How to Intermediate · 3 min read

Sliding window context strategy

Quick answer

The sliding window context strategy manages inputs longer than an LLM's context window by splitting text into overlapping chunks and sequentially processing them. This approach preserves context continuity across chunks, enabling the model to handle long documents without losing relevant information.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the openai Python package and set your API key as an environment variable for secure access.

bash

pip install openai

output

Collecting openai
  Downloading openai-1.x.x-py3-none-any.whl (xx kB)
Installing collected packages: openai
Successfully installed openai-1.x.x

Step by step

This example demonstrates how to implement a sliding window over a long text to generate responses chunk by chunk, preserving context overlap.

python

import os
from openai import OpenAI

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

# Parameters
model = "gpt-4o"
max_context = 2048  # Example max tokens for context window
chunk_size = 1000  # Tokens per chunk
overlap = 200      # Tokens to overlap between chunks

# Simulated long text (replace with your actual text)
long_text = "".join(["This is sentence number {}.".format(i) for i in range(1, 300)])

# Simple tokenizer approximation: split by spaces
words = long_text.split()

# Function to get chunks with overlap
chunks = []
start = 0
while start < len(words):
    end = min(start + chunk_size, len(words))
    chunk = " ".join(words[start:end])
    chunks.append(chunk)
    if end == len(words):
        break
    start = end - overlap  # slide window back by overlap tokens

# Process each chunk with the model
responses = []
for i, chunk in enumerate(chunks):
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": f"Analyze this text chunk:\n{chunk}"}
    ]
    response = client.chat.completions.create(model=model, messages=messages)
    text = response.choices[0].message.content
    print(f"Chunk {i+1} response:\n{text}\n")
    responses.append(text)

output

Chunk 1 response:
[Model output analyzing first chunk of text]

Chunk 2 response:
[Model output analyzing second chunk with overlap]

... (continues for all chunks)

Common variations

Use async calls with asyncio for parallel chunk processing.
Adjust chunk_size and overlap based on model context limits and task sensitivity.
Use different models like gpt-4o-mini for cost-effective processing.
Incorporate streaming responses for real-time output.

Troubleshooting

If you get context length exceeded errors, reduce chunk_size or increase overlap cautiously.
Ensure your tokenizer matches the model's tokenization to avoid chunk misalignment.
Watch for repeated content in outputs due to overlap; tune overlap size accordingly.

✅

Key Takeaways

Split long inputs into overlapping chunks to fit within the model's context window.
Overlap tokens between chunks to maintain context continuity across segments.
Tune chunk and overlap sizes based on model limits and task requirements.

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

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