Chunking Strategies: A Comprehensive Guide

by Alex Johnson 43 views

In the realm of information retrieval and natural language processing, chunking plays a pivotal role in optimizing how we process and understand large documents. This guide delves into the intricacies of chunking strategies, providing a detailed exploration of various techniques and their applications. Whether you're building a search engine, implementing a retrieval-augmented generation (RAG) system, or simply trying to make sense of vast amounts of text data, mastering chunking is essential.

1. The Role of Chunking in Your Design

When designing systems that deal with large text documents, chunking serves as a foundational step. Imagine you're building a system where users can search for information within a vast repository of documents. Your ingestion pipeline might involve two key components:

  • Keyword-based Indexing: This involves creating an inverted index where keywords are associated with specific chunks of text within the documents. When a user searches for a keyword, the system can quickly identify the relevant chunks.
  • Semantic Base: This approach uses vector embeddings to represent the semantic meaning of text chunks. A query is also converted into a vector, and the system finds chunks with similar vector representations. This allows for semantic search, where users can find information even if they don't use the exact keywords.

In both cases, the chunk is the fundamental unit of search. It's on these chunks that BM25 (a ranking function for full-text search) is applied, embeddings are calculated, and strong links are maintained back to the original document. Therefore, effective chunking is crucial; if chunking is done poorly, the entire retrieval process can suffer. The quality of your chunks directly impacts the relevance of search results, the accuracy of semantic understanding, and the efficiency of the overall system.

Chunking isn't just about dividing text; it's about strategically segmenting information to optimize retrieval and understanding. A well-defined chunk strikes a balance between being concise enough for efficient processing and comprehensive enough to retain context. The size and method of chunking can significantly influence the performance of subsequent tasks, such as semantic search, question answering, and text summarization. By thoughtfully chunking your documents, you lay the groundwork for a robust and effective information retrieval system.

2. A Proposed Hybrid Chunking Strategy

Considering the importance of chunking, a hybrid approach is often the most effective. This strategy combines structural chunking with a sliding window technique to address the challenges of varying text structures and content lengths. The proposed hybrid chunking strategy consists of two main steps:

2.1. Step 1 – Structural Chunking

The first step involves leveraging the natural structure of the documents. Procedures, for example, often have a clear organizational pattern with headings, subheadings, paragraphs, and lists. We can exploit these structural elements to create initial chunks. This means:

  • Identifying separators: Look for double newlines (\n\n), titles, lists, and section markers (e.g., “1.”, “2.”, “Step 1”, etc.).
  • Creating blocks: Each paragraph or section identified becomes a basic block or a chunk. This approach aligns well with the logical flow of information, especially in structured documents like procedures. Each chunk ideally represents a distinct step, idea, or concept within the document.

By using structural chunking, we ensure that chunks are semantically meaningful and correspond to logical units of information. This is particularly beneficial for procedures, where each chunk can represent a specific step or instruction. This method respects the inherent organization of the text, making it easier to understand the context and relationships between different parts of the document. Structural chunking sets the stage for more refined chunking by breaking down the text into manageable, meaningful segments.

2.2. Step 2 – Sliding Window on Tokens

While structural chunking provides a solid foundation, some paragraphs or sections may still be too long for optimal processing by embedding models or large language models (LLMs). To address this, we introduce a sliding window technique. This involves:

  • Defining a window size: Set a maximum number of tokens for each chunk (e.g., 300 tokens).
  • Setting an overlap: Define an overlap between consecutive chunks (e.g., 80 tokens). This overlap ensures that context is preserved across chunks and prevents the artificial separation of related information.
  • Creating overlapping chunks: If a block is longer than the window size, apply the sliding window to create multiple chunks. For instance, a block of 800 tokens, with a window size of 300 tokens and an overlap of 80 tokens, would be divided as follows:
    • Chunk 1: Tokens 0–300
    • Chunk 2: Tokens 220–520
    • Chunk 3: Tokens 440–740

This sliding window approach ensures that chunks remain within the acceptable length limits for downstream models while maintaining contextual continuity. The overlap is crucial because it prevents the separation of phrases or steps across chunks, thereby preserving the flow of information. This hybrid approach effectively balances structural integrity with practical length constraints, making it a versatile solution for chunking diverse types of documents.

3. How to Technically Describe Chunking

When presenting your chunking strategy, it's essential to be clear and concise. A well-structured explanation will help stakeholders understand the process and its benefits. Here's a way to describe the technical aspects of chunking:

Chunking (Lot 2)

  • Input: Raw text parsed by Tika (stored in the Elasticsearch documents index).
  • Step 1 – Structural Segmentation:
    • Process: Separate text into sections and paragraphs based on \n\n, titles, lists, and other structural markers.
    • Objective: Align each chunk with a business unit (step, section of a procedure).
  • Step 2 – Sliding Window on Tokens:
    • Process: For each block, create segments of approximately 300 tokens with an overlap of around 80 tokens.
    • Objectives:
      • Ensure each chunk remains within the limits of embedding models and LLMs.
      • Maintain context between successive chunks.
  • Output:
    • chunk_id
    • doc_id (reference to the original document)
    • chunk_index (order within the document)
    • chunk_text (text of the chunk)
    • start_token / end_token or character offset (optional)

This structured description provides a clear overview of the chunking process, from input to output. It highlights the two-step nature of the hybrid approach and explains the objectives behind each step. By including details such as the approximate chunk size (300 tokens) and overlap (80 tokens), you provide concrete information that can be easily understood and implemented. This level of clarity is crucial for technical discussions and documentation.

4. How to Argue for Your Choice (Points to Discuss in Meetings)

When presenting your chunking strategy, it's important to articulate the rationale behind your choices. Here’s a structured way to argue for your approach:

  • 🎯 Objectives:
    • Granularity: Aim for a relevant level of granularity—neither the entire document (too large) nor isolated sentences (too small).
    • Optimization: Optimize both:
      • Search relevance.
      • LLM response quality.
      • Costs (embeddings, storage, latency).
  • âś… Why Not “Entire Document”?
    • Too long for models (embeddings + LLMs).
    • Risk of introducing noise, leading to less precise LLM responses.
    • Impossible to quickly pinpoint the exact part of the procedure.
  • âś… Why Not “Phrase by Phrase”?
    • Too fine-grained:
      • Explosion in the number of chunks.
      • Loss of context.
      • Excessive indexing and embedding costs.
    • A procedure is often understandable by section, not by isolated sentences.
  • âś… Why Structural Chunking + Sliding Window Is a Good Compromise:
    • Respects Business Boundaries: A chunk is approximately a step or section of the procedure.
    • Context Preservation: Overlap ensures that if a phrase begins at the end of a chunk, it’s also present at the beginning of the next chunk.
    • Controlled Size for Models: A window of 300 tokens is compatible with embeddings and RAG, avoiding excessively long chunks that dilute meaning.
    • Good Quality/Cost Ratio: A reasonable number of chunks, enough context for semantic search, and no vector explosion.

You can even summarize it in one sentence:

“We’ve chosen a hybrid chunking approach by section + sliding window (~300 tokens with overlap) to respect the business structure of procedures while optimizing semantic relevance and embedding costs.”

This structured argument addresses potential concerns and demonstrates the thoughtful considerations behind the chosen strategy. By highlighting the trade-offs and benefits, you can effectively communicate the value of your chunking approach to stakeholders.

5. How It Integrates into Your “Keyword → Chunk → Doc / Vector → Chunk → Doc” Design

With this chunking strategy, your architecture becomes coherent and efficient. Here’s how it fits into the overall design:

  • Inverted Index (BM25):
    • Tokens are indexed at the chunk_text level.
    • Each entry in the inverted index contains: token → {chunk_id, doc_id}
    • ⇒ Keyword associated with a chunk associated with a document âś…
  • Vector Database:
    • Calculate an embedding per chunk_text.
    • Store in ES: vector, chunk_id, doc_id
    • ⇒ A vector corresponds to a chunk corresponds to a document âś…

Therefore, your textual architecture becomes coherent:

  • Ingestion:
    • Hybrid Chunking
    • Embedding per chunk
    • Inverted index by chunk
  • Hybrid Search:
    • BM25 on chunk_text
    • Semantic search on vector
    • Ranking/RRF at the chunk level, then grouping by document if needed

This integration ensures that both keyword-based and semantic searches can effectively leverage the chunked data. The consistent link between keywords, vectors, chunks, and documents allows for a flexible and powerful search experience. The hybrid search approach combines the strengths of both methods, providing comprehensive results that are both relevant and semantically meaningful.

Choosing Your Chunk Type: Key Considerations

When deciding on a chunking strategy, several factors come into play. These considerations will help you tailor your approach to the specific characteristics of your documents and the goals of your system.

Document Structure

The structure of your documents is a primary factor. If you're working with procedures, for example, they often have titles, sections, and steps. This inherent structure provides a logical basis for chunking. Each block (section) can represent a single idea or step, making it easier to understand and process the information.

Intended Use

The intended use of the chunks also influences the chunking strategy. If you plan to use the chunks for:

  • Semantic search: You need chunks that are large enough to capture the semantic meaning of the text.
  • RAG with LLMs: Chunks should be comprehensive enough to provide context for generating accurate responses.

This means that a chunk must:

  • Be long enough to be understandable on its own.
  • Be short enough to be precise and not overwhelm memory or the model's context window.

Technical Constraints

Technical limitations also play a role. Consider:

  • Embedding Models: These models often have an optimal input size (e.g., 200–500 tokens).
  • LLMs: These models have context window limits (though you’ll only send the top-N chunks).

In conclusion, your chunks should align with the logical structure of your documents (sections/paragraphs) and stay within the size limits of your models (typically ~300–500 tokens).

2. Major Chunking Methods

There are several ways to approach chunking, each with its own strengths and weaknesses. Here’s an overview of the major methods:

🟦 Option A – Chunking by Paragraphs/Sections

  • Idea: Use the text's structure (line breaks, titles, “Step 1 / Step 2”, etc.) to chunk.
  • Process: Separate text based on:
    • \n\n (double newline).
    • Titles (regex on “1.”, “2.”, “A.”, etc.).
    • Logical sections (“Objective”, “Scope”, “Process”, etc.).
  • Advantages:
    • Each chunk has meaning (a paragraph/step).
    • Perfect for procedures (which are often well-structured).
  • Disadvantages:
    • Some paragraphs may be too long or too short.
    • May require post-processing for size.

🟧 Option B – Chunking by Length (Characters or Tokens) + Overlap (Sliding Window)

  • Idea: Largely ignore the structure and chunk “blindly”:
    • Windowing, e.g., 300–500 tokens.
    • With 20–30% overlap (sliding window).
  • Example:
    • Chunk 1: tokens 0–400
    • Chunk 2: tokens 300–700
    • Chunk 3: tokens 600–1000
    • etc.
  • Advantages:
    • Simple, controllable.
    • Stable for embedding models.
  • Disadvantages:
    • May cut off phrases, steps.
    • Less aligned with the business structure of procedures.

🟨 Option C – Hybrid (Recommended)

  • Idea:
    • First, chunk by paragraphs/sections.
    • Then, if a block is too long → re-chunk it into sub-chunks (by max length, with overlap).
  • Example:
    • Paragraph = 1500 tokens
      • → Re-chunk into chunks of 400 tokens with 80–100 token overlap.
    • Paragraph = 150 tokens
      • → Keep as is.
  • Advantages:
    • Respects business structure
    • Meets technical constraints of models
    • Ideal for your “PDF procedures” use case.

The hybrid approach offers a balanced solution that leverages the strengths of both structural and length-based chunking. It’s particularly well-suited for documents with a clear organizational structure, such as procedures, while also ensuring that chunks remain within manageable size limits.

3. Concrete Recommendation for You (PDF Procedures in ES)

Given the context of working with IT/risk procedures parsed with Tika and stored in Elasticsearch (ES), here’s a concrete recommendation for a chunking strategy:

🔹 Step 1 – Retrieve Text from Elasticsearch

For each document:

  • field type: content, text, body (to be confirmed with the Lot 1 team)
  • Also retrieve:
    • id_procedure
    • title
    • Useful metadata (version, date, type…)

Construct an object like:

{
  "doc_id": "PROC_1234",
  "title": "Procedure for Managing Hardware Incidents",
  "content": "Title 1...\n\nParagraph 1...\n\nParagraph 2...\n\n..."
}

Step 2 – First Chunking: Paragraphs/Sections

You can:

  • Split on \n\n, or
  • Use patterns from your procedures (e.g., “1. Objective”, “2. Scope”, “3. Process”, “4. Roles & Responsibilities”, etc.).

Result (example):

  • Chunk 1 = “Objective” section
  • Chunk 2 = “Scope” section
  • Chunk 3 = “Process – Steps 1–2”
  • Chunk 4 = “Process – Steps 3–4”

🔹 Step 3 – Second Chunking: Length Limit + Overlap

For each raw chunk:

  • If < 200–300 tokens → keep it as is
  • If > 400–500 tokens → re-chunk with a sliding window

Example:

  • “Process” paragraph = 1200 tokens
    • Chunk 1: tokens 0–400
    • Chunk 2: tokens 320–720
    • Chunk 3: tokens 640–1040

This gives you chunks like:

{
  "doc_id": "PROC_1234",
  "chunk_id": "PROC_1234_01",
  "chunk_text": "Section Objective ...",
  "position": 1
}
{
  "doc_id": "PROC_1234",
  "chunk_id": "PROC_1234_02",
  "chunk_text": "Section Scope ...",
  "position": 2
}
...

You can then:

  • Create embeddings on chunk_text
  • Index this in a vector index in ES

🔹 What Type of Chunk to Choose?

For your needs, a good starting setting is:

  • Target Size: 300–500 tokens
  • Overlap: 20–30% (60–120 tokens)
  • Logic: Hybrid chunking (paragraph → re-split if too long)

You can then:

  • Run a small POC
  • Measure result quality for different sizes (200, 400, 600 tokens)

This concrete recommendation provides a practical guide for implementing a hybrid chunking strategy in your specific environment. By outlining the steps involved and providing specific parameters, it makes it easier to get started and fine-tune your approach.

4. How to Decide in Practice?

To make informed decisions about your chunking strategy, consider the following practical factors:

Expected Question Types

  • Very precise questions: Shorter chunks (200–300 tokens)
  • More general questions: Larger chunks (400–600 tokens)

The type of questions users are likely to ask will influence the optimal chunk size. Precise questions often require fine-grained information, while broader questions may benefit from more context.

Average Procedure Length

  • Very long PDFs (20–30 pages): More aggressive chunking + reasonable overlap
  • Short sheets: Paragraph chunking may suffice

The length of your documents will also affect your chunking strategy. Longer documents may require more aggressive chunking to ensure efficiency and relevance.

Tests with Stakeholders

  • A few concrete scenarios (3–5 frequent questions)
  • See if the returned chunks really contain the right info

Testing your chunking strategy with real-world scenarios and feedback from stakeholders is crucial. This ensures that the chosen approach aligns with the needs of your users and the goals of your system.

By carefully considering these practical factors, you can refine your chunking strategy to achieve the best possible results.

In conclusion, chunking is a critical step in document processing, and the hybrid approach combining structural chunking with a sliding window technique offers a versatile and effective solution. By understanding the various options and carefully considering your specific needs, you can optimize your chunking strategy for maximum performance. For further reading on best practices in information retrieval, visit resources like https://www.elastic.co/.