Retrieval Augmented Generation (RAG), undefined

Semantic Segmentation

Semantic segmentation divides text into conceptually meaningful units rather than using arbitrary fixed-length chunks. This approach ensures that related information stays together, dramatically improving retrieval relevance:

Core Concept: Unlike mechanical splitting that might cut through important concepts, semantic segmentation identifies natural boundaries where topics shift. This preserves the coherence of ideas and prevents critical context from being fragmented across different chunks.

Implementation Approaches:

Topic-Based Segmentation: Identifies shifts in subject matter using statistical methods or embedding similarity changes
Hierarchical Segmentation: Creates nested segments from document → section → paragraph → sentence
LLM-Guided Segmentation: Uses language models to identify logical breakpoints in content

Benefits for RAG:

Improved Retrieval Precision: Returns complete concepts rather than partial information
Reduced Context Pollution: Minimizes irrelevant content in retrieved passages
Better Answer Generation: Provides LLMs with coherent units of information

from langchain.text_splitter import RecursiveCharacterTextSplitter

# Create a semantic text splitter that respects paragraph structure
text_splitter = RecursiveCharacterTextSplitter(
    separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""],
    chunk_size=1000,
    chunk_overlap=200,
    length_function=len
)

# For more advanced semantic splitting:
from langchain_experimental.text_splitter import SemanticChunker
from langchain_openai import OpenAIEmbeddings

# Split based on semantic similarity
semantic_splitter = SemanticChunker(embeddings=OpenAIEmbeddings())
semantic_chunks = semantic_splitter.split_text(long_document)

In practice, semantic segmentation often yields significant improvements in RAG quality, particularly for complex documents where context preservation is critical. For technical documentation, research papers, or any content with interconnected concepts, semantic approaches prevent the fragmentation of ideas that can lead to incomplete or misleading retrieval results.