From 88b8a7c75d1e45873397d1830df6dcf77c478f0b Mon Sep 17 00:00:00 2001
From: Google AI Agent <gemini@hermes.local>
Date: Mon, 30 Mar 2026 21:14:49 +0000
Subject: [PATCH] feat: add benchmarking script for quality assessment

---
 benchmarks/run_benchmarks.py | 75 ++++++++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 benchmarks/run_benchmarks.py

diff --git a/benchmarks/run_benchmarks.py b/benchmarks/run_benchmarks.py
new file mode 100644
index 0000000..45c8240
--- /dev/null
+++ b/benchmarks/run_benchmarks.py
@@ -0,0 +1,75 @@
+import json
+import time
+import requests
+import os
+from typing import List, Dict
+
+# ═══════════════════════════════════════════
+# TURBOQUANT BENCHMARKING SUITE (Issue #16)
+# ═══════════════════════════════════════════
+# This script runs a standardized set of prompts against the local inference 
+# engine (Ollama) and logs the results. This prevents cherry-picking and 
+# provides an objective baseline for quality comparisons.
+
+OLLAMA_URL = "http://localhost:11434/api/generate"
+PROMPTS_FILE = "benchmarks/prompts.json"
+RESULTS_FILE = f"benchmarks/results_{int(time.time())}.json"
+
+def run_benchmark(model: str = "llama3"):
+    """Run the benchmark suite for a specific model."""
+    if not os.path.exists(PROMPTS_FILE):
+        print(f"Error: {PROMPTS_FILE} not found.")
+        return
+
+    with open(PROMPTS_FILE, 'r') as f:
+        prompts = json.load(f)
+
+    results = []
+    print(f"Starting benchmark for model: {model}")
+    print(f"Saving results to: {RESULTS_FILE}")
+
+    for item in prompts:
+        print(f"Running prompt: {item['id']}...")
+        
+        start_time = time.time()
+        try:
+            response = requests.post(OLLAMA_URL, json={
+                "model": model,
+                "prompt": item['prompt'],
+                "stream": False
+            }, timeout=60)
+            
+            response.raise_for_status()
+            data = response.json()
+            end_time = time.time()
+            
+            results.append({
+                "id": item['id'],
+                "prompt": item['prompt'],
+                "response": data.get("response"),
+                "latency": end_time - start_time,
+                "tokens_per_second": data.get("eval_count", 0) / (data.get("eval_duration", 1) / 1e9) if data.get("eval_duration") else 0,
+                "status": "success"
+            })
+        except Exception as e:
+            print(f"Error running prompt {item['id']}: {e}")
+            results.append({
+                "id": item['id'],
+                "prompt": item['prompt'],
+                "error": str(e),
+                "status": "failed"
+            })
+
+    # Save results
+    with open(RESULTS_FILE, 'w') as f:
+        json.dump({
+            "model": model,
+            "timestamp": time.time(),
+            "results": results
+        }, f, indent=2)
+    
+    print("Benchmark complete.")
+
+if __name__ == "__main__":
+    # Default to llama3 for testing
+    run_benchmark("llama3")