Add scripts for inspecting LLM summaries and testing misleading comments

- 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.

codex-lens/scripts/show_llm_analysis.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+"""Directly show LLM analysis of test code."""
+
+from codexlens.semantic.llm_enhancer import LLMEnhancer, LLMConfig, FileData
+
+# Misleading code example
+TEST_CODE = '''"""Email sending service."""
+import psycopg2
+from psycopg2 import pool
+from contextlib import contextmanager
+
+class EmailSender:
+    """SMTP email sender with retry logic."""
+
+    def __init__(self, min_conn: int = 1, max_conn: int = 10):
+        """Initialize email sender."""
+        self.pool = psycopg2.pool.SimpleConnectionPool(
+            min_conn, max_conn,
+            user='dbuser', host='localhost', database='myapp'
+        )
+
+    @contextmanager
+    def send_email(self):
+        """Send email message."""
+        conn = self.pool.getconn()
+        try:
+            yield conn
+            conn.commit()
+        finally:
+            self.pool.putconn(conn)
+'''
+
+print("="*80)
+print("LLM ANALYSIS OF MISLEADING CODE")
+print("="*80)
+
+print("\n[Original Code with Misleading Comments]")
+print("-"*80)
+print(TEST_CODE)
+print("-"*80)
+
+print("\n[Actual Functionality]")
+print("  - Imports: psycopg2 (PostgreSQL library)")
+print("  - Class: EmailSender (but name is misleading!)")
+print("  - Actually: Creates PostgreSQL connection pool")
+print("  - Methods: send_email (actually gets DB connection)")
+
+print("\n[Misleading Documentation]")
+print("  - Module docstring: 'Email sending service' (WRONG)")
+print("  - Class docstring: 'SMTP email sender' (WRONG)")
+print("  - Method docstring: 'Send email message' (WRONG)")
+
+print("\n" + "="*80)
+print("TESTING LLM UNDERSTANDING")
+print("="*80)
+
+# Test LLM analysis
+config = LLMConfig(enabled=True, tool="gemini", batch_size=1)
+enhancer = LLMEnhancer(config)
+
+if not enhancer.check_available():
+    print("\n[X] CCW CLI not available")
+    print("Install: npm install -g ccw")
+    exit(1)
+
+print("\n[Calling Gemini to analyze code...]")
+file_data = FileData(path="db/pool.py", content=TEST_CODE, language="python")
+
+import tempfile
+from pathlib import Path
+
+with tempfile.TemporaryDirectory() as tmpdir:
+    result = enhancer.enhance_files([file_data], Path(tmpdir))
+
+    if "db/pool.py" in result:
+        metadata = result["db/pool.py"]
+
+        print("\n[LLM-Generated Summary]")
+        print("-"*80)
+        print(f"Summary: {metadata.summary}")
+        print(f"\nPurpose: {metadata.purpose}")
+        print(f"\nKeywords: {', '.join(metadata.keywords)}")
+        print("-"*80)
+
+        print("\n[Analysis]")
+        # Check if LLM identified the real functionality
+        summary_lower = metadata.summary.lower()
+        keywords_lower = [k.lower() for k in metadata.keywords]
+
+        correct_terms = ['database', 'postgresql', 'connection', 'pool', 'psycopg']
+        misleading_terms = ['email', 'smtp', 'send']
+
+        found_correct = sum(1 for term in correct_terms
+                           if term in summary_lower or any(term in k for k in keywords_lower))
+        found_misleading = sum(1 for term in misleading_terms
+                              if term in summary_lower or any(term in k for k in keywords_lower))
+
+        print(f"Correct terms found: {found_correct}/{len(correct_terms)}")
+        print(f"Misleading terms found: {found_misleading}/{len(misleading_terms)}")
+
+        if found_correct > found_misleading:
+            print("\n[OK] LLM correctly identified actual functionality!")
+            print("     LLM ignored misleading comments and analyzed code behavior")
+        elif found_misleading > found_correct:
+            print("\n[X] LLM was misled by incorrect comments")
+            print("    LLM trusted documentation over code analysis")
+        else:
+            print("\n[~] Mixed results - LLM found both correct and misleading terms")
+    else:
+        print("\n[X] LLM analysis failed - no results returned")
+
+print("\n" + "="*80)