David Blanc Brioir
d2f7165120
- 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>
79 lines
1.9 KiB
Python
79 lines
1.9 KiB
Python
#!/usr/bin/env python3
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"""
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Exemple d'utilisation de Library RAG depuis une application Python.
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Le MCP server est uniquement pour Claude Desktop.
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Pour Python, appelez directement les handlers!
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"""
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import asyncio
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from pathlib import Path
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# Import direct des handlers
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from mcp_tools import (
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parse_pdf_handler,
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ParsePdfInput,
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search_chunks_handler,
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SearchChunksInput,
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)
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async def example_parse_pdf():
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"""Exemple: Traiter un PDF ou Markdown."""
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# Depuis un chemin local
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input_data = ParsePdfInput(
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pdf_path="C:/Users/david/Documents/platon.pdf"
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)
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# OU depuis une URL
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# input_data = ParsePdfInput(
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# pdf_path="https://example.com/aristotle.pdf"
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# )
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# OU un fichier Markdown
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# input_data = ParsePdfInput(
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# pdf_path="/path/to/peirce.md"
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# )
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result = await parse_pdf_handler(input_data)
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if result.success:
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print(f"✓ Document traité: {result.document_name}")
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print(f" Pages: {result.pages}")
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print(f" Chunks: {result.chunks_count}")
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print(f" Coût: {result.cost_total:.4f}€")
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else:
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print(f"✗ Erreur: {result.error}")
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async def example_search():
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"""Exemple: Rechercher dans les chunks."""
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input_data = SearchChunksInput(
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query="nominalism and realism",
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limit=10,
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author_filter="Charles Sanders Peirce", # Optionnel
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)
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result = await search_chunks_handler(input_data)
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print(f"Trouvé {result.total_count} résultats:")
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for i, chunk in enumerate(result.results[:5], 1):
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print(f"\n[{i}] Similarité: {chunk.similarity:.3f}")
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print(f" {chunk.text[:200]}...")
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async def main():
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"""Point d'entrée principal."""
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# Exemple 1: Traiter un PDF
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# await example_parse_pdf()
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# Exemple 2: Rechercher
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await example_search()
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if __name__ == "__main__":
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asyncio.run(main())
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