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https://github.com/catlog22/Claude-Code-Workflow.git
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d21066c282
- Implement `inspect_llm_summaries.py` to display LLM-generated summaries from the semantic_chunks table in the database. - Create `show_llm_analysis.py` to demonstrate LLM analysis of misleading code examples, highlighting discrepancies between comments and actual functionality. - Develop `test_misleading_comments.py` to compare pure vector search with LLM-enhanced search, focusing on the impact of misleading or missing comments on search results. - Introduce `test_llm_enhanced_search.py` to provide a test suite for evaluating the effectiveness of LLM-enhanced vector search against pure vector search. - Ensure all new scripts are integrated with the existing codebase and follow the established coding standards.
89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
#!/usr/bin/env python3
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"""Inspect LLM-generated summaries in semantic_chunks table."""
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import sqlite3
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import sys
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from pathlib import Path
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def inspect_summaries(db_path: Path):
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"""Show LLM-generated summaries from database."""
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if not db_path.exists():
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print(f"Error: Database not found: {db_path}")
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return
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with sqlite3.connect(db_path) as conn:
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conn.row_factory = sqlite3.Row
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# Check if semantic_chunks table exists
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cursor = conn.execute(
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"SELECT name FROM sqlite_master WHERE type='table' AND name='semantic_chunks'"
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)
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if not cursor.fetchone():
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print("No semantic_chunks table found")
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return
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# Get all chunks with metadata
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cursor = conn.execute("""
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SELECT file_path, chunk_index, content,
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json_extract(metadata, '$.llm_summary') as summary,
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json_extract(metadata, '$.llm_keywords') as keywords,
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json_extract(metadata, '$.llm_purpose') as purpose,
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json_extract(metadata, '$.strategy') as strategy
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FROM semantic_chunks
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ORDER BY file_path, chunk_index
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""")
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chunks = cursor.fetchall()
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if not chunks:
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print("No chunks found in database")
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return
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print("="*80)
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print("LLM-GENERATED SUMMARIES INSPECTION")
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print("="*80)
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current_file = None
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for chunk in chunks:
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file_path = chunk['file_path']
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if file_path != current_file:
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print(f"\n{'='*80}")
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print(f"FILE: {file_path}")
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print(f"{'='*80}")
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current_file = file_path
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print(f"\n[Chunk {chunk['chunk_index']}]")
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print(f"Strategy: {chunk['strategy']}")
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if chunk['summary']:
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print(f"\nLLM Summary:")
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print(f" {chunk['summary']}")
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if chunk['keywords']:
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print(f"\nKeywords:")
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print(f" {chunk['keywords']}")
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if chunk['purpose']:
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print(f"\nPurpose:")
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print(f" {chunk['purpose']}")
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# Show first 200 chars of content
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content = chunk['content']
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if len(content) > 200:
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content = content[:200] + "..."
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print(f"\nOriginal Content (first 200 chars):")
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print(f" {content}")
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print("-" * 80)
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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print("Usage: python inspect_llm_summaries.py <path_to_index.db>")
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print("\nExample:")
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print(" python inspect_llm_summaries.py ~/.codexlens/indexes/myproject/_index.db")
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sys.exit(1)
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db_path = Path(sys.argv[1])
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inspect_summaries(db_path)
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