report/finalreport.md

Performance Analysis Report

1. Retrieval Time:

   • Milvus + LLaMA: 0.132s
   • Weaviate + Mistral: 0.157s
   • Milvus + Mistral: NaN

2. Context Relevance (higher is better):

   • Milvus + LLaMA: 0.640
   • Weaviate + Mistral: 0.591
   • Milvus + Mistral: 0.518

3. Context Utilization (higher is better):

   • Milvus + LLaMA: 0.673
   • Weaviate + Mistral: 0.619
   • Milvus + Mistral: 0.614

4. AUCROC (Area Under ROC Curve):

   • Milvus + LLaMA: 0.912
   • Weaviate + Mistral: 0.750
   • Milvus + Mistral: 0.844

5. RMSE (Root Mean Square Error):

   • Milvus + LLaMA:
     • Context Relevance RMSE: 0.179
     • Context Utilization RMSE: 0.302
   • Weaviate + Mistral:
     • Context Relevance RMSE: 0.414
     • Context Utilization RMSE: 0.482
   • Milvus + Mistral:
     • Context Relevance RMSE: 0.167
     • Context Utilization RMSE: 0.258

Analysis

1. Best Overall Performance: Milvus + LLaMA

   • Highest AUCROC score (0.912)
   • Best context relevance (0.640) and utilization (0.673)
   • Fast retrieval time (0.132s)
   • Moderate RMSE scores

2. Runner-up: Milvus + Mistral

   • Second-best AUCROC (0.844)
   • Lowest RMSE scores overall
   • Lower context relevance and utilization
   • Retrieval time data unavailable

3. Third Place: Weaviate + Mistral

   • Lowest AUCROC (0.750)
   • Highest RMSE scores
   • Slowest retrieval time (0.157s)
   • Moderate context metrics

Recommendation

Based on the comprehensive analysis of all metrics, Milvus + LLaMA emerges as the optimal choice for overall performance. It demonstrates:

• Superior accuracy (highest AUCROC)
• Better context handling capabilities
• Efficient retrieval speed
• Reasonable error rates

However, if minimizing error (RMSE) is the primary objective, Milvus + Mistral could be a viable alternative due to its lower error rates in both context relevance and utilization metrics.