Tags: pipeshub-ai/pipeshub-ai
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fix(indexing): size chunks by tokens, not characters, against the emb… …edder limit (#3286) * fix(indexing): size chunks by tokens, not characters, against the embedder limit Every block-sizing constant in the indexing path bounds characters, but the limit that rejects input is the embedding model's, in tokens: _MAX_BLOCK_CHARS_FOR_SENTENCE_SPLIT = 50_000 vectorstore.py:108 MAX_TEXT_BLOCK_CHARS = 50_000 text_splitting.py:23 text-embedding-3-{small,large} = 8_191 TOKENS 50,000 characters is roughly 12,500 tokens of English prose -- about 1.5x the ceiling. So the trigger that decides "too large to embed as one document" sits past the point where embedding fails: a 45,000-character block takes the small-enough branch and is emitted as a single ~10,000-token document, which the provider rejects with "maximum input length is 8192 tokens". That error is classified non-retriable, so the whole record fails -- losing the rest of a document because one paragraph was long. Three paths could emit an over-limit document, and fixing only the first leaves two open: 1. vectorstore.py:275 -- a block under the character cap is emitted whole. Between ~8k and ~12.5k tokens that single document is over the limit. 2. _chunk_oversized_text packs sentences into ~1,500-char windows, which is safe, except that a sentence LONGER than the window is appended as its own chunk unsplit. A table row, a base64 payload, a minified line, or text in a script the splitter does not segment arrives as exactly that. 3. split_into_sentences output is emitted directly, with the same hole. So this adds token-counting helpers and enforces the ceiling at all three. Character counts cannot approximate this, which is the reason for the change rather than a larger constant. Measured with tiktoken cl100k_base: input chars tokens under 50k? over 8,191? English prose 45,000 10,001 yes yes base64 payload 43,200 27,000 yes yes, 3.3x English prose 49,800 6,641 yes no The base64 row is 6x the token density of the first in a SHORTER string. Any single characters-per-token constant is wrong for one of them. Notes on the implementation: * The character cap is kept as a cheap pre-filter, not removed. A string shorter than ceiling * 3 characters cannot exceed the ceiling, so the common case never pays for tokenization. * The tiktoken import is optional. When it is unavailable the fallback estimates at 3 chars/token, which over-estimates and therefore errs toward splitting rather than toward emitting something that will be rejected. * The limit is configurable via PIPESHUB_EMBED_TOKEN_LIMIT, since ada-002 accepts 2,048 rather than 8,191 and self-hosted models vary. A malformed value falls back to the default rather than disabling the ceiling. * Chunk SIZING is deliberately unchanged. The ~1,500-character window and the sentence-splitting behaviour are untouched -- this is a correctness fix, not a retrieval-tuning change, and conflating the two makes it unmeasurable. Tests: 15 cases in tests/unit/modules/transformers/test_vectorstore_token_sizing.py. They encode the regression rather than the implementation -- each over-limit case asserts len(text) < 50_000 alongside the token assertion, so the test states "under the character cap AND over the token ceiling". One asserts "".join(pieces) == text, because a split that silently dropped content would otherwise pass while reintroducing the bug. Existing vectorstore tests are unaffected: the failure set before and after this change is identical, and the pass count rises by exactly the 15 added here. * fix(indexing): guarantee the token bound and keep splits lossless Addresses review on #3286. Three of the findings were correct and are fixed here; measurements below rather than assertions. 1. The no-tokenizer fallback was not a bound. It estimated 3 characters per token and the docstring called that "deliberately pessimistic". That is true only for ASCII prose. Measured against cl100k_base: input chars bytes tokens chars/token len/3 estimate ASCII prose 1200 1200 201 5.97 400 safe CJK 1100 3300 1000 1.10 366 UNDER by 2.7x emoji 100 400 300 0.33 33 UNDER by 9x base64 1600 1600 900 1.78 533 UNDER by 1.7x It under-counted on three of four input classes. A BPE token encodes at least one byte, so the UTF-8 byte length is a guaranteed upper bound for any input; _token_len now uses it. 2. The same error was in the cheap pre-filter, and worse there: it compared CHARACTERS against the ceiling, and a character count is not an upper bound on tokens -- one emoji is a single character and three tokens. So the guard waved through exactly the dense input the function exists to catch. It now measures bytes, with an isascii() fast path where characters and bytes coincide. 3. Splitting was lossy on multi-byte text. A token boundary is not a UTF-8 character boundary, and Encoding.decode substitutes U+FFFD for a partial sequence. Measured: 4,600 tokens of CJK and emoji split into 92 pieces produced 20 replacement characters and did NOT rejoin to the source. Slicing now goes through decode_bytes and an incremental decoder, which carries an incomplete trailing sequence into the next piece. The original "no content is dropped" test passed only because it used ASCII, where the failure cannot occur. That was a hole in the test, not bad luck. And the resulting pieces are now VERIFIED rather than assumed. Two effects make a slice of `ceiling` units come back over `ceiling` tokens: carrying a partial character forward adds bytes to the following piece, and re-encoding a decoded slice does not always reproduce its token count because BPE merges differ once the text is cut. Both are small; small is not a bound. Each attempt is measured and the step halved until every piece fits. Tests rewritten: 27 cases, up from 15. * the ceiling is a fixed local constant, not _embed_token_ceiling(), so an exported PIPESHUB_EMBED_TOKEN_LIMIT cannot move the fixtures out from under the assertions * tiktoken is not a declared dependency, so both paths are covered: tests that need an encoder are skipped without one, and a fixture forces the fallback path so it is exercised even where tiktoken is installed * losslessness is asserted across CJK, emoji and Latin-extended input, not only ASCII, and explicitly asserts no U+FFFD appears * the fallback path has its own bounded-and-lossless test Existing vectorstore tests remain unaffected: identical failure set before and after, pass count up by exactly the tests added.
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