forked from Rockachopa/Timmy-time-dashboard
3e31cafa83
Integrates Ollama embedding for semantic indexing of research outputs. Refactors memory_search and memory_store tools to align with issue requirements. - Added and to . - Modified to use and for generating embeddings via Ollama, with a fallback to . - Renamed to in , adjusting its signature to . - Updated in to use as default and pass confidence scoring. - Created to demonstrate indexing of research documents. Fixes #976
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
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import os
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import sys
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from pathlib import Path
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# Add the src directory to the Python path
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sys.path.insert(0, str(Path(__file__).parent / "src"))
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from timmy.memory_system import memory_store
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def index_research_documents():
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research_dir = Path("docs/research")
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if not research_dir.is_dir():
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print(f"Research directory not found: {research_dir}")
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return
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print(f"Indexing research documents from {research_dir}...")
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indexed_count = 0
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for file_path in research_dir.glob("*.md"):
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try:
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content = file_path.read_text()
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topic = file_path.stem.replace("-", " ").title() # Derive topic from filename
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print(f"Storing '{topic}' from {file_path.name}...")
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# Using type="research" as per issue requirement
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result = memory_store(topic=topic, report=content, type="research")
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print(f" Result: {result}")
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indexed_count += 1
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except Exception as e:
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print(f"Error indexing {file_path.name}: {e}")
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print(f"Finished indexing. Total documents indexed: {indexed_count}")
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if __name__ == "__main__":
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index_research_documents()
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