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:
2025-12-30 21:58:43 +01:00
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"""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 + " ")