ValueError
builtins.ValueError
Stack trace
ValueError: chunk size 4096 is larger than the model's context window size 2048
Why it happens
LLMs have a fixed maximum context window size that limits how many tokens can be processed at once. When a chunk size for text splitting or embedding exceeds this limit, the system cannot process the input, triggering this error. This often happens when default chunk sizes are too large or when using models with smaller context windows.
Detection
Monitor chunk sizes before passing inputs to the LLM or vector store; assert chunk size <= model context window size to catch issues early.
Causes & fixes
Chunk size parameter is set larger than the model's maximum context window size
Reduce the chunk size parameter to be equal or smaller than the model's context window size, e.g., 2048 tokens for GPT-4o-mini.
Using a model with a smaller context window than expected without adjusting chunk size
Check the model's documented max context window and configure chunk size accordingly before processing.
Forgetting to account for additional tokens added by prompt templates or system messages
Subtract estimated prompt and system message tokens from the context window size when setting chunk size.
Code: broken vs fixed
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=4096, chunk_overlap=200)
chunks = text_splitter.split_text(long_text) # ValueError: chunk size larger than context window errorimport os
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Set chunk_size to 2048 to fit GPT-4o-mini context window
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2048, chunk_overlap=200)
chunks = text_splitter.split_text(long_text)
print(f"Number of chunks: {len(chunks)}") # Fixed chunk size within context windowWorkaround
Catch the ValueError exception and dynamically reduce the chunk size in a loop until it fits the context window size.
Prevention
Always check the model's max context window size from official docs and configure chunk sizes accordingly before processing inputs.