Fabric MCP Agent

# Performance Optimization Guide ## ✅ **IMPLEMENTED: Token Usage Optimization Results** ### 🎯 **Data Compression Breakthrough** **Real-world test results** from SPT pricing queries: | Metric | Before Optimization | After Optimization | Improvement | |--------|-------------------|-------------------|-------------| | **Data Size** | 75,546 characters | 939 characters | **98.8% reduction** | | **Token Usage** | ~18,800 tokens | ~235 tokens | **98.7% reduction** | | **Cost per Query** | ~$0.28 | ~$0.007 | **97.5% cost savings** | | **Compression Time** | N/A | <1ms | **Instant** | ### 🔧 **Implementation Details** - **Technology**: Common field extraction with data deduplication - **Applied to**: Stage 1 intermediate processing, Stage 3 evaluation, fallback scenarios - **Performance**: Zero latency impact - compression is faster than network transfer - **Reliability**: Maintains 100% semantic accuracy - no data loss ### 📊 **Session-Based Logging Implemented** - **Before**: Fragmented logs across multiple files (api_calls.log, errors.log, performance.log) - **After**: Complete session traces in `logs/sessions/` with compression statistics - **Benefits**: Real-time cost monitoring, compression ratio tracking, easy debugging ## Current Performance Baseline ### Multi-Stage Execution Analysis **Total Execution Time**: 35-57 seconds (varies by query complexity) | Stage | Duration | Operations | Bottleneck Type | |-------|----------|------------|-----------------| | Intent Classification | 3-5s (8%) | LLM routing decision | LLM Processing | | Stage 1: SQL Gen + Execute | 14-22s (40%) | SQL generation + DB query | **LLM Processing** | | Stage 2: SQL Gen + Execute | 16-18s (35%) | SQL generation + DB query | **LLM Processing** | | Stage 3: Evaluation | 7-12s (17%) | Data compression + LLM analysis | **LLM Processing** | ### Key Performance Insights - **LLM operations dominate**: 30-40s (75%) of total execution time - **Database execution is fast**: ~1s actual query execution (cached: 0.7s) - **SQL generation is the bottleneck**: 14-16 seconds per LLM-generated query - **Token compression saves cost, not time**: 98.8% cost reduction with <1ms overhead ## Optimization Strategy ### 🚀 High Impact Optimizations (Target: 50% reduction) #### 1. SQL Generation Caching **Current Issue**: Each stage regenerates SQL from scratch **Solution**: Implement pattern-based SQL caching ```python # Cache frequently used SQL patterns SQL_PATTERN_CACHE = { "product_discovery": { "pattern": "SELECT pt.pt_part, pt.pt_desc1 FROM JPNPROdb_pt_mstr pt WHERE {search_conditions}", "variables": ["search_conditions"], "ttl": 3600 # 1 hour cache } } ``` **Expected Impact**: 60-70% reduction in Stage 1 & 2 times (14.4s → 5s, 15.7s → 6s) #### 2. Parallel Tool Execution **Current Issue**: Sequential tool execution in each stage **Solution**: Run independent operations in parallel ```python # Parallel execution example async def execute_stage_with_parallel(): sql_task = asyncio.create_task(run_sql_query(enhanced_question)) summary_task = asyncio.create_task(prepare_summary_context()) sql_result = await sql_task summary_result = await summary_task.run_with_data(sql_result) ``` **Expected Impact**: 20-30% reduction in overall execution time #### 3. Database Query Optimization **Current Issue**: Complex JOIN operations without proper indexing **Solution**: Add targeted indexes for common query patterns ```sql -- Recommended indexes for performance CREATE INDEX IX_pt_mstr_search ON JPNPROdb_pt_mstr (pt_desc1, pt_desc2, pt_part); CREATE INDEX IX_ps_mstr_lookup ON JPNPROdb_ps_mstr (ps_comp, ps_par, ps_domain); CREATE INDEX IX_nqpr_pricing ON JPNPROdb_nqpr_mstr (nqpr_comp, nqpr_domain, nqpr_price); ``` **Expected Impact**: 30-40% reduction in SQL execution time ### 🎯 Medium Impact Optimizations #### 4. LLM Response Caching **Current Issue**: Repeated LLM calls for similar queries **Solution**: Implement semantic caching for LLM responses ```python # Semantic similarity caching from sentence_transformers import SentenceTransformer class SemanticCache: def __init__(self, similarity_threshold=0.95): self.encoder = SentenceTransformer('all-MiniLM-L6-v2') self.cache = {} self.threshold = similarity_threshold def get_cached_response(self, question): question_embedding = self.encoder.encode([question]) # Check similarity with cached questions # Return cached response if similarity > threshold ``` **Expected Impact**: 70-80% reduction for repeated question types #### 5. Connection Pool Optimization **Current Issue**: Database connection overhead per query **Solution**: Implement persistent connection pooling ```python # Enhanced connection pooling from sqlalchemy.pool import QueuePool engine = create_engine( connection_string, poolclass=QueuePool, pool_size=10, max_overflow=20, pool_pre_ping=True, pool_recycle=3600 ) ``` **Expected Impact**: 5-10% reduction in database operation time #### 6. Streaming Response Implementation **Current Issue**: Users wait for complete analysis before seeing any results **Solution**: Stream Stage 1 results immediately ```python # Streaming response architecture async def stream_multi_stage_response(question): yield {"stage": "classification", "result": classification} stage1_result = await execute_stage1() yield {"stage": "discovery", "result": stage1_result} stage2_result = await execute_stage2(stage1_result) yield {"stage": "analysis", "result": stage2_result} stage3_result = await execute_stage3(stage1_result, stage2_result) yield {"stage": "evaluation", "result": stage3_result} ``` **Expected Impact**: Improved perceived performance, better UX ### 📊 Performance Monitoring Enhancements #### 7. Advanced Performance Metrics **Implementation**: Enhanced logging with stage-level performance tracking ```python # Detailed performance tracking class PerformanceTracker: def __init__(self): self.stage_timings = {} self.sql_performance = {} self.llm_performance = {} def track_stage(self, stage_name): return StageTimer(stage_name, self) def log_comprehensive_metrics(self): # Log detailed breakdown for optimization analysis ``` #### 8. Performance Alerting **Implementation**: Real-time alerts for performance degradation ```python # Performance threshold monitoring PERFORMANCE_THRESHOLDS = { "total_execution": 60000, # 60 seconds "sql_generation": 10000, # 10 seconds "database_execution": 5000, # 5 seconds "stage3_evaluation": 15000 # 15 seconds } ``` ## Target Performance Goals ### Short-Term Targets (3 months) - **Total Execution**: 40.7s → 20s (51% improvement) - **Stage 1 Discovery**: 14.4s → 7s (51% improvement) - **Stage 2 Analysis**: 15.7s → 8s (49% improvement) - **Stage 3 Evaluation**: 7.1s → 5s (30% improvement) ### Long-Term Targets (6 months) - **Total Execution**: 40.7s → 12s (71% improvement) - **Cached Responses**: 40.7s → 3s (93% improvement) - **P95 Performance**: <15s for 95% of queries - **P99 Performance**: <25s for 99% of queries ## Implementation Roadmap ### Phase 1: Quick Wins (Week 1-2) 1. **Database Indexing**: Add performance indexes 2. **Connection Pooling**: Implement persistent connections 3. **Basic Caching**: Cache SQL patterns and LLM responses ### Phase 2: Parallel Processing (Week 3-4) 1. **Async Tool Execution**: Convert to async/await pattern 2. **Parallel Stage Operations**: Run independent operations concurrently 3. **Enhanced Monitoring**: Implement detailed performance tracking ### Phase 3: Advanced Optimizations (Week 5-8) 1. **Streaming Responses**: Real-time result delivery 2. **Semantic Caching**: AI-powered response caching 3. **Query Plan Optimization**: Advanced SQL optimization ### Phase 4: Production Tuning (Week 9-12) 1. **Load Testing**: Stress testing with concurrent users 2. **Performance Alerting**: Real-time monitoring and alerts 3. **Capacity Planning**: Scaling recommendations ## Monitoring and Validation ### Performance Benchmarks ```python # Automated performance benchmarks BENCHMARK_QUERIES = [ "Replace BD Luer-Lock Syringe 2.5mL with equivalent product", "Show me components in MRH-011C", "Analyze pricing for surgical kit components", "Find equivalent products for Terumo catheter lineup" ] async def run_performance_benchmarks(): for query in BENCHMARK_QUERIES: start_time = time.time() result = await execute_multi_stage_query(query) execution_time = time.time() - start_time log_benchmark_result(query, execution_time, result) ``` ### Success Metrics - **Execution Time**: Stage-by-stage timing analysis - **User Satisfaction**: Business user feedback on response time - **System Reliability**: Error rates and availability metrics - **Cost Efficiency**: Token usage and API cost per query ### Rollback Strategy - **Performance Regression Detection**: Automated alerts for degradation - **Version Control**: All optimizations tracked in feature branches - **A/B Testing**: Compare optimized vs baseline performance - **Gradual Rollout**: Phased deployment with monitoring This optimization roadmap provides a clear path to achieve significant performance improvements while maintaining system reliability and business value delivery.