feat: add database mode for LLM message storage and analytics

Implements a database mode that stores LLM prompts and responses in PostgreSQL
instead of making API calls. This enables:
- Testing without consuming API credits
- Collecting analytics on usage patterns
- Development and debugging workflows

Changes:
- Added DatabaseClient class for PostgreSQL operations
- Modified LLMAnalyzer to support database/API mode toggle
- Added USE_DATABASE config flag to switch between modes
- Included Docker Compose setup for PostgreSQL
- Added utility scripts for database init and analytics viewing
- Comprehensive documentation in DATABASE_MODE.md

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

scripts/view_messages.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+"""View stored messages from the database.
+
+This script displays stored LLM messages with filtering options.
+
+Usage:
+    python scripts/view_messages.py [--company COMPANY] [--type TYPE] [--limit LIMIT]
+"""
+
+import sys
+import os
+import argparse
+from datetime import datetime
+
+# Add parent directory to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from SPARC import config
+from SPARC.database import DatabaseClient
+
+
+def main():
+    """Display messages from the database."""
+    parser = argparse.ArgumentParser(description="View stored SPARC messages")
+    parser.add_argument(
+        "--company",
+        type=str,
+        help="Filter by company name"
+    )
+    parser.add_argument(
+        "--type",
+        type=str,
+        choices=["single_patent", "portfolio"],
+        help="Filter by analysis type"
+    )
+    parser.add_argument(
+        "--limit",
+        type=int,
+        default=10,
+        help="Maximum number of messages to display (default: 10)"
+    )
+    args = parser.parse_args()
+
+    print("SPARC Stored Messages")
+    print("=" * 70)
+
+    try:
+        db_client = DatabaseClient(config.database_url)
+        messages = db_client.get_messages(
+            company_name=args.company,
+            analysis_type=args.type,
+            limit=args.limit
+        )
+
+        if not messages:
+            print("\nNo messages found.")
+            return
+
+        print(f"\nShowing {len(messages)} message(s):\n")
+
+        for i, msg in enumerate(messages, 1):
+            print(f"Message #{msg['id']} - {msg['timestamp']}")
+            print(f"Company: {msg['company_name'] or '(unknown)'}")
+            print(f"Type: {msg['analysis_type'] or '(unknown)'}")
+            print(f"Model: {msg['model'] or '(unknown)'}")
+            print(f"\nPrompt (first 200 chars):")
+            print(f"  {msg['prompt'][:200]}...")
+            print(f"\nResponse (first 200 chars):")
+            print(f"  {msg['response'][:200] if msg['response'] else '(no response)'}...")
+            print("\n" + "-" * 70 + "\n")
+
+    except Exception as e:
+        print(f"Error retrieving messages: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()