2858e0a344
Features: - Vector search with Pinecone + Vertex AI embeddings - Document relationships (link, unlink, related, graph) - Auto-link with LLM analysis - Intelligent merge with Gemini Modular structure: - clients/: Pinecone, Vertex AI - tools/: core, relations, stats - utils/: validation, logging Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
"""Statistics and monitoring tools."""
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from clients import get_index
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from utils.logging import get_logger
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from config import AUTO_LINK_THRESHOLD, AUTO_LINK_TOP_K
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logger = get_logger(__name__)
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def rag_stats() -> str:
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"""
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Return RAG database statistics.
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Returns:
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Formatted statistics or error message
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"""
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try:
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index = get_index()
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stats = index.describe_index_stats()
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total = stats.get("total_vector_count", 0)
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output = [
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"=== RAG Statistics ===",
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f"Total documents: {total}",
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f"Dimension: {stats.get('dimension', 'N/A')}",
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f"Index fullness: {stats.get('index_fullness', 'N/A')}",
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"",
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"=== Auto-link Settings ===",
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f"Threshold: {AUTO_LINK_THRESHOLD}",
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f"Top-K candidates: {AUTO_LINK_TOP_K}"
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]
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logger.info(f"Stats retrieved: {total} documents")
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return "\n".join(output)
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except Exception as e:
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logger.error(f"rag_stats failed: {str(e)}", exc_info=True)
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return f"Error: {str(e)}"
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