feat: Optimize chunk sizes with 1000-word limit and overlap
Implemented chunking optimization to resolve oversized chunks and improve semantic search quality: CHUNKING IMPROVEMENTS: - Added strict 1000-word max limit (vs previous 1500-2000) - Implemented 100-word overlap between consecutive chunks - Created llm_chunker_improved.py with overlap functionality - Added 3 fallback points in llm_chunker.py for robustness RE-CHUNKING RESULTS: - Identified and re-chunked 31 oversized chunks (>2000 tokens) - Split into 92 optimally-sized chunks (max 1995 tokens) - Preserved all metadata (workTitle, workAuthor, sectionPath, etc.) - 0 chunks now exceed 2000 tokens (vs 31 before) VECTORIZATION: - Created manual vectorization script for chunks without vectors - Successfully vectorized all 92 new chunks (100% coverage) - All 5,304 chunks now have BGE-M3 embeddings DOCKER CONFIGURATION: - Exposed text2vec-transformers port 8090 for manual vectorization - Added cluster configuration to fix "No private IP address found" - Increased worker timeout to 600s for large chunks TESTING: - Created comprehensive search quality test suite - Tests distribution, overlap detection, and semantic search - Modified to use near_vector() (Chunk_v2 has no vectorizer) Scripts: - 08_fix_summaries_properties.py - Add missing Work metadata to summaries - 09_rechunk_oversized.py - Re-chunk giant chunks with overlap - 10_test_search_quality.py - Validate search improvements - 11_vectorize_missing_chunks.py - Manual vectorization via API Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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@@ -52,9 +52,15 @@ from .llm_structurer import (
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)
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from .llm_cleaner import clean_page_markers, is_chunk_valid
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from .types import LLMProvider, SemanticChunk
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from .llm_chunker_improved import simple_chunk_with_overlap, validate_chunk_size
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logger: logging.Logger = logging.getLogger(__name__)
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# Chunk size limits (2024-01 optimization)
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MAX_CHUNK_WORDS = 1000 # Hard limit to stay within BGE-M3 context
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OVERLAP_WORDS = 100 # Overlap for context preservation
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FORCE_SIMPLE_CHUNKING_THRESHOLD = 1500 # Words - force simple chunking above this
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# =============================================================================
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# Type Definitions for LLM Chunker
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@@ -221,8 +227,43 @@ def chunk_section_with_llm(
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# Nettoyer le contenu
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content: str = clean_page_markers(section_content)
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# Si le contenu est court, ne pas découper
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# Compter les mots
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word_count: int = len(content.split())
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# FORCE SIMPLE CHUNKING if section is too long (> 1500 words)
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# This prevents giant chunks that exceed BGE-M3 limits
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if word_count > FORCE_SIMPLE_CHUNKING_THRESHOLD:
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logger.warning(
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f"Section '{section_title}' is too long ({word_count} words), "
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f"forcing simple chunking with overlap"
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)
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simple_texts = simple_chunk_with_overlap(
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content,
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max_words=MAX_CHUNK_WORDS,
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overlap_words=OVERLAP_WORDS
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)
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# Convert to SemanticChunk format
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result_chunks: List[SemanticChunk] = []
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for i, text in enumerate(simple_texts):
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para_num = extract_paragraph_number(text)
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chunk: SemanticChunk = {
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"text": text,
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"summary": f"{section_title} (partie {i+1}/{len(simple_texts)})",
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"concepts": [],
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"type": "main_content",
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"section_level": section_level,
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}
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if para_num is not None:
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chunk["paragraph_number"] = para_num
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if subsection_title and subsection_title != section_title:
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chunk["subsection_title"] = subsection_title
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result_chunks.append(chunk)
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logger.info(f"Section split into {len(result_chunks)} chunks with overlap")
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return result_chunks
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# Si le contenu est court, ne pas découper
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if word_count < target_chunk_size * 0.8:
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para_num: Optional[int] = extract_paragraph_number(content)
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chunk: SemanticChunk = {
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@@ -320,39 +361,66 @@ RÉPONDS avec un JSON entre <JSON></JSON>:
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valid_chunks.append(chunk_data)
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# Si aucun chunk valide, retourner le contenu complet
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# Si aucun chunk valide, utiliser simple chunking avec overlap
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if not valid_chunks:
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logger.warning(f"Aucun chunk valide pour '{section_title}', retour contenu complet")
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para_num = extract_paragraph_number(content)
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fallback: SemanticChunk = {
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"text": content,
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"summary": section_title,
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"concepts": [],
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"type": "main_content",
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"section_level": section_level,
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}
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if para_num is not None:
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fallback["paragraph_number"] = para_num
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return [fallback]
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logger.warning(
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f"Aucun chunk valide pour '{section_title}', "
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f"fallback vers simple chunking avec overlap"
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)
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simple_texts = simple_chunk_with_overlap(
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content,
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max_words=MAX_CHUNK_WORDS,
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overlap_words=OVERLAP_WORDS
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)
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fallback_chunks: List[SemanticChunk] = []
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for i, text in enumerate(simple_texts):
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para_num = extract_paragraph_number(text)
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chunk_data: SemanticChunk = {
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"text": text,
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"summary": f"{section_title} (partie {i+1}/{len(simple_texts)})",
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"concepts": [],
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"type": "main_content",
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"section_level": section_level,
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}
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if para_num is not None:
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chunk_data["paragraph_number"] = para_num
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fallback_chunks.append(chunk_data)
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logger.info(f"Fallback: section split into {len(fallback_chunks)} chunks")
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return fallback_chunks
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logger.info(f"Section '{section_title}' découpée en {len(valid_chunks)} chunks")
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return valid_chunks
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except Exception as e:
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logger.error(f"Erreur chunking LLM: {e}")
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# Fallback: retourner le contenu complet
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para_num = extract_paragraph_number(content)
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fallback_err: SemanticChunk = {
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"text": content,
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"summary": section_title,
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"concepts": [],
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"type": "main_content",
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"section_level": section_level,
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"error": str(e),
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}
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if para_num is not None:
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fallback_err["paragraph_number"] = para_num
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return [fallback_err]
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# Fallback: utiliser simple chunking avec overlap
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logger.warning(f"Exception LLM, fallback vers simple chunking avec overlap")
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simple_texts = simple_chunk_with_overlap(
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content,
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max_words=MAX_CHUNK_WORDS,
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overlap_words=OVERLAP_WORDS
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)
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error_chunks: List[SemanticChunk] = []
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for i, text in enumerate(simple_texts):
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para_num = extract_paragraph_number(text)
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chunk_data: SemanticChunk = {
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"text": text,
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"summary": f"{section_title} (partie {i+1}/{len(simple_texts)})",
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"concepts": [],
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"type": "main_content",
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"section_level": section_level,
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"error": f"LLM failed: {str(e)}",
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}
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if para_num is not None:
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chunk_data["paragraph_number"] = para_num
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error_chunks.append(chunk_data)
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logger.info(f"Error fallback: section split into {len(error_chunks)} chunks")
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return error_chunks
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def simple_chunk_by_paragraphs(
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