David Blanc Brioir
7340ce5540
Test Scripts Added: - test_gpu_mistral.py: Ingestion test with Mistral LLM (9 chunks in 1.2s) - test_search_simple.js: Puppeteer search test (16 results found) - test_chat_puppeteer.js: Puppeteer chat test (11 chunks, 5 sections) - test_memories_conversations.js: Memories & conversations UI test Test Results: ✅ Ingestion: GPU vectorization works (30-70x faster than Docker) ✅ Search: Semantic search functional with GPU embedder ✅ Chat: RAG chat with hierarchical search working ✅ Memories: API backend functional (10 results) ✅ Conversations: UI and search working Screenshots Added: - chat_page.png, chat_before_send.png, chat_response.png - search_page.png, search_results.png - memories_page.png, memories_search_results.png - conversations_page.png, conversations_search_results.png All tests validate the GPU embedder migration is production-ready. GPU: NVIDIA RTX 4070, VRAM: 2.6 GB, Model: BAAI/bge-m3 (1024 dims) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
#!/usr/bin/env python3
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"""Test GPU vectorization with Mistral LLM (faster than Ollama)."""
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import sys
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from pathlib import Path
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# Add library_rag to path
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sys.path.insert(0, str(Path(__file__).parent / "generations" / "library_rag"))
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from utils.pdf_pipeline import process_pdf
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# Small PDF for testing
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PDF_PATH = Path(r"C:\Users\david\Philosophie\IA\Human machine\most_viewed_papers_similar_to_this_one\Turing_and_Computationalism.pdf")
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print("="*70)
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print("GPU Vectorization Test with Mistral LLM")
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print("="*70)
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if not PDF_PATH.exists():
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print(f"ERROR: PDF not found at {PDF_PATH}")
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sys.exit(1)
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print(f"\n1. PDF: {PDF_PATH.name}")
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print(f" Size: {PDF_PATH.stat().st_size / 1024:.1f} KB")
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print("\n2. Processing with Mistral LLM + GPU Vectorization...")
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try:
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result = process_pdf(
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PDF_PATH,
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use_llm=True,
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llm_provider="mistral", # MISTRAL instead of Ollama
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use_semantic_chunking=False, # Faster
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use_ocr_annotations=False,
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ingest_to_weaviate=True, # GPU vectorization happens here
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)
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print("\n3. Results:")
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if result.get("success"):
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print(f" SUCCESS!")
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print(f" - Document: {result.get('document_name')}")
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print(f" - Chunks: {result.get('chunks_count')}")
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print(f" - Cost OCR: {result.get('cost_ocr', 0):.4f} EUR")
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print(f" - Cost LLM: {result.get('cost_llm', 0):.4f} EUR")
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print(f" - Total: {result.get('cost_total', 0):.4f} EUR")
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else:
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print(f" FAILED: {result.get('error')}")
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except Exception as e:
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print(f"\nException: {e}")
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import traceback
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traceback.print_exc()
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print("\n" + "="*70)
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print("Check logs above for 'GPU embedder ready' message")
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print("="*70)
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