koko210Serve
ae1e0aa144
Configuration: - .env.example, .gitignore, compose.yml (main docker compose) - docker-compose-amd.yml (ROCm), docker-compose-macos.yml - start.sh, stop.sh convenience scripts - LICENSE (Apache 2.0, from upstream Cheshire Cat) Memory management utilities: - analyze_consolidation.py, manual_consolidation.py, verify_consolidation.py - check_memories.py, extract_declarative_facts.py, store_declarative_facts.py - compare_systems.py (system comparison tool) - benchmark_cat.py, streaming_benchmark.py, streaming_benchmark_v2.py Test suite: - quick_test.py, test_setup.py, test_setup_simple.py - test_consolidation_direct.py, test_declarative_recall.py, test_recall.py - test_end_to_end.py, test_full_pipeline.py - test_phase2.py, test_phase2_comprehensive.py Documentation: - README.md, QUICK_START.txt, TEST_README.md, SETUP_COMPLETE.md - PHASE2_IMPLEMENTATION_NOTES.md, PHASE2_TEST_RESULTS.md - POST_OPTIMIZATION_ANALYSIS.md
33 lines
984 B
Python
33 lines
984 B
Python
from cat.mad_hatter.mad_hatter import MadHatter
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from cat.memory.vector_memory import VectorMemoryCollection
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from qdrant_client import QdrantClient
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# Connect to Qdrant
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client = QdrantClient(host="localhost", port=6333)
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# Check if collections exist
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collections = client.get_collections()
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print("Collections:", [c.name for c in collections.collections])
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# Try to query episodic directly
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episodic_points = client.scroll(
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collection_name="episodic",
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limit=5,
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with_payload=True,
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with_vectors=False
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)
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print(f"\nEpisodic memories found: {len(episodic_points[0])}")
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for point in episodic_points[0]:
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print(f" - {point.payload.get('page_content', '')[:100]}")
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# Try declarative
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declarative_points = client.scroll(
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collection_name="declarative",
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limit=5,
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with_payload=True,
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with_vectors=False
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)
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print(f"\nDeclarative facts found: {len(declarative_points[0])}")
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for point in declarative_points[0]:
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print(f" - {point.payload.get('page_content', '')}")
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