Add Library RAG project and cleanup root directory

- Add complete Library RAG application (Flask + MCP server)
  - PDF processing pipeline with OCR and LLM extraction
  - Weaviate vector database integration (BGE-M3 embeddings)
  - Flask web interface with search and document management
  - MCP server for Claude Desktop integration
  - Comprehensive test suite (134 tests)

- Clean up root directory
  - Remove obsolete documentation files
  - Remove backup and temporary files
  - Update autonomous agent configuration

- Update prompts
  - Enhance initializer bis prompt with better instructions

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

generations/library_rag/examples/example_direct_pipeline.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+"""
+Exemple d'utilisation DIRECTE du pipeline PDF (sans MCP).
+
+Plus simple et plus de contrôle sur les paramètres!
+"""
+
+from pathlib import Path
+from utils.pdf_pipeline import process_pdf, process_pdf_bytes
+import weaviate
+from weaviate.classes.query import Filter
+
+
+def example_process_local_file():
+    """Traiter un fichier local (PDF ou Markdown)."""
+
+    result = process_pdf(
+        pdf_path=Path("md/peirce_collected_papers_fixed.md"),
+        output_dir=Path("output"),
+
+        # Paramètres personnalisables
+        skip_ocr=True,                      # Déjà en Markdown
+        use_llm=False,                      # Pas besoin de LLM pour Peirce
+        use_semantic_chunking=False,        # Chunking basique (rapide)
+        ingest_to_weaviate=True,            # Ingérer dans Weaviate
+    )
+
+    if result.get("success"):
+        print(f"✓ {result['document_name']}: {result['chunks_count']} chunks")
+        print(f"  Coût total: {result['cost_total']:.4f}€")
+    else:
+        print(f"✗ Erreur: {result.get('error')}")
+
+
+def example_process_from_url():
+    """Télécharger et traiter depuis une URL."""
+
+    import httpx
+
+    url = "https://example.com/document.pdf"
+
+    # Télécharger
+    response = httpx.get(url, follow_redirects=True)
+    pdf_bytes = response.content
+
+    # Traiter
+    result = process_pdf_bytes(
+        file_bytes=pdf_bytes,
+        filename="document.pdf",
+        output_dir=Path("output"),
+
+        # Paramètres optimaux
+        use_llm=True,
+        llm_provider="mistral",             # Ou "ollama"
+        use_semantic_chunking=True,
+        ingest_to_weaviate=True,
+    )
+
+    return result
+
+
+def example_search():
+    """Rechercher directement dans Weaviate."""
+
+    client = weaviate.connect_to_local()
+
+    try:
+        collection = client.collections.get('Chunk')
+
+        # Recherche sémantique
+        response = collection.query.near_text(
+            query="nominalism and realism",
+            limit=10,
+        )
+
+        print(f"Trouvé {len(response.objects)} résultats:")
+        for obj in response.objects[:3]:
+            props = obj.properties
+            print(f"\n- {props.get('sectionPath', 'N/A')}")
+            print(f"  {props.get('text', '')[:150]}...")
+
+    finally:
+        client.close()
+
+
+if __name__ == "__main__":
+    # Choisir un exemple
+
+    # example_process_local_file()
+    # example_process_from_url()
+    example_search()