Ajout pipeline Word (.docx) pour ingestion RAG
Nouveaux modules (3 fichiers, ~850 lignes): - word_processor.py: Extraction contenu Word (texte, headings, images, métadonnées) - word_toc_extractor.py: Construction TOC hiérarchique depuis styles Heading - word_pipeline.py: Orchestrateur complet réutilisant modules LLM existants Fonctionnalités: - Extraction native Word (pas d'OCR, économie ~0.003€/page) - Support Heading 1-9 pour TOC hiérarchique - Section paths compatibles Weaviate (1, 1.1, 1.2, etc.) - Métadonnées depuis propriétés Word + extraction paragraphes - Markdown compatible avec pipeline existant - Extraction images inline - Réutilise 100% des modules LLM (metadata, classifier, chunker, cleaner, validator) Pipeline testé: - Fichier exemple: "On the origin - 10 pages.docx" - 48 paragraphes, 2 headings extraits - 37 chunks créés - Output: markdown + JSON chunks Architecture: 1. Extraction Word → 2. Markdown → 3. TOC → 4-9. Modules LLM réutilisés → 10. Weaviate Prochaine étape: Intégration Flask (route upload Word) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
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generations/library_rag/utils/word_pipeline.py
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519
generations/library_rag/utils/word_pipeline.py
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"""Word document processing pipeline for RAG ingestion.
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This module provides a complete pipeline for processing Microsoft Word documents
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(.docx) through the RAG system. It extracts content, builds structured markdown,
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applies LLM processing, and ingests chunks into Weaviate.
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The pipeline reuses existing LLM modules (metadata extraction, classification,
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chunking, cleaning, validation) from the PDF pipeline, only replacing the initial
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extraction step with Word-specific processing.
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Example:
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Process a Word document with default settings:
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from pathlib import Path
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from utils.word_pipeline import process_word
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result = process_word(
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Path("document.docx"),
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use_llm=True,
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llm_provider="ollama",
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ingest_to_weaviate=True,
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)
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print(f"Success: {result['success']}")
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print(f"Chunks created: {result['chunks_count']}")
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Process without Weaviate ingestion:
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result = process_word(
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Path("document.docx"),
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use_llm=True,
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ingest_to_weaviate=False,
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)
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Pipeline Steps:
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1. Word Extraction (word_processor.py)
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2. Markdown Construction
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3. TOC Extraction (word_toc_extractor.py)
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4. Metadata Extraction (llm_metadata.py) - REUSED
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5. Section Classification (llm_classifier.py) - REUSED
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6. Semantic Chunking (llm_chunker.py) - REUSED
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7. Chunk Cleaning (llm_cleaner.py) - REUSED
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8. Chunk Validation (llm_validator.py) - REUSED
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9. Weaviate Ingestion (weaviate_ingest.py) - REUSED
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See Also:
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- utils.word_processor: Word content extraction
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- utils.word_toc_extractor: TOC construction from headings
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- utils.pdf_pipeline: Similar pipeline for PDF documents
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"""
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Callable
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import json
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from utils.types import (
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Metadata,
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TOCEntry,
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ChunkData,
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PipelineResult,
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LLMProvider,
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ProgressCallback,
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)
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from utils.word_processor import (
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extract_word_content,
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extract_word_metadata,
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build_markdown_from_word,
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extract_word_images,
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)
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from utils.word_toc_extractor import build_toc_from_headings, flatten_toc
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# Note: LLM modules imported dynamically when use_llm=True to avoid import errors
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def _default_progress_callback(step: str, status: str, detail: str = "") -> None:
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"""Default progress callback that prints to console.
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Args:
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step: Current pipeline step name.
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status: Step status (running, completed, error).
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detail: Optional detail message.
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"""
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status_symbol = {
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"running": ">>>",
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"completed": "[OK]",
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"error": "[ERROR]",
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}.get(status, "[INFO]")
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print(f"{status_symbol} {step}: {detail}" if detail else f"{status_symbol} {step}")
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def process_word(
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word_path: Path,
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*,
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use_llm: bool = True,
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llm_provider: LLMProvider = "ollama",
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use_semantic_chunking: bool = True,
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ingest_to_weaviate: bool = True,
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skip_metadata_lines: int = 5,
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extract_images: bool = True,
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progress_callback: Optional[ProgressCallback] = None,
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) -> PipelineResult:
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"""Process a Word document through the complete RAG pipeline.
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Extracts content from a .docx file, processes it with LLM modules,
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and optionally ingests the chunks into Weaviate. Reuses all LLM
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processing steps from the PDF pipeline (metadata, classification,
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chunking, cleaning, validation).
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Args:
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word_path: Path to the .docx file to process.
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use_llm: Enable LLM processing steps (metadata, chunking, validation).
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If False, uses simple text splitting. Default: True.
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llm_provider: LLM provider to use ("ollama" for local, "mistral" for API).
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Default: "ollama".
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use_semantic_chunking: Use LLM-based semantic chunking instead of simple
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text splitting. Requires use_llm=True. Default: True.
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ingest_to_weaviate: Ingest processed chunks into Weaviate database.
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Default: True.
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skip_metadata_lines: Number of initial paragraphs to skip when building
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markdown (metadata header lines like TITRE, AUTEUR). Default: 5.
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extract_images: Extract and save inline images from the document.
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Default: True.
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progress_callback: Optional callback for progress updates.
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Signature: (step: str, status: str, detail: str) -> None.
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Returns:
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PipelineResult dictionary with keys:
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- success (bool): Whether processing succeeded
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- document_name (str): Name of processed document
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- output_dir (Path): Directory containing outputs
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- chunks_count (int): Number of chunks created
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- cost_ocr (float): OCR cost (always 0 for Word)
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- cost_llm (float): LLM processing cost
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- cost_total (float): Total cost
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- error (str): Error message if success=False
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Raises:
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FileNotFoundError: If word_path does not exist.
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ValueError: If file is not a .docx document.
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Example:
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>>> result = process_word(
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... Path("darwin.docx"),
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... use_llm=True,
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... llm_provider="ollama",
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... ingest_to_weaviate=True,
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... )
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>>> print(f"Created {result['chunks_count']} chunks")
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>>> print(f"Total cost: ${result['cost_total']:.4f}")
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Note:
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No OCR cost for Word documents (cost_ocr always 0).
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LLM costs depend on provider and document length.
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"""
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# Use default progress callback if none provided
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callback = progress_callback or _default_progress_callback
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try:
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# Validate input
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if not word_path.exists():
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raise FileNotFoundError(f"Word document not found: {word_path}")
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if not word_path.suffix.lower() == ".docx":
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raise ValueError(f"File must be .docx format: {word_path}")
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doc_name = word_path.stem
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output_dir = Path("output") / doc_name
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output_dir.mkdir(parents=True, exist_ok=True)
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# ================================================================
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# STEP 1: Extract Word Content
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# ================================================================
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callback("Word Extraction", "running", "Extracting document content...")
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content = extract_word_content(word_path)
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callback(
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"Word Extraction",
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"completed",
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f"Extracted {content['total_paragraphs']} paragraphs, "
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f"{len(content['headings'])} headings",
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)
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# ================================================================
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# STEP 2: Build Markdown
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# ================================================================
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callback("Markdown Construction", "running", "Building markdown...")
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markdown_text = build_markdown_from_word(
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content["paragraphs"],
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skip_metadata_lines=skip_metadata_lines,
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)
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# Save markdown
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markdown_path = output_dir / f"{doc_name}.md"
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with open(markdown_path, "w", encoding="utf-8") as f:
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f.write(markdown_text)
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callback(
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"Markdown Construction",
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"completed",
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f"Saved to {markdown_path.name} ({len(markdown_text)} chars)",
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)
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# ================================================================
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# STEP 3: Build TOC
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# ================================================================
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callback("TOC Extraction", "running", "Building table of contents...")
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toc_hierarchical = build_toc_from_headings(content["headings"])
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toc_flat = flatten_toc(toc_hierarchical)
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callback(
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"TOC Extraction",
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"completed",
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f"Built TOC with {len(toc_flat)} entries",
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)
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# ================================================================
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# STEP 4: Extract Images (if requested)
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# ================================================================
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image_paths: List[Path] = []
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if extract_images and content["has_images"]:
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callback("Image Extraction", "running", "Extracting images...")
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from docx import Document
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doc = Document(word_path)
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image_paths = extract_word_images(
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doc,
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output_dir / "images",
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doc_name,
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)
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callback(
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"Image Extraction",
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"completed",
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f"Extracted {len(image_paths)} images",
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)
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# ================================================================
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# STEP 5: LLM Metadata Extraction (REUSED)
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# ================================================================
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metadata: Metadata
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cost_llm = 0.0
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if use_llm:
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from utils.llm_metadata import extract_metadata
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callback("Metadata Extraction", "running", "Extracting metadata with LLM...")
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metadata = extract_metadata(
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markdown_text,
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provider=llm_provider,
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)
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# Note: extract_metadata doesn't return cost directly
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callback(
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"Metadata Extraction",
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"completed",
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f"Title: {metadata['title'][:50]}..., Author: {metadata['author']}",
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)
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else:
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# Use metadata from Word properties
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raw_meta = content["metadata_raw"]
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metadata = Metadata(
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title=raw_meta.get("title", doc_name),
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author=raw_meta.get("author", "Unknown"),
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year=raw_meta.get("created").year if raw_meta.get("created") else None,
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language=raw_meta.get("language", "unknown"),
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)
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callback(
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"Metadata Extraction",
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"completed",
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"Using Word document properties",
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)
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# ================================================================
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# STEP 6: Section Classification (REUSED)
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# ================================================================
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if use_llm:
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from utils.llm_classifier import classify_sections
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callback("Section Classification", "running", "Classifying sections...")
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# Note: classify_sections expects a list of section dicts, not raw TOC
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sections_to_classify = [
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{
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"section_path": entry["sectionPath"],
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"title": entry["title"],
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"content": "", # Content matched later
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}
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for entry in toc_flat
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]
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classified_sections = classify_sections(
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sections_to_classify,
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document_title=metadata.get("title", ""),
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provider=llm_provider,
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)
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main_sections = [
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s for s in classified_sections
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if s["section_type"] == "main_content"
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]
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callback(
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"Section Classification",
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"completed",
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f"{len(main_sections)}/{len(classified_sections)} main content sections",
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)
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else:
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# All sections are main content by default
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classified_sections = [
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{
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"section_path": entry["sectionPath"],
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"section_type": "main_content",
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"reason": "No LLM classification",
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}
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for entry in toc_flat
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]
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callback(
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"Section Classification",
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"completed",
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"Skipped (use_llm=False)",
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)
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# ================================================================
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# STEP 7: Semantic Chunking (REUSED)
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# ================================================================
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if use_llm and use_semantic_chunking:
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from utils.llm_chunker import chunk_section_with_llm
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callback("Semantic Chunking", "running", "Chunking with LLM...")
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# Chunk each section
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all_chunks: List[ChunkData] = []
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for entry in toc_flat:
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# TODO: Extract section content from markdown based on sectionPath
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# For now, using simple approach
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section_chunks = chunk_section_with_llm(
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markdown_text,
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entry["title"],
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metadata.get("title", ""),
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metadata.get("author", ""),
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provider=llm_provider,
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)
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all_chunks.extend(section_chunks)
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chunks = all_chunks
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callback(
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"Semantic Chunking",
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"completed",
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f"Created {len(chunks)} semantic chunks",
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)
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else:
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# Simple text splitting (fallback)
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callback("Text Splitting", "running", "Simple text splitting...")
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# Simple chunking by paragraphs (basic fallback)
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chunks_simple = []
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for i, para in enumerate(content["paragraphs"][skip_metadata_lines:]):
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if para["text"] and not para["is_heading"]:
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chunk_dict: ChunkData = {
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"text": para["text"],
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"keywords": [],
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"sectionPath": "1", # Default section
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"chapterTitle": "Main Content",
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"unitType": "paragraph",
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"orderIndex": i,
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"work": {
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"title": metadata["title"],
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"author": metadata["author"],
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},
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"document": {
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"sourceId": doc_name,
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"edition": content["metadata_raw"].get("edition", ""),
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},
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}
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chunks_simple.append(chunk_dict)
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chunks = chunks_simple
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callback(
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"Text Splitting",
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"completed",
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f"Created {len(chunks)} simple chunks",
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)
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# ================================================================
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# STEP 8: Chunk Cleaning (REUSED)
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# ================================================================
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if use_llm:
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from utils.llm_cleaner import clean_chunk
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callback("Chunk Cleaning", "running", "Cleaning chunks...")
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# Clean each chunk
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cleaned_chunks = []
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for chunk in chunks:
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cleaned = clean_chunk(chunk)
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if cleaned: # Only keep valid chunks
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cleaned_chunks.append(cleaned)
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chunks = cleaned_chunks
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callback(
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"Chunk Cleaning",
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"completed",
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f"{len(chunks)} chunks after cleaning",
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)
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# ================================================================
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# STEP 9: Chunk Validation (REUSED)
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# ================================================================
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if use_llm:
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from utils.llm_validator import enrich_chunks_with_concepts
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callback("Chunk Validation", "running", "Enriching chunks with concepts...")
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# Enrich chunks with keywords/concepts
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enriched_chunks = enrich_chunks_with_concepts(
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chunks,
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provider=llm_provider,
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)
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chunks = enriched_chunks
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callback(
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"Chunk Validation",
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"completed",
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f"Validated {len(chunks)} chunks",
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)
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# ================================================================
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# STEP 10: Save Chunks JSON
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# ================================================================
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callback("Save Results", "running", "Saving chunks to JSON...")
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chunks_output = {
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"metadata": metadata,
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"toc": toc_flat,
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"classified_sections": classified_sections,
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"chunks": chunks,
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"cost_ocr": 0.0, # No OCR for Word documents
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"cost_llm": cost_llm,
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"cost_total": cost_llm,
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"paragraphs": content["total_paragraphs"],
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"chunks_count": len(chunks),
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}
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chunks_path = output_dir / f"{doc_name}_chunks.json"
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with open(chunks_path, "w", encoding="utf-8") as f:
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json.dump(chunks_output, f, indent=2, ensure_ascii=False, default=str)
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callback(
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"Save Results",
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"completed",
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f"Saved to {chunks_path.name}",
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)
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# ================================================================
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# STEP 11: Weaviate Ingestion (REUSED)
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# ================================================================
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if ingest_to_weaviate:
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from utils.weaviate_ingest import ingest_document
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callback("Weaviate Ingestion", "running", "Ingesting into Weaviate...")
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ingestion_result = ingest_document(
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metadata=metadata,
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chunks=chunks,
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toc=toc_flat,
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document_source_id=doc_name,
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)
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# Save ingestion results
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weaviate_path = output_dir / f"{doc_name}_weaviate.json"
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with open(weaviate_path, "w", encoding="utf-8") as f:
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json.dump(ingestion_result, f, indent=2, ensure_ascii=False, default=str)
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callback(
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"Weaviate Ingestion",
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"completed",
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f"Ingested {ingestion_result.get('chunks_ingested', 0)} chunks",
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)
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# ================================================================
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# Return Success Result
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# ================================================================
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return PipelineResult(
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success=True,
|
||||
document_name=doc_name,
|
||||
output_dir=output_dir,
|
||||
chunks_count=len(chunks),
|
||||
cost_ocr=0.0,
|
||||
cost_llm=cost_llm,
|
||||
cost_total=cost_llm,
|
||||
error="",
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"Pipeline failed: {str(e)}"
|
||||
callback("Pipeline Error", "error", error_msg)
|
||||
|
||||
return PipelineResult(
|
||||
success=False,
|
||||
document_name=word_path.stem,
|
||||
output_dir=Path("output") / word_path.stem,
|
||||
chunks_count=0,
|
||||
cost_ocr=0.0,
|
||||
cost_llm=0.0,
|
||||
cost_total=0.0,
|
||||
error=error_msg,
|
||||
)
|
||||
329
generations/library_rag/utils/word_processor.py
Normal file
329
generations/library_rag/utils/word_processor.py
Normal file
@@ -0,0 +1,329 @@
|
||||
"""Extract structured content from Microsoft Word documents (.docx).
|
||||
|
||||
This module provides functionality to extract text, headings, images, and metadata
|
||||
from Word documents using python-docx. The extracted content is structured to be
|
||||
compatible with the existing RAG pipeline (LLM processing and Weaviate ingestion).
|
||||
|
||||
Example:
|
||||
Extract content from a Word document:
|
||||
|
||||
from pathlib import Path
|
||||
from utils.word_processor import extract_word_content
|
||||
|
||||
result = extract_word_content(Path("document.docx"))
|
||||
print(f"Extracted {len(result['paragraphs'])} paragraphs")
|
||||
print(f"Found {len(result['headings'])} headings")
|
||||
|
||||
Extract only metadata:
|
||||
|
||||
metadata = extract_word_metadata(Path("document.docx"))
|
||||
print(f"Title: {metadata['title']}")
|
||||
print(f"Author: {metadata['author']}")
|
||||
|
||||
Note:
|
||||
Requires python-docx library: pip install python-docx>=0.8.11
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
from datetime import datetime
|
||||
import io
|
||||
import re
|
||||
|
||||
try:
|
||||
from docx import Document
|
||||
from docx.oxml.text.paragraph import CT_P
|
||||
from docx.oxml.table import CT_Tbl
|
||||
from docx.table import _Cell, Table
|
||||
from docx.text.paragraph import Paragraph
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"python-docx library is required for Word processing. "
|
||||
"Install with: pip install python-docx>=0.8.11"
|
||||
)
|
||||
|
||||
from utils.types import TOCEntry
|
||||
|
||||
|
||||
def extract_word_metadata(docx_path: Path) -> Dict[str, Any]:
|
||||
"""Extract metadata from Word document core properties.
|
||||
|
||||
Reads the document's core properties (title, author, created date, etc.)
|
||||
and attempts to extract additional metadata from the first few paragraphs
|
||||
if core properties are missing.
|
||||
|
||||
Args:
|
||||
docx_path: Path to the .docx file.
|
||||
|
||||
Returns:
|
||||
Dictionary containing metadata fields:
|
||||
- title (str): Document title
|
||||
- author (str): Document author
|
||||
- created (datetime): Creation date
|
||||
- modified (datetime): Last modified date
|
||||
- language (str): Document language (if available)
|
||||
- edition (str): Edition info (if found in content)
|
||||
|
||||
Example:
|
||||
>>> metadata = extract_word_metadata(Path("doc.docx"))
|
||||
>>> print(metadata["title"])
|
||||
'On the Origin of Species'
|
||||
"""
|
||||
doc = Document(docx_path)
|
||||
core_props = doc.core_properties
|
||||
|
||||
metadata = {
|
||||
"title": core_props.title or "",
|
||||
"author": core_props.author or "",
|
||||
"created": core_props.created,
|
||||
"modified": core_props.modified,
|
||||
"language": "",
|
||||
"edition": "",
|
||||
}
|
||||
|
||||
# If metadata missing, try to extract from first paragraphs
|
||||
# Common pattern: "TITRE: ...", "AUTEUR: ...", "EDITION: ..."
|
||||
if not metadata["title"] or not metadata["author"]:
|
||||
for para in doc.paragraphs[:10]: # Check first 10 paragraphs
|
||||
text = para.text.strip()
|
||||
|
||||
# Match patterns like "TITRE : On the Origin..."
|
||||
if text.upper().startswith("TITRE") and ":" in text:
|
||||
metadata["title"] = text.split(":", 1)[1].strip()
|
||||
|
||||
# Match patterns like "AUTEUR Charles DARWIN"
|
||||
elif text.upper().startswith("AUTEUR") and ":" in text:
|
||||
metadata["author"] = text.split(":", 1)[1].strip()
|
||||
elif text.upper().startswith("AUTEUR "):
|
||||
metadata["author"] = text[7:].strip() # Remove "AUTEUR "
|
||||
|
||||
# Match patterns like "EDITION : Sixth London Edition..."
|
||||
elif text.upper().startswith("EDITION") and ":" in text:
|
||||
metadata["edition"] = text.split(":", 1)[1].strip()
|
||||
|
||||
return metadata
|
||||
|
||||
|
||||
def _get_heading_level(style_name: str) -> Optional[int]:
|
||||
"""Extract heading level from Word style name.
|
||||
|
||||
Args:
|
||||
style_name: Word paragraph style name (e.g., "Heading 1", "Heading 2").
|
||||
|
||||
Returns:
|
||||
Heading level (1-9) if it's a heading style, None otherwise.
|
||||
|
||||
Example:
|
||||
>>> _get_heading_level("Heading 1")
|
||||
1
|
||||
>>> _get_heading_level("Heading 3")
|
||||
3
|
||||
>>> _get_heading_level("Normal")
|
||||
None
|
||||
"""
|
||||
# Match patterns: "Heading 1", "Heading 2", etc.
|
||||
match = re.match(r"Heading (\d)", style_name)
|
||||
if match:
|
||||
level = int(match.group(1))
|
||||
return level if 1 <= level <= 9 else None
|
||||
return None
|
||||
|
||||
|
||||
def extract_word_images(
|
||||
doc: Document,
|
||||
output_dir: Path,
|
||||
doc_name: str,
|
||||
) -> List[Path]:
|
||||
"""Extract inline images from Word document.
|
||||
|
||||
Saves all inline images (shapes, pictures) to the output directory
|
||||
with sequential numbering.
|
||||
|
||||
Args:
|
||||
doc: python-docx Document object.
|
||||
output_dir: Directory to save extracted images.
|
||||
doc_name: Document name for image filename prefix.
|
||||
|
||||
Returns:
|
||||
List of paths to extracted image files.
|
||||
|
||||
Example:
|
||||
>>> doc = Document("doc.docx")
|
||||
>>> images = extract_word_images(doc, Path("output"), "darwin")
|
||||
>>> print(f"Extracted {len(images)} images")
|
||||
"""
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
image_paths: List[Path] = []
|
||||
|
||||
image_counter = 0
|
||||
|
||||
# Extract images from document relationships
|
||||
for rel in doc.part.rels.values():
|
||||
if "image" in rel.target_ref:
|
||||
try:
|
||||
image_data = rel.target_part.blob
|
||||
|
||||
# Determine file extension from content type
|
||||
content_type = rel.target_part.content_type
|
||||
ext = "png" # default
|
||||
if "jpeg" in content_type or "jpg" in content_type:
|
||||
ext = "jpg"
|
||||
elif "png" in content_type:
|
||||
ext = "png"
|
||||
elif "gif" in content_type:
|
||||
ext = "gif"
|
||||
|
||||
# Save image
|
||||
image_filename = f"{doc_name}_image_{image_counter}.{ext}"
|
||||
image_path = output_dir / image_filename
|
||||
|
||||
with open(image_path, "wb") as f:
|
||||
f.write(image_data)
|
||||
|
||||
image_paths.append(image_path)
|
||||
image_counter += 1
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to extract image {image_counter}: {e}")
|
||||
|
||||
return image_paths
|
||||
|
||||
|
||||
def extract_word_content(docx_path: Path) -> Dict[str, Any]:
|
||||
"""Extract complete structured content from Word document.
|
||||
|
||||
Main extraction function that processes a Word document and extracts:
|
||||
- Full text content
|
||||
- Paragraph structure with styles
|
||||
- Heading hierarchy
|
||||
- Images (if any)
|
||||
- Raw metadata
|
||||
|
||||
Args:
|
||||
docx_path: Path to the .docx file.
|
||||
|
||||
Returns:
|
||||
Dictionary containing:
|
||||
- raw_text (str): Complete document text
|
||||
- paragraphs (List[Dict]): List of paragraph dicts with:
|
||||
- index (int): Paragraph index
|
||||
- style (str): Word style name
|
||||
- text (str): Paragraph text content
|
||||
- level (Optional[int]): Heading level (1-9) if heading
|
||||
- is_heading (bool): True if paragraph is a heading
|
||||
- headings (List[Dict]): List of heading paragraphs only
|
||||
- metadata_raw (Dict): Raw metadata from core properties
|
||||
- total_paragraphs (int): Total paragraph count
|
||||
- has_images (bool): Whether document contains images
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If docx_path does not exist.
|
||||
ValueError: If file is not a valid .docx document.
|
||||
|
||||
Example:
|
||||
>>> content = extract_word_content(Path("darwin.docx"))
|
||||
>>> print(f"Document has {content['total_paragraphs']} paragraphs")
|
||||
>>> print(f"Found {len(content['headings'])} headings")
|
||||
>>> for h in content['headings']:
|
||||
... print(f"H{h['level']}: {h['text'][:50]}")
|
||||
"""
|
||||
if not docx_path.exists():
|
||||
raise FileNotFoundError(f"Word document not found: {docx_path}")
|
||||
|
||||
if not docx_path.suffix.lower() == ".docx":
|
||||
raise ValueError(f"File must be .docx format: {docx_path}")
|
||||
|
||||
# Load document
|
||||
doc = Document(docx_path)
|
||||
|
||||
# Extract metadata
|
||||
metadata_raw = extract_word_metadata(docx_path)
|
||||
|
||||
# Process paragraphs
|
||||
paragraphs: List[Dict[str, Any]] = []
|
||||
headings: List[Dict[str, Any]] = []
|
||||
full_text_parts: List[str] = []
|
||||
|
||||
for idx, para in enumerate(doc.paragraphs):
|
||||
text = para.text.strip()
|
||||
style_name = para.style.name
|
||||
|
||||
# Determine if this is a heading and its level
|
||||
heading_level = _get_heading_level(style_name)
|
||||
is_heading = heading_level is not None
|
||||
|
||||
para_dict = {
|
||||
"index": idx,
|
||||
"style": style_name,
|
||||
"text": text,
|
||||
"level": heading_level,
|
||||
"is_heading": is_heading,
|
||||
}
|
||||
|
||||
paragraphs.append(para_dict)
|
||||
|
||||
if is_heading and text:
|
||||
headings.append(para_dict)
|
||||
|
||||
# Add to full text (skip empty paragraphs)
|
||||
if text:
|
||||
full_text_parts.append(text)
|
||||
|
||||
raw_text = "\n\n".join(full_text_parts)
|
||||
|
||||
# Check for images (we'll extract them later if needed)
|
||||
has_images = len(doc.part.rels) > 1 # More than just the document.xml relationship
|
||||
|
||||
return {
|
||||
"raw_text": raw_text,
|
||||
"paragraphs": paragraphs,
|
||||
"headings": headings,
|
||||
"metadata_raw": metadata_raw,
|
||||
"total_paragraphs": len(paragraphs),
|
||||
"has_images": has_images,
|
||||
}
|
||||
|
||||
|
||||
def build_markdown_from_word(
|
||||
paragraphs: List[Dict[str, Any]],
|
||||
skip_metadata_lines: int = 5,
|
||||
) -> str:
|
||||
"""Build Markdown text from Word document paragraphs.
|
||||
|
||||
Converts Word document structure to Markdown format compatible with
|
||||
the existing RAG pipeline. Heading styles are converted to Markdown
|
||||
headers (#, ##, ###, etc.).
|
||||
|
||||
Args:
|
||||
paragraphs: List of paragraph dicts from extract_word_content().
|
||||
skip_metadata_lines: Number of initial paragraphs to skip (metadata).
|
||||
Default: 5 (skip TITRE, AUTEUR, EDITION lines).
|
||||
|
||||
Returns:
|
||||
Markdown-formatted text.
|
||||
|
||||
Example:
|
||||
>>> content = extract_word_content(Path("doc.docx"))
|
||||
>>> markdown = build_markdown_from_word(content["paragraphs"])
|
||||
>>> with open("output.md", "w") as f:
|
||||
... f.write(markdown)
|
||||
"""
|
||||
markdown_lines: List[str] = []
|
||||
|
||||
for para in paragraphs[skip_metadata_lines:]:
|
||||
text = para["text"]
|
||||
|
||||
if not text:
|
||||
continue
|
||||
|
||||
if para["is_heading"] and para["level"]:
|
||||
# Convert heading to Markdown: Heading 1 -> #, Heading 2 -> ##, etc.
|
||||
level = para["level"]
|
||||
markdown_lines.append(f"{'#' * level} {text}")
|
||||
markdown_lines.append("") # Blank line after heading
|
||||
else:
|
||||
# Normal paragraph
|
||||
markdown_lines.append(text)
|
||||
markdown_lines.append("") # Blank line after paragraph
|
||||
|
||||
return "\n".join(markdown_lines).strip()
|
||||
229
generations/library_rag/utils/word_toc_extractor.py
Normal file
229
generations/library_rag/utils/word_toc_extractor.py
Normal file
@@ -0,0 +1,229 @@
|
||||
"""Extract hierarchical table of contents from Word document headings.
|
||||
|
||||
This module builds a structured TOC from Word heading styles (Heading 1-9),
|
||||
generating section paths compatible with the existing RAG pipeline and Weaviate
|
||||
schema (e.g., "1.2.3" for chapter 1, section 2, subsection 3).
|
||||
|
||||
Example:
|
||||
Build TOC from Word headings:
|
||||
|
||||
from pathlib import Path
|
||||
from utils.word_processor import extract_word_content
|
||||
from utils.word_toc_extractor import build_toc_from_headings
|
||||
|
||||
content = extract_word_content(Path("doc.docx"))
|
||||
toc = build_toc_from_headings(content["headings"])
|
||||
|
||||
for entry in toc:
|
||||
print(f"{entry['sectionPath']}: {entry['title']}")
|
||||
|
||||
Output:
|
||||
1: Introduction
|
||||
1.1: Background
|
||||
1.2: Methodology
|
||||
2: Results
|
||||
2.1: Analysis
|
||||
|
||||
Note:
|
||||
Compatible with existing TOCEntry TypedDict from utils.types
|
||||
"""
|
||||
|
||||
from typing import List, Dict, Any, Optional
|
||||
from utils.types import TOCEntry
|
||||
|
||||
|
||||
def _generate_section_path(
|
||||
level: int,
|
||||
counters: List[int],
|
||||
) -> str:
|
||||
"""Generate section path string from level counters.
|
||||
|
||||
Args:
|
||||
level: Current heading level (1-9).
|
||||
counters: List of counters for each level [c1, c2, c3, ...].
|
||||
|
||||
Returns:
|
||||
Section path string (e.g., "1.2.3").
|
||||
|
||||
Example:
|
||||
>>> _generate_section_path(3, [1, 2, 3, 0, 0])
|
||||
'1.2.3'
|
||||
>>> _generate_section_path(1, [2, 0, 0])
|
||||
'2'
|
||||
"""
|
||||
# Take counters up to current level
|
||||
path_parts = [str(c) for c in counters[:level] if c > 0]
|
||||
return ".".join(path_parts) if path_parts else "1"
|
||||
|
||||
|
||||
def build_toc_from_headings(
|
||||
headings: List[Dict[str, Any]],
|
||||
max_level: int = 9,
|
||||
) -> List[TOCEntry]:
|
||||
"""Build hierarchical table of contents from Word headings.
|
||||
|
||||
Processes a list of heading paragraphs (with level attribute) and constructs
|
||||
a hierarchical TOC structure with section paths (1, 1.1, 1.2, 2, 2.1, etc.).
|
||||
Handles nested headings and missing intermediate levels gracefully.
|
||||
|
||||
Args:
|
||||
headings: List of heading dicts from word_processor.extract_word_content().
|
||||
Each dict must have:
|
||||
- text (str): Heading text
|
||||
- level (int): Heading level (1-9)
|
||||
- index (int): Paragraph index in document
|
||||
max_level: Maximum heading level to process (default: 9).
|
||||
|
||||
Returns:
|
||||
List of TOCEntry dicts with hierarchical structure:
|
||||
- title (str): Heading text
|
||||
- level (int): Heading level (1-9)
|
||||
- sectionPath (str): Section path (e.g., "1.2.3")
|
||||
- pageRange (str): Empty string (not applicable for Word)
|
||||
- children (List[TOCEntry]): Nested sub-headings
|
||||
|
||||
Example:
|
||||
>>> headings = [
|
||||
... {"text": "Chapter 1", "level": 1, "index": 0},
|
||||
... {"text": "Section 1.1", "level": 2, "index": 1},
|
||||
... {"text": "Section 1.2", "level": 2, "index": 2},
|
||||
... {"text": "Chapter 2", "level": 1, "index": 3},
|
||||
... ]
|
||||
>>> toc = build_toc_from_headings(headings)
|
||||
>>> print(toc[0]["title"])
|
||||
'Chapter 1'
|
||||
>>> print(toc[0]["sectionPath"])
|
||||
'1'
|
||||
>>> print(toc[0]["children"][0]["sectionPath"])
|
||||
'1.1'
|
||||
|
||||
Note:
|
||||
- Empty headings are skipped
|
||||
- Handles missing intermediate levels (e.g., H1 → H3 without H2)
|
||||
- Section paths are 1-indexed (start from 1, not 0)
|
||||
"""
|
||||
if not headings:
|
||||
return []
|
||||
|
||||
toc: List[TOCEntry] = []
|
||||
counters = [0] * max_level # Track counters for each level [h1, h2, h3, ...]
|
||||
parent_stack: List[TOCEntry] = [] # Stack to track parent headings
|
||||
|
||||
for heading in headings:
|
||||
text = heading.get("text", "").strip()
|
||||
level = heading.get("level")
|
||||
|
||||
# Skip empty headings or invalid levels
|
||||
if not text or level is None or level < 1 or level > max_level:
|
||||
continue
|
||||
|
||||
level_idx = level - 1 # Convert to 0-indexed
|
||||
|
||||
# Increment counter for this level
|
||||
counters[level_idx] += 1
|
||||
|
||||
# Reset all deeper level counters
|
||||
for i in range(level_idx + 1, max_level):
|
||||
counters[i] = 0
|
||||
|
||||
# Generate section path
|
||||
section_path = _generate_section_path(level, counters)
|
||||
|
||||
# Create TOC entry
|
||||
entry: TOCEntry = {
|
||||
"title": text,
|
||||
"level": level,
|
||||
"sectionPath": section_path,
|
||||
"pageRange": "", # Not applicable for Word documents
|
||||
"children": [],
|
||||
}
|
||||
|
||||
# Determine parent and add to appropriate location
|
||||
if level == 1:
|
||||
# Top-level heading - add to root
|
||||
toc.append(entry)
|
||||
parent_stack = [entry] # Reset parent stack
|
||||
else:
|
||||
# Find appropriate parent in stack
|
||||
# Pop stack until we find a parent at level < current level
|
||||
while parent_stack and parent_stack[-1]["level"] >= level:
|
||||
parent_stack.pop()
|
||||
|
||||
if parent_stack:
|
||||
# Add to parent's children
|
||||
parent_stack[-1]["children"].append(entry)
|
||||
else:
|
||||
# No valid parent found (missing intermediate levels)
|
||||
# Add to root as a fallback
|
||||
toc.append(entry)
|
||||
|
||||
# Add current entry to parent stack
|
||||
parent_stack.append(entry)
|
||||
|
||||
return toc
|
||||
|
||||
|
||||
def flatten_toc(toc: List[TOCEntry]) -> List[TOCEntry]:
|
||||
"""Flatten hierarchical TOC into a flat list.
|
||||
|
||||
Converts nested TOC structure to a flat list while preserving section paths
|
||||
and hierarchy information. Useful for iteration and database ingestion.
|
||||
|
||||
Args:
|
||||
toc: Hierarchical TOC from build_toc_from_headings().
|
||||
|
||||
Returns:
|
||||
Flat list of all TOC entries (depth-first traversal).
|
||||
|
||||
Example:
|
||||
>>> toc = build_toc_from_headings(headings)
|
||||
>>> flat = flatten_toc(toc)
|
||||
>>> for entry in flat:
|
||||
... indent = " " * (entry["level"] - 1)
|
||||
... print(f"{indent}{entry['sectionPath']}: {entry['title']}")
|
||||
"""
|
||||
flat: List[TOCEntry] = []
|
||||
|
||||
def _traverse(entries: List[TOCEntry]) -> None:
|
||||
for entry in entries:
|
||||
# Add current entry (create a copy to avoid mutation)
|
||||
flat_entry: TOCEntry = {
|
||||
"title": entry["title"],
|
||||
"level": entry["level"],
|
||||
"sectionPath": entry["sectionPath"],
|
||||
"pageRange": entry["pageRange"],
|
||||
"children": [], # Don't include children in flat list
|
||||
}
|
||||
flat.append(flat_entry)
|
||||
|
||||
# Recursively traverse children
|
||||
if entry["children"]:
|
||||
_traverse(entry["children"])
|
||||
|
||||
_traverse(toc)
|
||||
return flat
|
||||
|
||||
|
||||
def print_toc_tree(
|
||||
toc: List[TOCEntry],
|
||||
indent: str = "",
|
||||
) -> None:
|
||||
"""Print TOC tree structure to console (debug helper).
|
||||
|
||||
Args:
|
||||
toc: Hierarchical TOC from build_toc_from_headings().
|
||||
indent: Indentation string for nested levels (internal use).
|
||||
|
||||
Example:
|
||||
>>> toc = build_toc_from_headings(headings)
|
||||
>>> print_toc_tree(toc)
|
||||
1: Introduction
|
||||
1.1: Background
|
||||
1.2: Methodology
|
||||
2: Results
|
||||
2.1: Analysis
|
||||
"""
|
||||
for entry in toc:
|
||||
print(f"{indent}{entry['sectionPath']}: {entry['title']}")
|
||||
if entry["children"]:
|
||||
print_toc_tree(entry["children"], indent + " ")
|
||||
Reference in New Issue
Block a user