions or perhaps they're asking more complex ones? Number four, also consider the application purpose. Will you use the retrieved results? You are going to use them for semantic search, going to use them for question answering, or you're going to use them for summarization.
• [04:07] Answering these questions will guide you toward the right chunking approach for your use case. Let's now talk about the chunking approaches you can use in your applications. Let's start with the fixed size chunking. This is the most straightforward approach. You simply decide how many tokens each chunk should contain and optionally you can add an overlap between chunks to preserve context.
• [04:35] Fixed side chunking is computationally inexpensive and works well for most common use cases. Another approach is contextaware chunking. This method takes advantage of the natural structury of your content. For example, you can implement sentence level splitting. Many embedding models are optimized for sentence level content. On the application coding side, frameworks like link chain provide integrations with NLP libraries.
• [05:06] You can also leverage recursive chunking. Recursive chunkers intelligently divide text using a set of separators until they reach the desired chunk size. Another approach for structured content like markdown or latex, you can leverage specialized splitters because of the uniqueness of the structury of the content. This approach is known as specialized chunking.
• [05:34] Finally, you can implement semantic chunking to identify natural topic boundaries. This produces chunks that maintain thematic coherence rather than arbitrary token counts. As you can imagine, this approach is a bit more computationally expensive when compared to the other approaches.
• [05:54] Now that we've discussed the chunking approaches you can use, let's talk about how you can decise the optional chunk size. As you can imagine, this is going to be one of the most important tasks for your projects. To determine the best chunk size for your application, you should follow these three steps.
• [06:13] First pre-process your data by removing all the noise and irrelevant content from it. Second, you should select a chunk sizes and a range of them to test basin on your content type and your embedding model. First, you have to try smaller sizes like 128 until 256 tokens to accommodate more granular information. And perhaps you should try larger sizes for 512 until 1,024 tokens to accommodate more context.
• [06:47] And the third step is to evaluate the performance. You should create embeddings using different chunk sizes, store them into a vector database, and then run representative queries to evaluate their performance. In this evaluation, you should compare metrics like relevancy, accuracy, and retrieval speed.
• [07:08] While this process look tedious and if you do it manually, you can automate all of this by creating task and workflows with frameworks like lingraph to handle this evaluation process across different chunking parameters. To wrap up, chunking is a critical but often overlook aspect of building effective LLM applications with vector databases. This is what you should always remember.
• [07:33] There's no oneizefits-all solution. Consider your specific content, language model, and use case. Match the chunk size to the user squares expectations. Be familiar with the multiple chunking approaches that are available. Don't be afraid to experiment with other approaches if you feel needed.
• [07:55] The right chunking approach will dramatically improve the relevancy and quality of your LLM applications, creating a better experience for your users who will benefit from more accurate results. Thanks for watching and if you find this content helpful, please hit the like button. Also, please consider subscribing to this channel for more content about building effective AI applications.
• [08:19] [Music]