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_python_usage.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+"""
+Exemple d'utilisation de Library RAG depuis une application Python.
+
+Le MCP server est uniquement pour Claude Desktop.
+Pour Python, appelez directement les handlers!
+"""
+
+import asyncio
+from pathlib import Path
+
+# Import direct des handlers
+from mcp_tools import (
+    parse_pdf_handler,
+    ParsePdfInput,
+    search_chunks_handler,
+    SearchChunksInput,
+)
+
+
+async def example_parse_pdf():
+    """Exemple: Traiter un PDF ou Markdown."""
+
+    # Depuis un chemin local
+    input_data = ParsePdfInput(
+        pdf_path="C:/Users/david/Documents/platon.pdf"
+    )
+
+    # OU depuis une URL
+    # input_data = ParsePdfInput(
+    #     pdf_path="https://example.com/aristotle.pdf"
+    # )
+
+    # OU un fichier Markdown
+    # input_data = ParsePdfInput(
+    #     pdf_path="/path/to/peirce.md"
+    # )
+
+    result = await parse_pdf_handler(input_data)
+
+    if result.success:
+        print(f"✓ Document traité: {result.document_name}")
+        print(f"  Pages: {result.pages}")
+        print(f"  Chunks: {result.chunks_count}")
+        print(f"  Coût: {result.cost_total:.4f}€")
+    else:
+        print(f"✗ Erreur: {result.error}")
+
+
+async def example_search():
+    """Exemple: Rechercher dans les chunks."""
+
+    input_data = SearchChunksInput(
+        query="nominalism and realism",
+        limit=10,
+        author_filter="Charles Sanders Peirce",  # Optionnel
+    )
+
+    result = await search_chunks_handler(input_data)
+
+    print(f"Trouvé {result.total_count} résultats:")
+    for i, chunk in enumerate(result.results[:5], 1):
+        print(f"\n[{i}] Similarité: {chunk.similarity:.3f}")
+        print(f"    {chunk.text[:200]}...")
+
+
+async def main():
+    """Point d'entrée principal."""
+
+    # Exemple 1: Traiter un PDF
+    # await example_parse_pdf()
+
+    # Exemple 2: Rechercher
+    await example_search()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())