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/tokenizer.py

@@ -0,0 +1,33 @@
+"""Token counting for chunk sizing (ADR-0018).
+
+`cl100k_base` is a deliberate proxy for the embedding models' own tokenizers.
+`text-embedding-3-large` has an 8191-token window and never binds;
+`nomic-embed-text-v2-moe`'s 512-token sequence length is the only real
+constraint. cl100k tokenizes Persian inefficiently while nomic's multilingual
+tokenizer does not, so a cl100k count reliably over-estimates the nomic count --
+safe in the conservative direction, without shipping a second tokenizer and its
+model download into the ingestion path.
+"""
+
+from functools import lru_cache
+
+import tiktoken
+
+
+@lru_cache(maxsize=4)
+def get_encoder(encoding_name: str) -> tiktoken.Encoding:
+    """Return a cached tiktoken encoder.
+
+    Deliberately not a module-level constant: `tiktoken` fetches the BPE
+    vocabulary over the network the first time an encoding is used, and
+    ADR-0012 forbids external resource setup as an import-time side effect.
+    The lifespan warms this at startup so a process fails fast at boot rather
+    than inside the first ingestion request. Set `TIKTOKEN_CACHE_DIR` to a
+    pre-populated directory for offline deployments.
+    """
+    return tiktoken.get_encoding(encoding_name)
+
+
+def count_tokens(text: str, encoding_name: str) -> int:
+    """Return the number of tokens `text` encodes to."""
+    return len(get_encoder(encoding_name).encode(text))