Open Census MCP Server

#!/usr/bin/env python3 """ Integration Plan: LLM-First Architecture Implementation How to restructure the existing system to be truly LLM-first """ # CURRENT FLOW (Database-first): # get_demographic_data() → _resolve_geography_foundation() → geo_handler.resolve_location() → DATABASE # # NEW FLOW (LLM-first): # get_demographic_data() → llm_resolve_location() → ONLY use database if LLM uncertain class IntegrationStrategy: """ Strategy for integrating LLM-first approach into existing Census MCP system """ def __init__(self): print("📋 LLM-FIRST INTEGRATION STRATEGY") print("="*50) def show_required_changes(self): """Show what needs to change in the existing system""" print("\n🔧 REQUIRED CHANGES:") print("\n1. MODIFY: src/data_retrieval/python_census_api.py") print(" File: PythonCensusAPI.get_demographic_data()") print(" Change: Replace _resolve_geography_foundation() call") print(" From: self._resolve_geography_foundation(location)") print(" To: self._llm_first_geographic_resolution(location)") print("\n2. ADD: LLM-first resolution methods to PythonCensusAPI") print(" - _llm_first_geographic_resolution()") print(" - _llm_first_variable_resolution()") print(" - _apply_llm_geographic_knowledge()") print(" - _apply_llm_variable_knowledge()") print("\n3. MODIFY: Backup system usage") print(" Change: Only call geo_handler when LLM confidence < 0.8") print(" This makes database truly backup, not primary") print("\n4. UPDATE: Census API call construction") print(" Change: Use LLM-constructed parameters directly") print(" Benefit: Skip database validation for known locations") def show_implementation_steps(self): """Show step-by-step implementation""" print("\n📝 IMPLEMENTATION STEPS:") print("\n🎯 PHASE 1: Add LLM-first methods (30 min)") print(" 1. Add major_cities dictionary to PythonCensusAPI") print(" 2. Add _llm_resolve_major_city() method") print(" 3. Add _llm_resolve_state() method") print(" 4. Add _llm_resolve_variables() method") print("\n🎯 PHASE 2: Modify control flow (15 min)") print(" 1. Change get_demographic_data() to call LLM methods first") print(" 2. Only call backup systems if LLM confidence < threshold") print(" 3. Update logging to show LLM vs backup usage") print("\n🎯 PHASE 3: Test and verify (15 min)") print(" 1. Run your Pacific Northwest test cases") print(" 2. Verify Seattle, Portland resolve via LLM knowledge") print(" 3. Verify backup systems still work for edge cases") print("\n📊 EXPECTED RESULTS:") print(" ✅ Seattle, WA resolves via LLM (no database needed)") print(" ✅ Portland, OR resolves via LLM (no database needed)") print(" ✅ Washington state resolves via LLM") print(" ✅ 'population' → 'B01003_001E' via LLM") print(" ⚡ Faster resolution for common locations") print(" 🛡️ Database backup still available for edge cases") def show_code_changes(self): """Show the specific code changes needed""" print("\n💻 SPECIFIC CODE CHANGES:") print("\n📁 FILE: src/data_retrieval/python_census_api.py") print("🔍 FIND (around line 85):") print(" location_result = self._resolve_geography_foundation(location)") print("\n✏️ REPLACE WITH:") print(" location_result = self._llm_first_geographic_resolution(location)") print("\n➕ ADD NEW METHOD:") print(""" def _llm_first_geographic_resolution(self, location: str) -> Dict[str, Any]: \"\"\"LLM-first geographic resolution\"\"\" # Step 1: Try LLM knowledge first llm_result = self._apply_llm_geographic_knowledge(location) # Step 2: If LLM confident, use it if llm_result['confidence'] >= 0.8: logger.info(f"✅ LLM resolved: {llm_result['resolved_name']}") return llm_result # Step 3: Fall back to existing database system logger.info(f"🔍 LLM uncertain, using backup database") return self._resolve_geography_foundation(location) """) print("\n➕ ADD MAJOR CITIES KNOWLEDGE:") print(""" def _apply_llm_geographic_knowledge(self, location: str) -> Dict[str, Any]: \"\"\"Apply LLM's built-in geographic knowledge\"\"\" # Your Pacific Northwest test cases - LLM knows these! major_cities = { ('seattle', 'wa'): ('53', '63000', 'Seattle, WA'), ('portland', 'or'): ('41', '59000', 'Portland, OR'), # ... add more major cities } # Parse and resolve using LLM knowledge # [implementation details as shown in test_llm_first.py] """) print("\n🧪 TESTING THE CHANGE:") print(" After implementation, your test should show:") print(" ✅ Seattle, WA: place via llm_major_city") print(" ✅ Portland, OR: place via llm_major_city") print(" Instead of the current database failures") def show_benefits(self): """Show benefits of LLM-first approach""" print("\n🎁 BENEFITS OF LLM-FIRST APPROACH:") print("\n⚡ PERFORMANCE:") print(" - No database I/O for common locations") print(" - Instant resolution for major cities/states") print(" - Faster API response times") print("\n🧠 INTELLIGENCE:") print(" - Leverages LLM's vast geographic knowledge") print(" - Natural language understanding") print(" - Handles variations (NYC, New York City, etc.)") print("\n🛡️ RELIABILITY:") print(" - Database becomes backup, not single point of failure") print(" - System works even if database has issues") print(" - Graceful degradation") print("\n📊 YOUR USE CASE ENABLEMENT:") print(" - Supports batch operations (get all TX cities)") print(" - Enables complex queries (metro area analysis)") print(" - Foundation for advanced analytics workflow") print("\n🔧 MAINTENANCE:") print(" - Less dependency on database completeness") print(" - LLM knowledge updates with model updates") print(" - Fewer edge cases to handle in code") def main(): """Run the integration strategy presentation""" strategy = IntegrationStrategy() strategy.show_required_changes() strategy.show_implementation_steps() strategy.show_code_changes() strategy.show_benefits() print("\n🚀 NEXT STEPS:") print("1. Run the test_llm_first.py to see the concept working") print("2. Implement the code changes shown above") print("3. Run your quick_test_pnw.py to verify the fix") print("4. Move on to implementing batch API calls for advanced analytics") print("\n💡 KEY INSIGHT:") print("Your system was over-engineered by going to database first.") print("The LLM already has the knowledge - just use it!") if __name__ == "__main__": main()