Code intermediate · 3 min read

How to chunk code for RAG

Direct answer

Use text splitting techniques to divide code into logical, token-limited chunks for RAG, ensuring each chunk fits model context windows and preserves semantic boundaries.

Setup

Install

bash

pip install langchain openai

Env vars

OPENAI_API_KEY

Imports

python

import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from openai import OpenAI

Examples

indef add(a, b): return a + b print(add(2, 3))

out['def add(a, b):\n return a + b\n\nprint(add(2, 3))']

inclass Calculator: def add(self, a, b): return a + b def subtract(self, a, b): return a - b calc = Calculator() print(calc.add(5, 7))

out["class Calculator:\n def add(self, a, b):\n return a + b\n", " def subtract(self, a, b):\n return a - b\n\ncalc = Calculator()\nprint(calc.add(5, 7))"]

in# Large script with multiple functions and classes spanning 2000 tokens

out["Chunk 1 with first 1000 tokens", "Chunk 2 with next 1000 tokens"]

Integration steps

Import a text splitter like RecursiveCharacterTextSplitter from LangChain.
Load your code as a string and configure the splitter with chunk size and overlap.
Split the code into chunks respecting token limits and semantic boundaries.
Use these chunks as documents for embedding or retrieval in your RAG pipeline.
Query the retriever with user input to fetch relevant code chunks.
Pass retrieved chunks plus query to the LLM for augmented generation.

Full code

python

import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from openai import OpenAI

# Load your code as a string (example code snippet)
code_text = '''
class Calculator:
    def add(self, a, b):
        return a + b

    def subtract(self, a, b):
        return a - b

calc = Calculator()
print(calc.add(5, 7))
'''

# Initialize the text splitter with chunk size and overlap
splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,  # max tokens approx
    chunk_overlap=50,
    separators=["\n\n", "\n", " "]
)

# Split the code into chunks
chunks = splitter.split_text(code_text)

# Print chunks for inspection
for i, chunk in enumerate(chunks):
    print(f"Chunk {i+1} (length {len(chunk)} chars):\n{chunk}\n{'-'*40}")

# Example: Initialize OpenAI client for RAG retrieval or embedding
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Normally, you'd embed chunks and build a vector store here for retrieval
# This example only shows chunking logic

output

Chunk 1 (length 142 chars):
class Calculator:
    def add(self, a, b):
        return a + b

    def subtract(self, a, b):
        return a - b

----------------------------------------
Chunk 2 (length 27 chars):
calc = Calculator()
print(calc.add(5, 7))

----------------------------------------

API trace

Request

json

{"model": "gpt-4o", "messages": [{"role": "user", "content": "Retrieve relevant code chunks for query"}]}

Response

json

{"choices": [{"message": {"content": "Relevant code chunk text..."}}], "usage": {"total_tokens": 150}}

Extractresponse.choices[0].message.content

Variants

Streaming chunk output ›

Use streaming when processing large code chunks to provide incremental output and better UX.

python

import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from openai import OpenAI

code_text = '''def foo():\n    pass\n\n# More code...'''

splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=30)
chunks = splitter.split_text(code_text)

for chunk in chunks:
    print(chunk)

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Process this code chunk:" + chunk}],
    stream=True
)
for event in response:
    print(event.choices[0].message.content, end='')

Async chunking and retrieval ›

Use async when integrating chunking with concurrent API calls for efficiency.

python

import os
import asyncio
from langchain.text_splitter import RecursiveCharacterTextSplitter
from openai import OpenAI

async def chunk_and_query():
    code_text = 'def async_func():\n    pass\n'
    splitter = RecursiveCharacterTextSplitter(chunk_size=400, chunk_overlap=40)
    chunks = splitter.split_text(code_text)

    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

    for chunk in chunks:
        response = await client.chat.completions.acreate(
            model="gpt-4o",
            messages=[{"role": "user", "content": f"Analyze this code chunk:\n{chunk}"}]
        )
        print(response.choices[0].message.content)

asyncio.run(chunk_and_query())

Alternative model for cost efficiency ›

Use smaller models like gpt-4o-mini for cheaper, faster chunk summarization when high accuracy is not critical.

python

import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from openai import OpenAI

code_text = 'def cheap_model_func():\n    return True\n'

splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_text(code_text)

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

for chunk in chunks:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": f"Summarize this code chunk:\n{chunk}"}]
    )
    print(response.choices[0].message.content)

Performance

Latency~800ms for gpt-4o non-streaming calls

Cost~$0.002 per 500 tokens for gpt-4o

Rate limitsTier 1: 500 RPM / 30K TPM

Use chunk overlap sparingly to reduce redundant tokens.
Trim comments or non-essential code before chunking to save tokens.
Batch multiple chunks in one request if model context allows.

Approach	Latency	Cost/call	Best for
Standard chunking with gpt-4o	~800ms	~$0.002 per 500 tokens	High accuracy RAG
Streaming chunk output	Varies, faster perceived	Similar	Large codebases, better UX
Async chunking	Improved throughput	Similar	Concurrent processing
Using gpt-4o-mini	~400ms	~$0.0005 per 500 tokens	Cost-sensitive summarization

✓

Quick tip

Use semantic-aware text splitters like RecursiveCharacterTextSplitter to chunk code preserving logical blocks and token limits.

⚠

Common mistake

Splitting code arbitrarily by fixed character count without semantic awareness leads to broken code chunks and poor retrieval results.

Verified 2026-04 · gpt-4o, gpt-4o-mini

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