Retrieval Augmented Generation (RAG), undefined

LLM-Guided Segmentation

LLM-guided segmentation leverages the language understanding capabilities of large language models to identify natural conceptual boundaries in text. This approach treats chunking as an intelligent task rather than a mechanical one:

Core Mechanism: A language model is prompted to analyze the document and identify logical break points where conceptual shifts occur. These LLM-identified boundaries are then used to create chunks that align with the semantic structure of the content.

Advantages:

Semantic Understanding: Identifies conceptual boundaries that might not align with formatting
Content-Aware: Adapts to document style and subject matter automatically
Higher Retrieval Quality: Creates chunks that align with how information is conceptually organized

Disadvantages:

Computational Cost: Requires LLM inference, adding significant processing overhead
Latency Concerns: Much slower than rule-based approaches, especially for large documents
Consistency Challenges: May produce different results for similar content depending on model behavior

from langchain.text_splitter import TextSplitter
from langchain_openai import ChatOpenAI

class LLMTextSplitter(TextSplitter):
    def __init__(self, llm):
        self.llm = llm
        super().__init__()
    
    def split_text(self, text):
        prompt = f"""
        Analyze the following text and identify 3-5 logical breaking points where the topic shifts.
        Return only the breaking points as character indices, separated by commas.
        
        TEXT: {text}
        """
        
        response = self.llm.invoke(prompt)
        # Parse indices from response and split text accordingly
        # [Implementation details omitted for brevity]
        return []  # Placeholder

llm = ChatOpenAI(model="gpt-3.5-turbo")
llm_splitter = LLMTextSplitter(llm)
chunks = llm_splitter.split_text(document)

Best Use Cases: LLM-guided segmentation is particularly valuable for complex, nuanced content where conceptual boundaries don't align neatly with formatting. It excels with philosophical texts, creative writing, and documents where ideas develop across structural boundaries. Due to its cost, it's often reserved for high-value content where retrieval quality is paramount.