Chunking methods for LLMs

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Language models like OpenAI's GPT-3.5 have a token limit of 4096, but often in real-world applications, we encounter big data spanning multiple disparate files that exceed this limit. This presents a challenge when attempting to harness the full potential of these models. One promising solution is chunking the input text before converting it to embeddings. This approach not only ensures the compatibility of data with the model but also enhances the efficiency of processing by reducing noise and retaining semantic relevance. The video below illustrates different methods of chunking along with their respective pros and cons.
Chunking is particularly beneficial when using tools like Pinecone, which require content to be embedded before indexing. The primary objective of chunking is to generate an embedding for a piece of content that carries as little noise as possible while remaining semantically relevant. Let's take the example of semantic search, where we index a corpus of documents, with each document providing vital information on a specific topic. By applying an effective chunking strategy, we can ensure that our search results capture the essence of the user’s query. Both under-chunking and over-chunking can lead to imprecise search results or overlooked opportunities to surface pertinent content. The ideal chunk size should be such that the piece of text remains meaningful to a human even without the surrounding context.
Conversational agents provide another context where chunking plays a pivotal role. Here, embedded chunks are used to construct the context for the conversational agent based on a knowledge base grounded in reliable information. Our chunking strategy in this scenario is crucial for two reasons. Firstly, it determines the relevance of the context to our prompt. Secondly, it dictates whether we can fit the retrieved text into the context before sending it to an external model provider, like OpenAI, considering the constraints on the number of tokens we can transmit per request. While larger context windows, such as the 32k context window in GPT-4, may alleviate this issue, the use of excessively large chunks can potentially compromise the relevance of the results retrieved from Pinecone.
Several chunking methods are available, each with its own trade-offs, which we will explore in this post. When choosing a chunking size and method, it is important to weigh these trade-offs carefully to find the optimal strategy for your application. Tools like OpenAI provide API access to powerful large language models (LLMs) like ChatGPT and GPT-4 and embedding models for converting text to embeddings. Pinecone offers vector storage for embeddings, semantic similarity comparisons, and swift retrieval. Langchain, with its six modules, offers a comprehensive solution, providing flexibility in models, efficient memory management, and a pipeline-like structure for processing user input, model selection, prompt application, and context searching. Ultimately, finding the best chunk size and method depends on your specific needs and the nature of your data.
00:00 Introduction to Chunking for LLMS
00:25 Basic Chunking Method
00:33 Iterative Summarization Technique
00:41 Summarize Summaries Method
00:49 Hierarchical Summarization Explained
01:00 Sliding Window Approach
01:07 Conclusion: Choosing the Right Method
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