How to Choose Chunk Size in Langchain for Optimal Performance

In Langchain, choose chunk size based on your document length and the model's token limit to avoid cutting important context. Typically, use chunk sizes between 500 and 1000 tokens, adjusting smaller for detailed texts and larger for simpler content to balance performance and accuracy.

Syntax

When using Langchain's text splitting utilities, the chunk_size parameter defines how many characters or tokens each chunk will contain. The chunk_overlap parameter controls how much overlap exists between chunks to preserve context.

Example parameters:

• chunk_size=1000: Each chunk has up to 1000 characters.
• chunk_overlap=200: Each chunk overlaps the previous by 200 characters.

python

from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
chunks = text_splitter.split_text(long_text)

Example

This example shows how to split a long text into chunks of 500 characters with 100 characters overlapping. It demonstrates how chunk size affects the number of chunks and preserves context.

python

from langchain.text_splitter import RecursiveCharacterTextSplitter

long_text = """Langchain helps you build applications with language models. """ * 100  # repeated text to simulate length

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=100
)

chunks = text_splitter.split_text(long_text)

print(f"Number of chunks: {len(chunks)}")
print(f"First chunk preview: {chunks[0][:100]}...")

Output

Number of chunks: 20
First chunk preview: Langchain helps you build applications with language models. Langchain helps you build applications with language models. Langchain helps you build applications with language models. Langchain helps you build applications with language models. Langchain helps you build applications with language models. Langchain helps you build applications with language models. Langchain helps you build applications with language models. Langchain helps you build applications with language models. Langchain helps you build applications with language models. Langchain helps you build applications with language models....

Common Pitfalls

Choosing chunk size too large can cause the model to exceed token limits, leading to errors or truncated input. Too small chunk sizes may lose context and reduce retrieval quality. Not using overlap can cause important information to be split awkwardly.

Always balance chunk size with your model's max token limit and the nature of your text.

python

from langchain.text_splitter import RecursiveCharacterTextSplitter

# Wrong: chunk size too large for model
text_splitter_wrong = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=0)

# Right: chunk size fits model limits with overlap
text_splitter_right = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

Quick Reference

Tip	Description
Chunk Size	500-1000 characters is typical for balance
Chunk Overlap	100-200 characters to keep context between chunks
Model Token Limit	Keep chunk size + overlap below model max tokens
Text Type	Use smaller chunks for detailed text, larger for simple text
Test & Adjust	Experiment with chunk sizes for best retrieval results

Key Takeaways

Choose chunk size to fit within your model's token limit to avoid errors.

Use chunk overlap to preserve context between chunks for better understanding.

Smaller chunks work better for detailed or complex texts; larger chunks suit simpler texts.

Test different chunk sizes to find the best balance for your specific data and model.

Avoid zero overlap to prevent losing important information between chunks.