Recursive Chunking with LangChain

Capsule overview

Recursive chunking is the optimal balance between simplicity and quality: it respects the document's structure (paragraphs, sentences) without semantic chunking's cost. LangChain implements RecursiveCharacterTextSplitter, which tries to split using hierarchical separators (paragraphs → sentences → words → chars) until it finds chunks of the size you want.

This strategy lands +10-15% precision over fixed-size at zero extra cost (no API calls, instant). It's the recommended default for production in 90% of cases: simple to configure, structure-aware, and it uses chunk overlap to preserve context between chunks.

This capsule teaches you how recursive splitting works, how to implement it with LangChain, how to tune custom separators for different document types, and how to benchmark it against fixed-size to quantify the gain.

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🔄 How recursive chunking works

The concept: hierarchical separators

Recursive chunking tries to split the document using separators in priority order:

separators = [
    "\n\n",    # 1. Try paragraphs first (the most coherent unit)
    "\n",      # 2. If a paragraph is too big, split by line
    ". ",      # 3. If a line is too big, split by sentence
    " ",       # 4. If a sentence is too big, split by word
    ""         # 5. If all else fails, split by character (last resort)
]

The process:

1. Try to split the document by \n\n (paragraphs)
2. If the resulting chunk is < chunk_size → ✅ keep it
3. If the resulting chunk is > chunk_size → try the next separator (\n)
4. Repeat recursively until the chunks are the right size

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A visual example:

Original document:
┌────────────────────────────────────────┐
│ Paragraph 1: FastAPI is a modern, fast │
│ web framework.                         │ ← 50 chars
│                                        │
│ Paragraph 2: Features:                 │
│ - High performance                     │
│ - Automatic validation                 │ ← 120 chars
│ - Automatic documentation              │
│                                        │
│ Paragraph 3: FastAPI history.          │ ← 30 chars
└────────────────────────────────────────┘

↓ Recursive chunking (chunk_size=100, separators=["\n\n", "\n", ". "])

Step 1: Split by "\n\n" (paragraphs)
├─ Chunk A: "Paragraph 1..." (50 chars) ✅ < 100 chars → Keep
├─ Chunk B: "Paragraph 2..." (120 chars) ❌ > 100 chars → Split further
│   ↓ Split by "\n" (lines)
│   ├─ "Features:" (9 chars)
│   ├─ "- High performance" (18 chars)
│   ├─ "- Automatic validation" (22 chars)
│   ├─ "- Automatic documentation" (25 chars)
│   ↓ Group them back up to ~100 chars
│   ├─ Chunk B1: "Features:\n- High performance\n- Automatic validation" (50 chars) ✅
│   └─ Chunk B2: "- Automatic documentation" (25 chars) ✅
└─ Chunk C: "Paragraph 3..." (30 chars) ✅ < 100 chars → Keep

The result:
├─ Chunk 1: Paragraph 1, complete
├─ Chunk 2: Features (part 1)
├─ Chunk 3: Features (part 2)
└─ Chunk 4: Paragraph 3, complete

The benefit: every chunk respects the document's logical structure

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💻 Implementing it with LangChain

Step 1: Installation

pip install langchain-text-splitters==0.0.1

---

Step 2: Basic usage

from langchain.text_splitters import RecursiveCharacterTextSplitter

# A sample document
document = """
FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.8+.

Main features:
- Fast: Very high performance, comparable to NodeJS and Go
- Easy: Designed to be easy to use and learn
- Robust: Production-ready code with automatic validation
- Standards-based: OpenAPI and JSON Schema

FastAPI was created by Sebastián Ramírez in 2018. It has been adopted by companies like Microsoft, Netflix, and Uber.
"""

# Configure the splitter
splitter = RecursiveCharacterTextSplitter(
    chunk_size=200,         # The chunk's maximum size
    chunk_overlap=20,       # The overlap between chunks (it preserves context)
    separators=["\n\n", "\n", ". ", " ", ""],  # The separators, in priority order
    length_function=len     # The function that measures size (len() or tiktoken)
)

# Split the document
chunks = splitter.split_text(document)

# Show the results
for i, chunk in enumerate(chunks, 1):
    print(f"Chunk {i} ({len(chunk)} chars):")
    print(f"{chunk}")
    print("=" * 60)

Output:

Chunk 1 (94 chars):
FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.8+.
============================================================
Chunk 2 (176 chars):
Main features:
- Fast: Very high performance, comparable to NodeJS and Go
- Easy: Designed to be easy to use and learn
- Robust: Production-ready code with automatic validation
============================================================
Chunk 3 (42 chars):
- Standards-based: OpenAPI and JSON Schema
============================================================
Chunk 4 (117 chars):
FastAPI was created by Sebastián Ramírez in 2018. It has been adopted by companies like Microsoft, Netflix, and Uber.
============================================================

The analysis:

• ✅ Chunk 1: a complete paragraph (a coherent intro)
• ✅ Chunk 2: the feature list (part 1, coherent)
• ✅ Chunk 3: the feature list (part 2, it completes the list)
• ✅ Chunk 4: a complete paragraph (the history, coherent)

Compared with fixed-size:

# Fixed-size (chunk_size=200)
fixed_chunks = [document[i:i+200] for i in range(0, len(document), 200)]

# Chunk 1 (fixed): "FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.8+.\n\nMain features:\n- Fast: Very high performance, comparable to NodeJS and Go\n- Easy: Designed to be easy t" ← Cut mid-sentence
 
# Chunk 1 (recursive): "FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.8+." ← A complete paragraph

The gain: recursive respects the structure → coherent chunks → +10-15% precision

---

🎛️ Advanced configuration

Parameter 1: chunk_size

# A small chunk size (100 chars)
splitter_small = RecursiveCharacterTextSplitter(chunk_size=100)
chunks_small = splitter_small.split_text(document)
print(f"Small chunks: {len(chunks_small)}")  # 8 chunks

# A large chunk size (500 chars)
splitter_large = RecursiveCharacterTextSplitter(chunk_size=500)
chunks_large = splitter_large.split_text(document)
print(f"Large chunks: {len(chunks_large)}")  # 2 chunks

The trade-off:

Chunk size	Pros	Cons	When to use
Small (100-200)	✅ High precision (specific matches)	❌ Can lose context	Specific queries
Medium (300-500)	✅ Balances precision and context	⚠️ Neutral	The recommended default
Large (700-1000)	✅ Lots of context	❌ Low precision (lots of noise)	Long-form narrative

The recommendation: 400-600 chars (the optimal balance)

---

Parameter 2: chunk_overlap

# No overlap
splitter_no_overlap = RecursiveCharacterTextSplitter(
    chunk_size=200,
    chunk_overlap=0  # No overlap
)

# With overlap
splitter_with_overlap = RecursiveCharacterTextSplitter(
    chunk_size=200,
    chunk_overlap=50  # 25% overlap (50/200)
)

Why does overlap matter?

document = """
FastAPI is a web framework. It uses Pydantic for validation.
Pydantic is a Python library. It validates types automatically.
"""

# No overlap (chunk_size=60)
chunks_no_overlap = splitter_no_overlap.split_text(document)

# Chunk 1: "FastAPI is a web framework. It uses Pydantic for validation."
# Chunk 2: "Pydantic is a Python library. It validates types..."

# Query: "How does FastAPI use Pydantic for validation?"
# The problem: the concept "FastAPI + Pydantic + validation" sits across Chunks 1 and 2
# Chunk 1: has "FastAPI" + "Pydantic" + "validation"
# Chunk 2: has "Pydantic" + "validates" but no "FastAPI"
# The result: no chunk has the full context → a partial match


# With overlap (chunk_size=60, overlap=20)
chunks_with_overlap = splitter_with_overlap.split_text(document)

# Chunk 1: "FastAPI is a web framework. It uses Pydantic for validation."
# Chunk 2: "It uses Pydantic for validation. Pydantic is a Python library..." ← The overlap preserves the context

# Query: "How does FastAPI use Pydantic for validation?"
# Chunk 2: has "Pydantic for validation" (from the overlap) + the explanation of Pydantic
# The result: the chunk has the full context → a full match

The trade-off:

Overlap	Pros	Cons
0% (no overlap)	✅ Less storage	❌ Loses context between chunks
10-20% overlap	✅ Balances context and storage	⚠️ +10-15% storage
30-50% overlap	✅ Maximum context	❌ +30-50% storage, duplication

The recommendation: 10-20% overlap (50-100 chars for chunk_size=500)

---

Parameter 3: separators (custom)

# The default separators (for generic documents)
default_separators = ["\n\n", "\n", ". ", " ", ""]

# Custom separators for Python code
code_separators = [
    "\nclass ",      # Split by class first
    "\ndef ",        # Then by function
    "\n    ",        # Then by indentation
    "\n",            # Then by line
    " ",             # Words
    ""               # Chars (last resort)
]

# Custom separators for Markdown
markdown_separators = [
    "\n## ",         # Split by H2 headers first
    "\n### ",        # Then by H3 headers
    "\n\n",          # Then by paragraph
    "\n",            # Lines
    ". ",            # Sentences
    " ",             # Words
    ""               # Chars
]

# An example with Python code
python_code = """
class User:
    def __init__(self, name):
        self.name = name
    
    def greet(self):
        return f"Hello, {self.name}"

class Product:
    def __init__(self, name, price):
        self.name = name
        self.price = price
"""

splitter_code = RecursiveCharacterTextSplitter(
    chunk_size=100,
    chunk_overlap=10,
    separators=code_separators
)

chunks = splitter_code.split_text(python_code)

# Output:
# Chunk 1: the complete class User
# Chunk 2: the complete class Product
# The benefit: it respects the code's structure (no cutting mid-class)

---

📊 Benchmarking: fixed-size vs recursive

The experiment:

# benchmark_chunking.py
from langchain.text_splitters import RecursiveCharacterTextSplitter
import time
import statistics

def benchmark_chunking_strategies(document: str, num_iterations: int = 100):
    """Compares fixed-size vs recursive chunking"""
    
    # Strategy 1: Fixed-size
    def fixed_size_chunking(text, size=500):
        return [text[i:i+size] for i in range(0, len(text), size)]
    
    # Strategy 2: Recursive
    recursive_splitter = RecursiveCharacterTextSplitter(
        chunk_size=500,
        chunk_overlap=50,
        separators=["\n\n", "\n", ". ", " ", ""]
    )
    
    # Measure the latency
    fixed_times = []
    for _ in range(num_iterations):
        start = time.time()
        fixed_chunks = fixed_size_chunking(document, size=500)
        fixed_times.append((time.time() - start) * 1000)  # ms
    
    recursive_times = []
    for _ in range(num_iterations):
        start = time.time()
        recursive_chunks = recursive_splitter.split_text(document)
        recursive_times.append((time.time() - start) * 1000)  # ms
    
    # Measure coherence (proxy: how many chunks end with punctuation)
    fixed_final = fixed_size_chunking(document)
    recursive_final = recursive_splitter.split_text(document)
    
    fixed_coherence = sum(1 for c in fixed_final if c.rstrip()[-1] in '.!?') / len(fixed_final)
    recursive_coherence = sum(1 for c in recursive_final if c.rstrip()[-1] in '.!?') / len(recursive_final)
    
    return {
        "fixed": {
            "num_chunks": len(fixed_final),
            "avg_latency_ms": statistics.mean(fixed_times),
            "coherence": fixed_coherence
        },
        "recursive": {
            "num_chunks": len(recursive_final),
            "avg_latency_ms": statistics.mean(recursive_times),
            "coherence": recursive_coherence
        }
    }

# Run the benchmark
with open("data/fastapi_docs.txt", "r") as f:
    document = f.read()

results = benchmark_chunking_strategies(document)

print("Benchmark Results:")
print(f"\nFixed-Size:")
print(f"  - Chunks: {results['fixed']['num_chunks']}")
print(f"  - Latency: {results['fixed']['avg_latency_ms']:.2f}ms")
print(f"  - Coherence: {results['fixed']['coherence']:.2%}")

print(f"\nRecursive:")
print(f"  - Chunks: {results['recursive']['num_chunks']}")
print(f"  - Latency: {results['recursive']['avg_latency_ms']:.2f}ms")
print(f"  - Coherence: {results['recursive']['coherence']:.2%}")

Expected output:

Benchmark Results:

Fixed-Size:
  - Chunks: 250
  - Latency: 0.08ms
  - Coherence: 45%  ← Only 45% of the chunks end with punctuation (the rest are cut mid-sentence)

Recursive:
  - Chunks: 268
  - Latency: 1.2ms
  - Coherence: 89%  ← 89% of the chunks end with punctuation (complete sentences)

The analysis:

Metric	Fixed-size	Recursive	Delta
Num chunks	250	268	+7% (from the overlap)
Latency	0.08ms	1.2ms	+1.12ms (negligible)
Coherence	45%	89%	+44%

The conclusion: recursive has 2x better coherence for negligible latency (+1ms)

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🎯 Summary

Key concepts:

• ✅ Recursive chunking: splits by hierarchical separators (\n\n → \n → . → space → char)
• ✅ It respects structure: it prioritizes complete paragraphs and sentences over arbitrary cuts
• ✅ Chunk overlap: preserves context between chunks (+10-15% recall)
• ✅ Trade-offs: +1-2ms latency (negligible), +10-15% storage (the overlap), +44% coherence
• ✅ The gain over fixed-size: +10-15% precision, +5-8% recall
• ✅ The recommended default: chunk_size=500, overlap=50, default separators
• ✅ Custom separators: for code, HTML, Markdown (they respect each format's structure)

What's next:

Capsule 04 teaches you semantic chunking: grouping by semantic topic using embeddings for maximum coherence (+15-20% precision), with a trade-off in cost and latency.

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📚 Additional resources

1. LangChain RecursiveCharacterTextSplitter Docs - The official documentation
2. Chunking Strategies Deep Dive - Pinecone's guide
3. Optimal Chunk Size Research - The academic paper
4. Chunk Overlap Best Practices - A community discussion
5. Custom Separators Examples - The LangChain cookbook

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Created: February 6, 2026
Version: 1.0