RAG Chunking Strategies Deep Dive

Source: Dev.to

The Chunking Challenge

Without proper chunking, RAG systems suffer from:

• Lost context – breaking text at arbitrary boundaries destroys semantic meaning.
• Poor retrieval – overly large chunks reduce precision; overly small chunks lose context.
• Inefficient embedding – vector databases work best with semantically coherent units.
• Token waste – irrelevant information consumes precious context‑window space.

Built‑in chunking strategies solve these problems by providing intelligent, domain‑aware text segmentation that preserves semantic boundaries while optimizing retrieval performance.

What is Chunking?

Chunking is the process of breaking down large documents into smaller, semantically meaningful segments that can be:

• Embedded as dense vectors for similarity search.
• Retrieved independently based on relevance to a query.
• Fed to an LLM within its context‑window constraints.

Effective chunking balances two competing goals:

1. Chunks must be small enough to be precise and fit within embedding model limits (typically 512–8192 tokens).
2. Chunks must be large enough to contain sufficient context for accurate retrieval and generation.

The optimal chunking strategy depends on your document type, retrieval task, and downstream LLM usage.

Framework Overview

The Agentic Memory library includes an extensible chunking framework that lets you split documents into optimal chunks for semantic search and retrieval.

Architecture

All chunking strategies are part of the core framework in the io.github.vishalmysore.rag.chunking package. The example code below demonstrates how to use these strategies.

Core Components

• ChunkingStrategy interface – base interface for all chunking strategies.

  List chunk(String content);   // Splits content into chunks
  String getName();            // Returns strategy name
  String getDescription();     // Returns strategy description

• RAGService.addDocumentWithChunking() – convenience method for automatic chunking.

  int chunkCount = rag.addDocumentWithChunking(
      "document_id",
      content,
      chunkingStrategy
  );

Built‑in Strategies

1. Sliding Window Chunking

Package: io.github.vishalmysore.rag.chunking.SlidingWindowChunking

Creates overlapping chunks to preserve context across boundaries.

Technical details

• Sliding window with configurable size and overlap.
• Word‑based tokenization with configurable delimiters.
• Maintains approximately equal chunk sizes for consistent embedding quality.

ChunkingStrategy strategy = new SlidingWindowChunking(150, 30);
// 150 words per chunk, 30 words overlap (20%)

Parameters

• windowSize – number of words per chunk (typical: 100–300).
• overlap – number of overlapping words between chunks (typical: 10–20 % of window size).

Best for: Healthcare records, continuous narratives, patient notes where context flows across boundaries.

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2. Adaptive Chunking

Package: io.github.vishalmysore.rag.chunking.AdaptiveChunking

Respects natural document boundaries while staying within token limits.

Technical details

• Regex pattern matching to identify semantic boundaries (sections, paragraphs, etc.).
• Dynamically adjusts chunk size based on boundary locations.
• Enforces min/max token constraints to balance precision and context.

ChunkingStrategy strategy = new AdaptiveChunking(
    "(?m)^SECTION \\d+:",  // Boundary pattern (regex)
    800,                     // Min tokens
    1200                     // Max tokens
);

Parameters

• boundaryPattern – regex to identify split points (e.g., section headers).
• minTokens – minimum chunk size to maintain context.
• maxTokens – maximum chunk size to fit embedding model limits.

Best for: Legal contracts, structured documents, policy documents with clear section markers.

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3. Entity‑Based Chunking

Package: io.github.vishalmysore.rag.chunking.EntityBasedChunking

Groups sentences by mentioned entities (people, companies, locations).

Technical details

• Performs Named Entity Recognition (NER) on input text.
• Groups consecutive sentences that reference the same entities.
• Uses entity co‑occurrence analysis to determine chunk boundaries.

String[] entities = {"Elon Musk", "Tesla", "SpaceX"};
ChunkingStrategy strategy = new EntityBasedChunking(entities);

Parameters

• entities – array of entity names to track (people, organizations, locations, etc.).
• Optional: entity types (PERSON, ORG, LOCATION) for automatic detection.

Algorithm

1. Scan text for entity mentions.
2. Group sentences with shared entity references.
3. Create a new chunk when the entity focus shifts.
4. Preserve co‑occurrence relationships.

Best for: News articles, research papers, multi‑person biographies, documents with multiple actors.

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4. Topic/Theme‑Based Chunking

Package: io.github.vishalmysore.rag.chunking.TopicBasedChunking

Groups content by underlying topics or themes.

Technical details

• Uses topic modeling or keyword matching to identify thematic shifts.
• Regex‑based topic boundary detection for structured documents.
• Optional: Latent Dirichlet Allocation (LDA) for unsupervised topic discovery.

ChunkingStrategy strategy = new TopicBasedChunking(
    "(EDUCATION|CAREER|PATENTS):"
);

Parameters

• topicPattern – regex pattern to identify topic boundaries.
• Optional: topic model configuration for unsupervised chunking.

Best for: Research papers, technical documentation, structured content with explicit topic markers.

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5. Hybrid Chunking

Package: io.github.vishalmysore.rag.chunking.HybridChunking

Combines multiple strategies in a pipeline.

ChunkingStrategy adaptive = new AdaptiveChunking("(?m)^===\\s*$");
ChunkingStrategy topic = new TopicBasedChunking("(INTRO|BODY|CONCLUSION):");

ChunkingStrategy strategy = new HybridChunking(adaptive, topic);

Best for: Complex documents requiring multi‑stage processing.

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6. Task‑Aware Chunking

Package: io.github.vishalmysore.rag.chunking.TaskAwareChunking

Adapts chunking based on the downstream task (summarization, search, Q&A).

Technical details

• Implements task‑specific heuristics for optimal chunk sizing.

// Summarization (small chunks)
ChunkingStrategy strategy = new TaskAwareChunking(TaskType.SUMMARIZATION);

// Search (medium chunks)
ChunkingStrategy strategy = new TaskAwareChunking(TaskType.SEARCH);

// Q&A (large chunks)
ChunkingStrategy strategy = new TaskAwareChunking(TaskType.QA);