feat(ingestion): add DOCX/CSV/XLSX parsing and fixed-size chunking (ADR-0018)

Adds src/application/ingestion/ -- Persian normalization, DOCX body
walk with structural data/layout table classification, CSV/XLSX row
rendering, and fixed-size token chunking (cl100k_base, 400/60/512) --
as pure functions per ADR-0015, tested against real production
documents (asia_data_sample, kept out of the repo). ADR-0018 records
where this diverges from ADR-0004 (fixed-size default, no invented
headings/tree, structural table classification, header-provable
labeling only). Plan 001's scope line is corrected from CSV-only to
DOCX/XLSX/CSV, and CLAUDE.md's stale project-status paragraph is
updated to match current implementation state.

src/application/ingestion/normalization.py

@@ -0,0 +1,73 @@
+"""Persian text normalization (ADR-0018).
+
+Applied to every extracted text block before chunking, for all source formats.
+
+The problem this solves is silent: Persian authored on mixed Arabic/Persian
+keyboards contains both U+06A9 and U+0643 for "k", both U+06CC and U+064A for
+"y". Those are distinct codepoints and therefore distinct tokens to every
+embedding model, so the same word embeds two different ways depending on which
+key the author pressed.
+
+Letter folding only -- digits and punctuation are left as authored, because
+chunk content is what citations render back to the reader and Western digits
+inside Persian prose read as wrong.
+"""
+
+import re
+import unicodedata
+
+# Both tables are written as codepoints rather than character literals. Arabic
+# letterforms are visually indistinguishable from one another (and alef from a
+# Latin "l") in a monospace editor -- which is the very confusion this module
+# exists to resolve -- and literals would render right-to-left, visually
+# reordering the source line.
+#
+# `str.translate` accepts an ordinal->ordinal mapping directly, and an ordinal
+# mapped to None is deleted.
+
+_ARABIC_KAF = 0x0643
+_ARABIC_YEH = 0x064A
+_ALEF_MAKSURA = 0x0649
+_ALEF_HAMZA_ABOVE = 0x0623
+_ALEF_HAMZA_BELOW = 0x0625
+_NOT_SIGN = 0x00AC
+
+_PERSIAN_KEHEH = 0x06A9
+_PERSIAN_YEH = 0x06CC
+_ALEF = 0x0627
+_SPACE = 0x0020
+
+_LETTER_FOLDING: dict[int, int] = {
+    _ARABIC_KAF: _PERSIAN_KEHEH,
+    _ARABIC_YEH: _PERSIAN_YEH,
+    _ALEF_MAKSURA: _PERSIAN_YEH,
+    _ALEF_HAMZA_ABOVE: _ALEF,
+    _ALEF_HAMZA_BELOW: _ALEF,
+    # A soft-hyphen artifact from documents exported by older Word versions.
+    _NOT_SIGN: _SPACE,
+}
+
+_TATWEEL = 0x0640
+_SUPERSCRIPT_ALEF = 0x0670
+_HARAKAT = range(0x064B, 0x0660)
+
+# Applied after NFKC, which can itself decompose presentation forms into a
+# base letter plus a combining mark.
+_MARK_REMOVAL: dict[int, int | None] = dict.fromkeys(_HARAKAT)
+_MARK_REMOVAL[_TATWEEL] = None
+_MARK_REMOVAL[_SUPERSCRIPT_ALEF] = None
+
+_WHITESPACE = re.compile(r"\s+")
+
+
+def normalize_persian_text(text: str) -> str:
+    """Fold Arabic letterforms to Persian and collapse whitespace.
+
+    Call this per text block, **before** blocks are assembled into a document.
+    The whitespace collapse maps `\\n` to a space, so running it over assembled
+    markdown would flatten every heading and paragraph onto one line.
+    """
+    text = text.translate(_LETTER_FOLDING)
+    text = unicodedata.normalize("NFKC", text)
+    text = text.translate(_MARK_REMOVAL)
+    return _WHITESPACE.sub(" ", text).strip()