import importlib.util import asyncio import sys import types from contextlib import contextmanager from pathlib import Path @contextmanager def _load_token_chunker_with_stubs(): root = Path(__file__).resolve().parents[3] original_modules = {} def _install(name: str, module: types.ModuleType): original_modules.setdefault(name, sys.modules.get(name)) sys.modules[name] = module try: rag_pkg = types.ModuleType("rag") rag_pkg.__path__ = [str(root / "rag")] _install("rag", rag_pkg) rag_flow_pkg = types.ModuleType("rag.flow") rag_flow_pkg.__package__ = "rag" rag_flow_pkg.__path__ = [str(root / "rag" / "flow")] _install("rag.flow", rag_flow_pkg) rag_flow_chunker_pkg = types.ModuleType("rag.flow.chunker") rag_flow_chunker_pkg.__package__ = "rag.flow" rag_flow_chunker_pkg.__path__ = [str(root / "rag" / "flow" / "chunker")] _install("rag.flow.chunker", rag_flow_chunker_pkg) rag_flow_parser_pkg = types.ModuleType("rag.flow.parser") rag_flow_parser_pkg.__package__ = "rag.flow" rag_flow_parser_pkg.__path__ = [str(root / "rag" / "flow" / "parser")] _install("rag.flow.parser", rag_flow_parser_pkg) common_pkg = types.ModuleType("common") common_pkg.__path__ = [str(root / "common")] _install("common", common_pkg) common_float_utils = types.ModuleType("common.float_utils") common_float_utils.normalize_overlapped_percent = lambda value: value _install("common.float_utils", common_float_utils) common_token_utils = types.ModuleType("common.token_utils") common_token_utils.num_tokens_from_string = lambda text: 1 _install("common.token_utils", common_token_utils) rag_nlp = types.ModuleType("rag.nlp") rag_nlp.naive_merge = lambda *_args, **_kwargs: [] _install("rag.nlp", rag_nlp) class ProcessParamBase: def __init__(self): pass class ProcessBase: def __init__(self, _pipeline, _id, param): self._pipeline = _pipeline self._id = _id self._param = param self._outputs = {} self.callback = lambda *_args, **_kwargs: None def set_output(self, key, value): self._outputs[key] = value rag_flow_base = types.ModuleType("rag.flow.base") rag_flow_base.ProcessBase = ProcessBase rag_flow_base.ProcessParamBase = ProcessParamBase _install("rag.flow.base", rag_flow_base) rag_flow_parser_pdf_metadata = types.ModuleType("rag.flow.parser.pdf_chunk_metadata") rag_flow_parser_pdf_metadata.PDF_POSITIONS_KEY = "pdf_positions" rag_flow_parser_pdf_metadata.extract_pdf_positions = lambda _item: [] rag_flow_parser_pdf_metadata.finalize_pdf_chunk = lambda chunk: chunk async def restore_pdf_text_previews(*_args, **_kwargs): return None rag_flow_parser_pdf_metadata.restore_pdf_text_previews = restore_pdf_text_previews _install("rag.flow.parser.pdf_chunk_metadata", rag_flow_parser_pdf_metadata) try: import pydantic # noqa: F401 schema_spec = importlib.util.spec_from_file_location( "rag.flow.chunker.schema", root / "rag" / "flow" / "chunker" / "schema.py", ) if schema_spec is None or schema_spec.loader is None: raise RuntimeError("Failed to locate rag.flow.chunker.schema stub loader.") schema_module = importlib.util.module_from_spec(schema_spec) _install("rag.flow.chunker.schema", schema_module) schema_spec.loader.exec_module(schema_module) except Exception: schema_module = types.ModuleType("rag.flow.chunker.schema") class TokenChunkerFromUpstream: def __init__( self, name, file=None, chunks=None, output_format=None, json_result=None, markdown_result=None, text_result=None, html_result=None, _created_time=None, _elapsed_time=None, ): self.name = name self.file = file self.chunks = chunks self.output_format = output_format self.json_result = json_result self.json = json_result self.markdown_result = markdown_result self.markdown = markdown_result self.text_result = text_result self.text = text_result self.html_result = html_result self.html = html_result self._created_time = _created_time self._elapsed_time = _elapsed_time @classmethod def model_validate(cls, data): if isinstance(data, dict): return cls( name=data.get("name", ""), file=data.get("file"), chunks=data.get("chunks"), output_format=data.get("output_format"), json_result=data.get("json_result", data.get("json")), markdown_result=data.get("markdown_result", data.get("markdown")), text_result=data.get("text_result", data.get("text")), html_result=data.get("html_result", data.get("html")), _created_time=data.get("_created_time"), _elapsed_time=data.get("_elapsed_time"), ) raise TypeError("TokenChunkerFromUpstream expects a dict payload.") schema_module.TokenChunkerFromUpstream = TokenChunkerFromUpstream _install("rag.flow.chunker.schema", schema_module) token_chunker_spec = importlib.util.spec_from_file_location( "rag.flow.chunker.token_chunker", root / "rag" / "flow" / "chunker" / "token_chunker.py", ) if token_chunker_spec is None or token_chunker_spec.loader is None: raise RuntimeError("Failed to locate rag.flow.chunker.token_chunker stub loader.") token_chunker_module = importlib.util.module_from_spec(token_chunker_spec) _install("rag.flow.chunker.token_chunker", token_chunker_module) token_chunker_spec.loader.exec_module(token_chunker_module) yield token_chunker_module finally: for module_name, original in original_modules.items(): if original is None: sys.modules.pop(module_name, None) else: sys.modules[module_name] = original def test_token_chunker_prefers_upstream_chunks_for_json_output_format_chunks(): # Regression for #16812: when the upstream (e.g. TitleChunker) emits # output_format="chunks", TokenChunker must consume from_upstream.chunks and # not fall through to the raw parser json_result. Heavy deps are stubbed so # the real TokenChunker._invoke runs against the real schema when pydantic is # available (see title_chunker/common.py for the same chunks-vs-json branch). with _load_token_chunker_with_stubs() as token_chunker_module: token_chunker = token_chunker_module.TokenChunker param = token_chunker_module.TokenChunkerParam() param.delimiter_mode = "one" chunker = token_chunker(None, "token_chunker", param) kwargs = { "name": "token_chunker", "output_format": "chunks", "chunks": [{"text": "CHAPTER-AWARE"}], "json": [{"text": "RAW-PARSER-JSON"}], } asyncio.run(chunker._invoke(**kwargs)) assert chunker._outputs["chunks"] == [{"text": "CHAPTER-AWARE"}] def _build_json_chunker(param: dict, monkeypatch_positions=True): """Build a TokenChunker (bypassing ComponentBase.__init__) wired for the JSON ``delimiter_mode`` path, with heavy deps stubbed. Returns ``(chunker, module)`` so callers can monkeypatch the module-global ``extract_pdf_positions`` (it is imported as a name, so rebinding the module attribute reaches the call sites inside ``_build_json_chunks``). """ with _load_token_chunker_with_stubs() as token_chunker_module: token_chunker = token_chunker_module.TokenChunker param_obj = token_chunker_module.TokenChunkerParam() for key, value in param.items(): setattr(param_obj, key, value) chunker = token_chunker(None, "token_chunker", param_obj) chunker._canvas = types.SimpleNamespace(_doc_id=None, _tenant_id="t") if monkeypatch_positions: # Echo per-item positions so we can assert PDF coordinates survive. token_chunker_module.extract_pdf_positions = lambda item: item.get("positions", []) yield token_chunker_module, chunker def test_json_delimiter_mode_drop_delimiter_text(): # The delimiter is a boundary: its text must never appear inside a chunk. for module, chunker in _build_json_chunker({"delimiter_mode": "delimiter", "delimiters": ["`##`"]}): kwargs = { "name": "token_chunker", "output_format": "json", "json_result": [{"text": "first part##second part##third part", "doc_type_kwd": "text"}], } asyncio.run(chunker._invoke(**kwargs)) chunks = chunker._outputs["chunks"] texts = [c["text"] for c in chunks] assert texts == ["first part", "second part", "third part"] assert all("##" not in t for t in texts) def test_json_delimiter_mode_newline_join_not_glued(): # Regression for #17723: JSON flush must join buffered text items with "\\n", # never glue them. Two adjacent items "hello" + "world" must stay # "hello\\nworld", never become "helloworld". for module, chunker in _build_json_chunker({"delimiter_mode": "delimiter", "delimiters": []}): kwargs = { "name": "token_chunker", "output_format": "json", "json_result": [ {"text": "hello", "doc_type_kwd": "text"}, {"text": "world", "doc_type_kwd": "text"}, ], } asyncio.run(chunker._invoke(**kwargs)) chunks = chunker._outputs["chunks"] assert len(chunks) == 1 assert chunks[0]["text"] == "hello\nworld" def test_json_delimiter_mode_children_delimiters_applied(): # Regression for #17723: children_delimiters (secondary split) must run before # finalizing the JSON ``delimiter_mode`` path, or they are silently ignored. for module, chunker in _build_json_chunker({"delimiter_mode": "delimiter", "delimiters": [], "children_delimiters": ["|"]}): kwargs = { "name": "token_chunker", "output_format": "json", "json_result": [{"text": "alpha|beta", "doc_type_kwd": "text"}], } asyncio.run(chunker._invoke(**kwargs)) chunks = chunker._outputs["chunks"] texts = [c["text"] for c in chunks] assert texts == ["alpha", "beta"] def test_json_delimiter_mode_pdf_positions_retained(): # PDF coordinates carried on the combined chunk must survive into the output. for module, chunker in _build_json_chunker({"delimiter_mode": "delimiter", "delimiters": []}): kwargs = { "name": "token_chunker", "output_format": "json", "json_result": [ {"text": "hello", "doc_type_kwd": "text", "positions": [[1, 0, 10, 0, 5]]}, {"text": "world", "doc_type_kwd": "text", "positions": [[2, 0, 20, 0, 8]]}, ], } asyncio.run(chunker._invoke(**kwargs)) chunks = chunker._outputs["chunks"] assert len(chunks) == 1 assert chunks[0].get("pdf_positions") == [[1, 0, 10, 0, 5], [2, 0, 20, 0, 8]] def test_json_delimiter_mode_pdf_positions_per_segment_not_broadcast(): # Regression for #3 (PDF coordinate leak): when consecutive text items from # different pages are buffered and then split by a custom delimiter, each # output segment must carry only the PDF positions of the item(s) that # contributed to it -- not the union of every buffered item. The old code # broadcast ``combined_pos`` to every split chunk, so a page-1 segment also # claimed page-2 coordinates and all segments shared one PDF preview image. for module, chunker in _build_json_chunker({"delimiter_mode": "delimiter", "delimiters": ["`。`"]}): # Mirror production's preview-cache behaviour: a chunk's preview image is # keyed by its position set, so chunks sharing positions share one image. async def _restore_previews(chunks, from_upstream, canvas): preview_cache = {} for chunk in chunks: positions = chunk.get("pdf_positions") or [] key = tuple(tuple(p[:5]) for p in positions) if key in preview_cache: chunk["img_id"] = preview_cache[key] else: new_id = "img-%d" % len(preview_cache) chunk["img_id"] = new_id preview_cache[key] = new_id module.restore_pdf_text_previews = _restore_previews kwargs = { "name": "doc.pdf", "output_format": "json", "json_result": [ {"text": "第一章。第二段", "doc_type_kwd": "text", "positions": [[1, 0, 10, 0, 5]]}, {"text": "第三章。第四章", "doc_type_kwd": "text", "positions": [[2, 0, 20, 0, 8]]}, ], } asyncio.run(chunker._invoke(**kwargs)) chunks = chunker._outputs["chunks"] texts = [c["text"] for c in chunks] # Custom "。" splits the buffered text into three segments; the "\n" join # between the two items is NOT a split point, so the middle segment spans # both pages. assert texts == ["第一章", "第二段\n第三章", "第四章"], texts positions = [c.get("pdf_positions") for c in chunks] # Page-1-only segment must NOT carry page-2 coordinates. assert positions[0] == [[1, 0, 10, 0, 5]], positions # Spanning segment legitimately carries both pages. assert positions[1] == [[1, 0, 10, 0, 5], [2, 0, 20, 0, 8]], positions # Page-2-only segment must NOT carry page-1 coordinates. assert positions[2] == [[2, 0, 20, 0, 8]], positions # Previews must not be shared: distinct position sets -> distinct images. img_ids = [c.get("img_id") for c in chunks] assert len(set(img_ids)) == len(img_ids), img_ids def test_json_delimiter_mode_consecutive_delimiter_keeps_boundary(): # Regression for #17723: "A####B" with pattern "##" must yield ["A", "B"], # both boundary-adjacent segments preserved (the bug collapsed it to "A##B"). for module, chunker in _build_json_chunker({"delimiter_mode": "delimiter", "delimiters": ["`##`"]}): kwargs = { "name": "token_chunker", "output_format": "json", "json_result": [{"text": "A####B", "doc_type_kwd": "text"}], } asyncio.run(chunker._invoke(**kwargs)) chunks = chunker._outputs["chunks"] texts = [c["text"] for c in chunks] assert texts == ["A", "B"] assert all("##" not in t for t in texts) def test_text_delimiter_mode_token_size_zero_or_one_no_atom_split(): # token_size=0/1 must not atom-split delimiter segments into 1-token chunks; # the delimiter path produces delimiter-boundary chunks regardless of cap. for module, chunker in _build_json_chunker({"delimiter_mode": "token_size", "delimiters": ["`|`"]}): # text path: delimiter_mode is token_size but a custom delimiter is # present, so the delimiter branch (_split_text_by_pattern) is used. kwargs = { "name": "token_chunker", "output_format": "text", "text": "aaa|bbb|ccc", } for chunk_token_size in (0, 1): setattr(chunker._param, "chunk_token_size", chunk_token_size) asyncio.run(chunker._invoke(**kwargs)) chunks = chunker._outputs["chunks"] texts = [c["text"] for c in chunks] assert texts == ["aaa", "bbb", "ccc"], f"token_size={chunk_token_size} atom-split a delimiter segment: {texts}"