Maximum Chunk Size for RAG
What would be the maximum Chunk Size that I can use with this embedding model, if I want to split up my documents into chunks for RAG?
It would be 512 tokens.
Hi, I have a follow up question. What is the expected behaviour when the passed text is longer than 512 tokens? I assume it gets cut off at 512.
Should we account for the "passage:" prefix when chunking the documents?
i.e. should f"passage: {doc.page_content}" be 512 tokens long or doc.page_content itself?
And with this being the max_len for a chunk, is there an optimal_len we should aim for?
Yes—count passage: as part of the input, and include the special tokens in the 512-token budget. The pinned model-card example adds the prefix before tokenizing with max_length=512; its Sentence Transformers config also sets 512.
A tokenizer-only CPU check on October 5 with Transformers 5.15.0 illustrates the boundary:
from transformers import AutoTokenizer
repo = "intfloat/multilingual-e5-large"
rev = "3d7cfbdacd47fdda877c5cd8a79fbcc4f2a574f3"
tok = AutoTokenizer.from_pretrained(repo, revision=rev, trust_remote_code=False)
bare = "a " * 510
for text in (bare, "passage: " + bare):
full = tok(text, truncation=False)["input_ids"]
cut = tok(text, truncation=True, max_length=512)["input_ids"]
print(len(full), len(cut), len(full) - len(cut))
This printed 512 512 0 and 514 512 2: that bare chunk fits, but adding the prefix causes two content tokens to be dropped. Both counts include . In a second check, prefixed chunks ending in cat versus dog produced identical retained IDs at 512; shorter versions using 505 tokens retained the difference. Allowing 514 in the tokenizer retained it too, but that is only a tokenizer control, not evidence that the model supports a larger context.
For your chunks, measure len(tok("passage: " + doc.page_content, truncation=False)["input_ids"]) and keep that complete count at or below 512 to avoid this truncation. The two-token prefix increase above belongs to these synthetic strings; measure your prepared text rather than assuming token costs always add independently.
This check does not establish an optimal chunk length or retrieval quality, and it ran no embeddings or serving backend. It used previously acquired pinned tokenizer/config files; a clean installation and the snippet's online acquisition path were not tested.
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