chore: bootstrap repository with governance docs
Initialize git, add Apache-2.0 LICENSE, .gitattributes (LF line endings), AGENTS.md (entry points, stack, discovery order, baseline checks), RUNBOOK.md (dev boot, prod deploy with overlay, ingestion, failures, rollback, scaling notes), .env.prod.example with rotated credential placeholders, and dev-only warnings on .env.example. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Vadim Malanov
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app/ingestion/chunker.py
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317
app/ingestion/chunker.py
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"""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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ExtractedFigure,
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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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