Why does my sub-512MB file trigger a token limit error during indexing?

It sounds like the issue isn’t with the file size but with token limits. The 512 MB limit you’re referring to is a size constraint, but for language models, the real limit is the number of tokens in a prompt. Even if your file is under 512 MB, if the processed text (after any splitting, if applicable) results in more tokens than the model’s context window (for example, GPT‑3.5‑turbo’s limit of roughly 4096 tokens), you’ll run into errors.

To address this, make sure to split or summarize your input into smaller chunks that fit within the token limits. For example, you might use a text splitter like LangChain’s RecursiveCharacterTextSplitter:

PYTHONCopied
from langchain_text_splitters import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_documents(docs)

This ensures each chunk is within the acceptable token range for your model, avoiding issues when integrating with the retrieval tool.

In summary, the error is likely due to token count limitations rather than file size. Adjust your processing logic to account for the model’s token limits.