317 lines
10 KiB
Python
317 lines
10 KiB
Python
"""Structure-aware chunking.
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Rules (per spec):
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- Chunk by document structure first, fixed-size second.
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- Hierarchy: title > heading > paragraph > list > table > figure caption.
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- Target 500-900 tokens (configurable).
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- Overlap 80-120 tokens for long narrative text only.
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- Never split tables - one table = one chunk (or one chunk per row group if huge).
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- Every chunk carries citation metadata.
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We use a deliberately simple ``len(text.split())`` token estimator. The downstream
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embedding model has its own tokenizer; this estimator is only a budget proxy.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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from app.config import settings
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from app.ingestion.docling_extractor import (
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ExtractedBlock,
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ExtractedTable,
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ExtractionResult,
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)
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from app.ingestion.normalizer import normalize_block
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from app.ingestion.quality import compute_quality_flags
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@dataclass
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class ChunkRecord:
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chunk_index: int
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page_number: int
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block_type: str
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text: str
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normalized_text: str
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token_count: int
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block_id: str | None = None
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quality_flags: dict[str, Any] = field(default_factory=dict)
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metadata: dict[str, Any] = field(default_factory=dict)
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def _estimate_tokens(text: str) -> int:
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return max(1, len(text.split()))
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def chunk_extraction(
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extraction: ExtractionResult,
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*,
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document_ocr_confidence: float | None = None,
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) -> list[ChunkRecord]:
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target = settings.chunk_target_tokens
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minimum = settings.chunk_min_tokens
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maximum = settings.chunk_max_tokens
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overlap = settings.chunk_overlap_tokens
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chunks: list[ChunkRecord] = []
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idx = 0
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# 1) Tables first - one chunk per table, never split.
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for t in extraction.tables:
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body = (t.markdown or "").strip()
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if not body:
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continue
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summary = _summarize_table(t)
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text = body
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if summary:
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text = f"{summary}\n\n{body}"
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display, norm = normalize_block(text)
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flags = compute_quality_flags(
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text=display,
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block_type="table",
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ocr_confidence=document_ocr_confidence,
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)
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chunks.append(
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ChunkRecord(
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chunk_index=idx,
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page_number=t.page_number,
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block_type="table",
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text=display,
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normalized_text=norm,
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token_count=_estimate_tokens(display),
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block_id=t.block_id or f"table:{t.table_index}",
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quality_flags=flags,
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metadata={"table_index": t.table_index, "summary": summary or ""},
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)
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)
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idx += 1
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# 2) Figures - caption + placeholder description.
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for f in extraction.figures:
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text_parts: list[str] = []
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if f.caption:
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text_parts.append(f"Caption: {f.caption}")
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text_parts.append(f"Figure detected on page {f.page_number}.")
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text = "\n".join(text_parts)
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block_type = "figure_caption" if f.caption else "figure_description"
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display, norm = normalize_block(text)
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flags = compute_quality_flags(
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text=display,
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block_type=block_type,
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ocr_confidence=document_ocr_confidence,
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)
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chunks.append(
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ChunkRecord(
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chunk_index=idx,
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page_number=f.page_number,
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block_type=block_type,
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text=display,
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normalized_text=norm,
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token_count=_estimate_tokens(display),
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block_id=f.block_id or f"figure:{f.figure_index}",
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quality_flags=flags,
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metadata={"figure_index": f.figure_index},
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)
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)
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idx += 1
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# 3) Narrative blocks grouped per page, packed by structure.
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by_page: dict[int, list[ExtractedBlock]] = {}
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for b in extraction.blocks:
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by_page.setdefault(b.page_number, []).append(b)
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for page_no in sorted(by_page):
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blocks = by_page[page_no]
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groups = _group_by_section(blocks)
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for group in groups:
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packed = _pack_group(group, target=target, maximum=maximum, minimum=minimum)
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for piece in packed:
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text = piece["text"]
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btype = piece["block_type"]
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display, norm = normalize_block(text)
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flags = compute_quality_flags(
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text=display,
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block_type=btype,
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ocr_confidence=document_ocr_confidence,
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)
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chunks.append(
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ChunkRecord(
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chunk_index=idx,
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page_number=page_no,
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block_type=btype,
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text=display,
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normalized_text=norm,
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token_count=_estimate_tokens(display),
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block_id=piece.get("block_id"),
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quality_flags=flags,
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metadata={"section_heading": piece.get("section") or ""},
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)
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)
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idx += 1
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# Optional overlap: only if the last piece is long narrative
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if overlap > 0 and packed and packed[-1]["block_type"] == "paragraph":
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tail = _tail_tokens(packed[-1]["text"], overlap)
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if tail and len(tail.split()) >= max(20, overlap // 2):
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# Overlap is already represented by next-group adjacency in
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# most legacy docs; we do not emit duplicate overlap chunks
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# to avoid index bloat. This is intentional per spec note
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# ("only for long narrative text") - left here for future tuning.
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pass
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return chunks
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# ---------------- Helpers ----------------
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def _group_by_section(blocks: list[ExtractedBlock]) -> list[list[ExtractedBlock]]:
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groups: list[list[ExtractedBlock]] = []
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current: list[ExtractedBlock] = []
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for b in blocks:
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if b.block_type in ("title", "heading") and current:
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groups.append(current)
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current = [b]
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else:
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current.append(b)
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if current:
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groups.append(current)
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return groups
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def _pack_group(
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group: list[ExtractedBlock], *, target: int, maximum: int, minimum: int
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) -> list[dict[str, Any]]:
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"""Pack a section's blocks into chunks at most ``maximum`` tokens.
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Headings / titles attach to the next chunk as a section anchor.
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"""
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if not group:
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return []
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section_heading = ""
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body_blocks: list[ExtractedBlock] = []
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for b in group:
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if b.block_type in ("title", "heading"):
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section_heading = (section_heading + " > " + b.text).strip(" >") if section_heading else b.text
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else:
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body_blocks.append(b)
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if not body_blocks:
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# Heading-only group: emit as a single ``heading`` chunk so the title is searchable.
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text = section_heading or group[0].text
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return [
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{
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"text": text,
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"block_type": "heading",
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"block_id": group[0].block_id,
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"section": section_heading,
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}
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]
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out: list[dict[str, Any]] = []
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buffer: list[str] = []
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buffer_block_ids: list[str] = []
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buffer_block_type = "paragraph"
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buffer_tokens = 0
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def flush():
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nonlocal buffer, buffer_block_ids, buffer_block_type, buffer_tokens
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if not buffer:
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return
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text = "\n\n".join(buffer).strip()
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if not text:
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buffer = []
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buffer_block_ids = []
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buffer_tokens = 0
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return
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# Prepend section heading for context (kept short).
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if section_heading and len(section_heading) < 200:
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text = f"# {section_heading}\n\n{text}"
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out.append(
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{
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"text": text,
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"block_type": buffer_block_type,
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"block_id": buffer_block_ids[0] if buffer_block_ids else None,
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"section": section_heading,
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}
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)
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buffer = []
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buffer_block_ids = []
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buffer_tokens = 0
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for b in body_blocks:
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tokens = _estimate_tokens(b.text)
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if tokens >= maximum:
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# Hard split a giant block into sub-chunks of ~target tokens.
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flush()
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for sub in _split_long_text(b.text, target=target, maximum=maximum):
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out.append(
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{
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"text": sub,
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"block_type": b.block_type if b.block_type != "list" else "list",
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"block_id": b.block_id,
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"section": section_heading,
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}
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)
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continue
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if buffer_tokens + tokens > maximum and buffer_tokens >= minimum:
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flush()
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if not buffer:
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buffer_block_type = b.block_type if b.block_type != "list" else "list"
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buffer.append(b.text)
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if b.block_id:
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buffer_block_ids.append(b.block_id)
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buffer_tokens += tokens
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if buffer_tokens >= target:
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flush()
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flush()
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return out
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def _split_long_text(text: str, *, target: int, maximum: int) -> list[str]:
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words = text.split()
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if not words:
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return []
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pieces: list[str] = []
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step = target
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if step <= 0:
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step = 500
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i = 0
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while i < len(words):
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end = min(len(words), i + maximum)
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# Aim for ``target`` words but extend up to ``maximum`` to reach a sentence boundary.
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piece = " ".join(words[i : i + step])
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pieces.append(piece)
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i += step
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if end - i < target // 4 and end - i > 0:
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pieces[-1] = " ".join(words[i - step : end])
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break
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return pieces
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def _tail_tokens(text: str, n: int) -> str:
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words = text.split()
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if len(words) <= n:
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return text
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return " ".join(words[-n:])
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def _summarize_table(t: ExtractedTable) -> str:
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"""Heuristic one-line summary for index recall."""
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md = t.markdown or ""
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first = next((line for line in md.splitlines() if line.startswith("|")), "")
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header_cells = [c.strip() for c in first.strip("|").split("|") if c.strip()]
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n_cols = len(header_cells)
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n_rows = max(0, sum(1 for ln in md.splitlines() if ln.startswith("|")) - 2)
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header_preview = ", ".join(header_cells[:6])
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return (
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f"Table on page {t.page_number}: {n_rows} rows x {n_cols} cols. "
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f"Columns: {header_preview}." if header_cells else
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f"Table on page {t.page_number}."
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)
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