{
  "project": "LitKit Semantic Search & RAG Pipeline",
  "testRunId": "litkit-run-104b",
  "date": "2023-11-20",
  "environment": {
    "vectorDatabase": "ChromaDB v0.4.16",
    "embeddingModel": "text-embedding-ada-002",
    "concurrency": 250,
    "totalQueries": 5000
  },
  "metrics": {
    "totalDocumentsPages": 10245,
    "vectorChunksCount": 32140,
    "retrievalLatency": {
      "preOptimizationAvgMs": 1200,
      "postOptimizationAvgMs": 180,
      "p95LatencyMs": 204,
      "p99LatencyMs": 235
    },
    "retrievalRelevance": {
      "contextRelevanceScore": 0.94,
      "cosineSimilarityAverage": 0.88,
      "groundednessScore": 0.96
    }
  },
  "optimizationsApplied": [
    {
      "technique": "Semantic Chunking",
      "description": "Switched from character-length chunking to semantic sentence splitters with overlapping sections (500 tokens chunk, 50 tokens overlap)."
    },
    {
      "technique": "Embedding Pre-Caching",
      "description": "Pre-computed embeddings for frequently queried academic texts, storing vector maps in Redis."
    },
    {
      "technique": "ChromaDB Index Tuning",
      "description": "Optimized HNSW graph construction parameters (M=16, ef_construction=64) to speed up vector lookup."
    }
  ]
}