mirror of
https://github.com/tiennm99/DocsGPT.git
synced 2026-10-03 15:11:30 +00:00
Alex
7da46c2bea
- Ship tiktoken's cl100k_base inside the package and build the encoding from it, so token counting never downloads anything. - Default EMBEDDINGS_CACHE_DIR to <data home>/models instead of FastEmbed's temp dir, and read tokenizer.json and repo metadata from that cache, so a model downloads once and survives reboots. - TTS_PROVIDER=none and STT_PROVIDER=none switch the speech features off: the endpoints return 404, audio files fail to ingest with a clear message, /api/config reports tts_available/stt_available, and the UI hides the Speak and microphone buttons. - Drop the Google Fonts Roboto import from the web UI. - prefetch-models fills the cache the app reads; verify-offline checks the packaged encoding. - Docs: new Air-Gapped Deployment guide, settings and cache notes.
353 lines
14 KiB
Python
353 lines
14 KiB
Python
"""Chunk sizes must be counted in the embedding model's units, and splitting
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must never rewrite the text it splits."""
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import sys
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import types
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import pytest
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from docsgpt.parser import tokenization
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from docsgpt.parser.tokenization import (
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HuggingFaceCounter,
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TiktokenCounter,
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get_token_counter,
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)
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SAMPLES = [
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"Hello World: DocsGPT ANSWERS Questions.",
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"The quick brown fox jumps over the lazy dog. " * 40,
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"Comment configurer l'authentification avec une clé API ?",
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"def embed(text: str) -> list[float]:\n return model.encode(text)\n",
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"Ünïcödé — em-dashes, curly “quotes”, and 日本語 text.",
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"a,b,c\n1,2,3\n4,5,6\n" * 30,
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]
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class _StubEncoding:
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"""Whitespace tokenizer standing in for tiktoken."""
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def encode_ordinary(self, text):
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return [ord(c) for c in text]
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def decode(self, ids):
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return "".join(chr(i) for i in ids)
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def decode_with_offsets(self, ids):
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# One token per character, so each token starts where the last ended.
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return self.decode(ids), list(range(len(ids)))
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@pytest.fixture(autouse=True)
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def _clear_cache():
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tokenization.reset_cache()
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yield
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tokenization.reset_cache()
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class TestSplittingPreservesText:
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"""The property that protects every stored document."""
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@pytest.mark.parametrize("text", SAMPLES)
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def test_tiktoken_split_reassembles_exactly(self, text, monkeypatch):
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monkeypatch.setattr(tokenization, "get_encoding", _StubEncoding)
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counter = TiktokenCounter()
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pieces = counter.split(text, 7)
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assert "".join(pieces) == text
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@pytest.mark.parametrize("text", SAMPLES)
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def test_hf_split_reassembles_exactly(self, text, hf_counter):
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"""WordPiece lowercases on decode, so splitting must slice, not decode."""
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pieces = hf_counter.split(text, 7)
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assert "".join(pieces) == text
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def test_hf_split_does_not_lowercase(self, hf_counter):
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text = "Hello World: DocsGPT ANSWERS Questions."
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assert "".join(hf_counter.split(text, 3)) == text
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assert "DocsGPT" in "".join(hf_counter.split(text, 3))
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@pytest.mark.parametrize("text", SAMPLES)
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def test_every_piece_is_within_budget(self, text, hf_counter):
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budget = 10
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for piece in hf_counter.split(text, budget):
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# The final piece can absorb trailing characters the tokenizer
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# dropped, so allow a small overshoot there only.
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assert hf_counter.count(piece) <= budget + 2
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def test_short_text_is_returned_whole(self, hf_counter):
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assert hf_counter.split("short", 100) == ["short"]
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def test_empty_text_yields_no_pieces(self, hf_counter):
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assert hf_counter.split("", 10) == []
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def test_zero_budget_is_clamped_not_infinite_loop(self, hf_counter):
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pieces = hf_counter.split("some words here to split", 0)
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assert "".join(pieces) == "some words here to split"
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class TestCounting:
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def test_counts_differ_between_tokenizers(self, hf_counter, monkeypatch):
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"""The whole point: mpnet and cl100k disagree, so units matter."""
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monkeypatch.setattr(tokenization, "get_encoding", _StubEncoding)
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text = "internationalisation tokenization"
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assert hf_counter.count(text) != TiktokenCounter().count(text)
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def test_empty_text_counts_zero(self, hf_counter):
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assert hf_counter.count("") == 0
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class TestSelection:
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def test_registered_model_uses_its_own_tokenizer(self):
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counter = get_token_counter("huggingface_sentence-transformers/all-mpnet-base-v2")
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assert isinstance(counter, HuggingFaceCounter)
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assert counter.name == "sentence-transformers/all-mpnet-base-v2"
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def test_openai_model_falls_back_to_cl100k(self):
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"""OpenAI models are served remotely and genuinely count cl100k."""
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assert isinstance(get_token_counter("openai_text-embedding-ada-002"), TiktokenCounter)
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def test_unreachable_tokenizer_falls_back_rather_than_raising(self, monkeypatch):
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monkeypatch.setattr(tokenization, "_load_hf_counter", lambda repo: None)
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assert isinstance(get_token_counter("granite-311m"), TiktokenCounter)
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def test_counter_is_cached_per_model(self):
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first = get_token_counter("granite-311m")
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assert get_token_counter("granite-311m") is first
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class TestTiktokenSplitAgainstRealCl100k:
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"""The stub above is one token per character, so it can never place a cut
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inside a character. Real cl100k can, and that is the case that corrupted
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text: decoding each window on its own turns a straddled multi-byte
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character into U+FFFD on both sides of the cut."""
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@pytest.fixture
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def real_counter(self):
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try:
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counter = TiktokenCounter()
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counter.count("probe")
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except Exception as exc: # offline CI, same policy as the HF fixture
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pytest.skip(f"cl100k encoding unavailable: {exc}")
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return counter
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# 2000 is the shipped default max_tokens, 384 mpnet's window; the small
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# values place many more cuts per unit of text.
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@pytest.mark.parametrize("window", [1, 2, 3, 7, 128, 384, 2000])
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@pytest.mark.parametrize(
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"text",
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[
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"日本語のテキストです。絵文字も🎉あります。",
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"検索は自然言語でできます。" * 200,
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"Здравствуйте, как настроить аутентификацию?",
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"🎉🎊✨🚀🔥💡📚🧠" * 50,
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"Ünïcödé — em-dashes, curly “quotes”, and 日本語 text.",
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],
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ids=["ja-short", "ja-long", "ru", "emoji", "mixed"],
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)
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def test_split_reassembles_exactly(self, real_counter, text, window):
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pieces = real_counter.split(text, window)
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assert "".join(pieces) == text
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@pytest.mark.parametrize("window", [1, 3, 128, 2000])
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def test_split_never_emits_a_replacement_character(self, real_counter, window):
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text = "検索は自然言語でできます。絵文字も🎉あります。" * 100
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assert "�" not in "".join(real_counter.split(text, window))
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def test_first_window_budget_is_honoured_and_lossless(self, real_counter):
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text = "日本語のテキストです。" * 50
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pieces = real_counter.split(text, 20, first_max_tokens=5)
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assert "".join(pieces) == text
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assert real_counter.count(pieces[0]) <= 5
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class TestTiktokenCounterEdges:
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"""The cl100k path is the fallback, so its edges matter as much."""
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@pytest.fixture
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def counter(self, monkeypatch):
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monkeypatch.setattr(tokenization, "get_encoding", _StubEncoding)
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return TiktokenCounter()
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def test_empty_text_counts_zero_and_splits_to_nothing(self, counter):
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assert counter.count("") == 0
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assert counter.split("", 10) == []
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def test_text_within_budget_is_returned_whole(self, counter):
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assert counter.split("abc", 10) == ["abc"]
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def test_first_window_can_be_smaller_than_the_rest(self, counter):
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"""A header eats into the first chunk's budget only."""
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pieces = counter.split("abcdefghij", 4, first_max_tokens=2)
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assert pieces[0] == "ab"
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assert "".join(pieces) == "abcdefghij"
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class TestCounterContract:
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def test_base_class_requires_an_implementation(self):
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base = tokenization.TokenCounter()
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with pytest.raises(NotImplementedError):
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base.count("x")
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with pytest.raises(NotImplementedError):
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base.split("x", 1)
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class TestFallbackWhenTokenizerUnavailable:
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def test_load_failure_returns_none_rather_than_raising(self, monkeypatch, caplog):
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"""Chunking must survive an offline host or a bad repo name."""
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import builtins
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real_import = builtins.__import__
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def boom(name, *args, **kwargs):
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if name == "tokenizers":
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raise ImportError("no tokenizers here")
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return real_import(name, *args, **kwargs)
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monkeypatch.setattr(builtins, "__import__", boom)
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assert tokenization._load_hf_counter("some/repo") is None
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def test_selection_falls_back_to_cl100k_on_failure(self, monkeypatch):
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monkeypatch.setattr(tokenization, "_load_hf_counter", lambda repo: None)
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assert isinstance(get_token_counter("granite-97m"), TiktokenCounter)
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def test_reset_cache_forces_reselection(self, monkeypatch):
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first = get_token_counter("granite-311m")
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tokenization.reset_cache()
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monkeypatch.setattr(tokenization, "_load_hf_counter", lambda repo: None)
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assert get_token_counter("granite-311m") is not first
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class TestOffsetsWithoutSpans:
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"""Some tokenizers emit ``(0, 0)`` for specials or normalised-away chars.
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Those tokens consume budget but point at no text, so the splitter has to
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skip the window rather than emit an empty piece or lose the tail.
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"""
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class _Encoded:
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def __init__(self, offsets):
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self.offsets = offsets
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self.ids = list(range(len(offsets)))
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class _Tokenizer:
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def __init__(self, offsets):
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self._offsets = offsets
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def encode(self, text, add_special_tokens=False):
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return TestOffsetsWithoutSpans._Encoded(self._offsets)
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def _counter(self, offsets):
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return HuggingFaceCounter(self._Tokenizer(offsets), "stub")
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def test_span_less_windows_are_skipped_not_emitted_empty(self):
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# Two real tokens, then a window of pure (0, 0) padding-like entries.
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counter = self._counter([(0, 2), (2, 4), (0, 0), (0, 0)])
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pieces = counter.split("abcd", 2)
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assert "" not in pieces
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assert "".join(pieces) == "abcd"
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def test_trailing_text_is_never_dropped(self):
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"""Offsets that stop short of the string must not lose the remainder."""
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counter = self._counter([(0, 1), (1, 2), (2, 3)])
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pieces = counter.split("abcdef", 2)
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assert "".join(pieces) == "abcdef"
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def test_all_span_less_offsets_still_return_the_text(self):
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counter = self._counter([(0, 0), (0, 0), (0, 0)])
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assert "".join(counter.split("abc", 1)) == "abc"
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class TestUnknownTokenCollapse:
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"""A tokenizer that folds a long unbroken run into one ``[UNK]``.
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WordPiece gives up on any word longer than ``max_input_chars_per_word``
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and emits a single unknown token for it. Counting that as one token makes
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a base64 blob or a minified bundle look tiny, so the chunker never splits
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it and an oversized chunk reaches the embedding server.
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"""
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class _CollapsingEncoding:
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"""One token per whitespace-separated word, however long the word."""
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def __init__(self, text):
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self.ids = []
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self.offsets = []
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cursor = 0
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for word in text.split(" "):
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if word:
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self.ids.append(0)
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self.offsets.append((cursor, cursor + len(word)))
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cursor += len(word) + 1
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class _CollapsingTokenizer:
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def encode(self, text, add_special_tokens=False):
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return TestUnknownTokenCollapse._CollapsingEncoding(text)
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def _counter(self):
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return tokenization.HuggingFaceCounter(self._CollapsingTokenizer(), "stub")
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def test_long_unbroken_run_is_charged_by_its_span(self):
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counter = self._counter()
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assert counter.count("a" * 9000) > 100
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def test_ordinary_prose_is_unaffected(self):
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counter = self._counter()
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text = "the quick brown fox jumps over the lazy dog"
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assert counter.count(text) == 9
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def test_split_bounds_a_collapsed_run(self):
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counter = self._counter()
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text = "a" * 9000
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pieces = counter.split(text, 20)
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assert "".join(pieces) == text, "split must not lose or alter text"
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assert len(pieces) > 1, "a collapsed run must still be cut into pieces"
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assert all(counter.count(p) <= 20 for p in pieces)
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class TestTokenizerFile:
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"""The chunker's tokenizer must come from the hub cache without a network round trip."""
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def test_cache_hit_makes_no_online_call(self, monkeypatch):
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calls = []
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def fake_download(repo, filename, local_files_only=False, cache_dir=None):
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calls.append(local_files_only)
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return "/cache/tokenizer.json"
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fake_hub = types.ModuleType("huggingface_hub")
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fake_hub.hf_hub_download = fake_download
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monkeypatch.setitem(sys.modules, "huggingface_hub", fake_hub)
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assert tokenization._tokenizer_file("org/model") == "/cache/tokenizer.json"
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assert calls == [True]
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def test_cache_miss_falls_back_to_online(self, monkeypatch):
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calls = []
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def fake_download(repo, filename, local_files_only=False, cache_dir=None):
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calls.append(local_files_only)
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if local_files_only:
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raise FileNotFoundError("not cached")
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return "/downloaded/tokenizer.json"
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fake_hub = types.ModuleType("huggingface_hub")
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fake_hub.hf_hub_download = fake_download
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monkeypatch.setitem(sys.modules, "huggingface_hub", fake_hub)
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assert tokenization._tokenizer_file("org/model") == "/downloaded/tokenizer.json"
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assert calls == [True, False]
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def test_reads_the_embedding_model_cache(self, monkeypatch):
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"""FastEmbed's snapshot already holds tokenizer.json; a second cache downloads it again."""
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cache_dirs = []
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def fake_download(repo, filename, local_files_only=False, cache_dir=None):
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cache_dirs.append(cache_dir)
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if local_files_only:
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raise FileNotFoundError("not cached")
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return "/models/tokenizer.json"
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fake_hub = types.ModuleType("huggingface_hub")
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fake_hub.hf_hub_download = fake_download
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monkeypatch.setitem(sys.modules, "huggingface_hub", fake_hub)
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monkeypatch.setattr(tokenization.settings, "EMBEDDINGS_CACHE_DIR", "/models")
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tokenization._tokenizer_file("org/model")
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assert cache_dirs == ["/models", "/models"]
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