Enhance SemanticChunker with LLM-Based Dynamic Semantic Analysis

[archervanderwaal]

Checked

• I searched existing ideas and did not find a similar one
• I added a very descriptive title
• I've clearly described the feature request and motivation for it

Feature request

While LangChain's SemanticChunker effectively splits text based on semantic similarity using predefined thresholds, integrating large language models (LLMs) could further enhance this process. LLMs possess a deeper understanding of context and semantics, enabling more nuanced and accurate chunking decisions.

Motivation

In complex documents, semantic relationships between sentences or paragraphs may not always align with predefined thresholds. LLMs can analyze the content holistically, ensuring that semantically related information is grouped together, thereby improving the quality of information retrieval and generation.​

Proposed Enhancement:

• Dynamic Semantic Analysis: Incorporate LLMs to assess semantic coherence between sentences or paragraphs, allowing for more context-aware chunking.​

• Adaptive Thresholding: Utilize LLMs to dynamically adjust thresholds for splitting, based on the content and context of the text.​

• Contextual Chunking: Enable the SemanticChunker to consider broader context when determining chunk boundaries, improving the relevance of retrieved information in Retrieval-Augmented Generation (RAG) systems.

Implementing these enhancements could lead to more accurate and contextually appropriate chunking, thereby improving the performance of RAG applications.​

Proposal (If applicable)

If this proposal is accepted, I would be honored to contribute to its implementation. I am prepared to develop and submit a pull request that integrates LLM-based semantic analysis into the SemanticChunker, enhancing its ability to perform dynamic and context-aware chunking.