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How to set chunk overlap in LangChain

Q: How to set chunk overlap in LangChain

In LangChain, set chunk overlap by specifying the chunk_overlap parameter in text splitter classes like RecursiveCharacterTextSplitter. This controls how many characters overlap between chunks, preserving context across splits.

Quick answer

In LangChain, set chunk overlap by specifying the chunk_overlap parameter in text splitter classes like RecursiveCharacterTextSplitter. This controls how many characters overlap between chunks, preserving context across splits.

PREREQUISITES

Python 3.8+
pip install langchain>=0.2
Basic familiarity with LangChain text splitting

Setup

Install LangChain if you haven't already. Use the following command to install the latest LangChain package:

bash

pip install langchain>=0.2

Step by step

Use the RecursiveCharacterTextSplitter from langchain.text_splitter and set the chunk_overlap parameter to control how many characters overlap between chunks. This helps maintain context when processing long documents.

python

from langchain.text_splitter import RecursiveCharacterTextSplitter

text = """LangChain is a powerful framework for building applications with language models. """ \
       "When splitting text, chunk overlap helps preserve context across chunks. """ \
       "This example shows how to set chunk overlap in LangChain."""

# Initialize the text splitter with chunk size and overlap
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=50,
    chunk_overlap=10
)

# Split the text
chunks = text_splitter.split_text(text)

# Print the chunks
for i, chunk in enumerate(chunks, 1):
    print(f"Chunk {i}: {chunk}\n")

output

Chunk 1: LangChain is a powerful framework for building applications

Chunk 2: applications with language models. When splitting text, chunk overlap

Chunk 3: overlap helps preserve context across chunks. This example shows how

Chunk 4: how to set chunk overlap in LangChain.

Common variations

You can use other text splitters like CharacterTextSplitter or MarkdownTextSplitter which also support chunk_overlap. Adjust chunk_size and chunk_overlap based on your use case and model context window.

python

from langchain.text_splitter import CharacterTextSplitter

text_splitter = CharacterTextSplitter(
    chunk_size=100,
    chunk_overlap=20
)
chunks = text_splitter.split_text(text)
print(f"Number of chunks: {len(chunks)}")

output

Number of chunks: 2

Troubleshooting

If chunks are too small or too many, increase chunk_size or decrease chunk_overlap.
If context is lost between chunks, increase chunk_overlap to preserve more text.
Ensure your chunk_overlap is less than chunk_size to avoid errors.

✅

Key Takeaways

Set chunk_overlap in LangChain text splitters to preserve context between chunks.
Use RecursiveCharacterTextSplitter or CharacterTextSplitter with overlap for flexible chunking.
Adjust chunk_size and chunk_overlap based on your model's context window and task needs.

Verified 2026-04

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