Squashed 'vendor/ruvector/' content from commit b64c2172

git-subtree-dir: vendor/ruvector
git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900

npm/packages/agentic-synth-examples/tests/dspy/benchmark.test.ts

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+/**
+ * Tests for Multi-Model Benchmarking
+ */
+
+import { describe, it, expect, beforeEach } from 'vitest';
+import { MultiModelBenchmark } from '../../src/dspy/benchmark.js';
+import { ModelProvider } from '../../src/types/index.js';
+import type { BenchmarkConfig } from '../../src/dspy/benchmark.js';
+
+describe('MultiModelBenchmark', () => {
+  let config: BenchmarkConfig;
+
+  beforeEach(() => {
+    config = {
+      models: [
+        {
+          provider: ModelProvider.GEMINI,
+          model: 'gemini-2.0-flash-exp',
+          apiKey: 'test-key-1'
+        },
+        {
+          provider: ModelProvider.CLAUDE,
+          model: 'claude-sonnet-4',
+          apiKey: 'test-key-2'
+        }
+      ],
+      tasks: ['code-generation', 'text-summarization'],
+      iterations: 3
+    };
+  });
+
+  describe('Initialization', () => {
+    it('should create benchmark with valid config', () => {
+      const benchmark = new MultiModelBenchmark(config);
+      expect(benchmark).toBeDefined();
+    });
+
+    it('should accept timeout option', () => {
+      const benchmarkWithTimeout = new MultiModelBenchmark({
+        ...config,
+        timeout: 5000
+      });
+      expect(benchmarkWithTimeout).toBeDefined();
+    });
+  });
+
+  describe('Benchmark Execution', () => {
+    it('should run complete benchmark and return results', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      expect(result.results).toBeDefined();
+      expect(result.results.length).toBeGreaterThan(0);
+      expect(result.bestModel).toBeDefined();
+      expect(result.bestProvider).toBeDefined();
+      expect(result.summary).toBeDefined();
+    });
+
+    it('should test all model and task combinations', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      // 2 models × 2 tasks × 3 iterations = 12 results
+      expect(result.results.length).toBe(12);
+
+      // Verify all tasks are covered
+      const tasks = new Set(result.results.map(r => r.task));
+      expect(tasks.size).toBe(2);
+      expect(tasks.has('code-generation')).toBe(true);
+      expect(tasks.has('text-summarization')).toBe(true);
+
+      // Verify all models are covered
+      const providers = new Set(result.results.map(r => r.provider));
+      expect(providers.size).toBe(2);
+    });
+
+    it('should run multiple iterations per task', async () => {
+      const benchmark = new MultiModelBenchmark({
+        ...config,
+        iterations: 5
+      });
+      const result = await benchmark.run();
+
+      // 2 models × 2 tasks × 5 iterations = 20 results
+      expect(result.results.length).toBe(20);
+    });
+  });
+
+  describe('Performance Metrics', () => {
+    it('should track latency for each test', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      result.results.forEach(r => {
+        expect(r.latency).toBeGreaterThan(0);
+        expect(r.latency).toBeLessThan(2000); // Reasonable latency limit
+      });
+    });
+
+    it('should track cost for each test', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      result.results.forEach(r => {
+        expect(r.cost).toBeGreaterThanOrEqual(0);
+      });
+
+      expect(result.summary.totalCost).toBeGreaterThan(0);
+    });
+
+    it('should track tokens used', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      result.results.forEach(r => {
+        expect(r.tokensUsed).toBeGreaterThanOrEqual(0);
+      });
+    });
+
+    it('should calculate quality scores', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      result.results.forEach(r => {
+        expect(r.score).toBeGreaterThanOrEqual(0);
+        expect(r.score).toBeLessThanOrEqual(1);
+      });
+    });
+  });
+
+  describe('Result Aggregation', () => {
+    it('should generate summary statistics', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      expect(result.summary.totalTests).toBe(12);
+      expect(result.summary.avgScore).toBeGreaterThan(0);
+      expect(result.summary.avgLatency).toBeGreaterThan(0);
+      expect(result.summary.totalCost).toBeGreaterThan(0);
+      expect(result.summary.successRate).toBeGreaterThan(0);
+      expect(result.summary.successRate).toBeLessThanOrEqual(1);
+    });
+
+    it('should include model comparison in summary', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      expect(result.summary.modelComparison).toBeDefined();
+      expect(Array.isArray(result.summary.modelComparison)).toBe(true);
+      expect(result.summary.modelComparison.length).toBe(2); // 2 models
+
+      result.summary.modelComparison.forEach((comparison: any) => {
+        expect(comparison.model).toBeDefined();
+        expect(comparison.avgScore).toBeDefined();
+        expect(comparison.minScore).toBeDefined();
+        expect(comparison.maxScore).toBeDefined();
+      });
+    });
+
+    it('should identify best performing model', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      expect(result.bestModel).toBeDefined();
+      expect(result.bestProvider).toBeDefined();
+      expect([ModelProvider.GEMINI, ModelProvider.CLAUDE]).toContain(result.bestProvider);
+
+      // Verify the best model actually performed best
+      const bestModelResults = result.results.filter(
+        r => r.model === result.bestModel && r.provider === result.bestProvider
+      );
+      const avgBestScore = bestModelResults.reduce((sum, r) => sum + r.score, 0) / bestModelResults.length;
+
+      // Best model should have above-average score
+      expect(avgBestScore).toBeGreaterThanOrEqual(result.summary.avgScore * 0.9);
+    });
+  });
+
+  describe('Model Comparison', () => {
+    it('should directly compare two models', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.compare(
+        config.models[0],
+        config.models[1],
+        'code-generation'
+      );
+
+      expect(result.winner).toBeDefined();
+      expect([ModelProvider.GEMINI, ModelProvider.CLAUDE]).toContain(result.winner);
+      expect(result.model1Results.length).toBe(3); // 3 iterations
+      expect(result.model2Results.length).toBe(3);
+      expect(result.comparison).toBeDefined();
+      expect(result.comparison.scoreImprovement).toBeGreaterThanOrEqual(0);
+    });
+
+    it('should calculate score improvement in comparison', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.compare(
+        config.models[0],
+        config.models[1],
+        'text-summarization'
+      );
+
+      expect(result.comparison.model1Avg).toBeGreaterThan(0);
+      expect(result.comparison.model2Avg).toBeGreaterThan(0);
+      expect(typeof result.comparison.scoreImprovement).toBe('number');
+    });
+  });
+
+  describe('Error Handling', () => {
+    it('should handle API failures gracefully', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      // Some tests might fail (simulated 5% failure rate)
+      const failedTests = result.results.filter(r => r.score === 0);
+      const successRate = result.summary.successRate;
+
+      expect(successRate).toBeGreaterThan(0.8); // At least 80% success
+      expect(successRate).toBeLessThanOrEqual(1.0);
+    });
+
+    it('should continue after individual test failures', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      // Should complete all tests even if some fail
+      expect(result.results.length).toBe(12);
+    });
+
+    it('should handle timeout scenarios', async () => {
+      const benchmark = new MultiModelBenchmark({
+        ...config,
+        timeout: 100 // Very short timeout
+      });
+
+      const result = await benchmark.run();
+      expect(result.results).toBeDefined();
+      // Tests should complete or fail, but not hang
+    });
+  });
+
+  describe('Task Variations', () => {
+    it('should handle single task benchmark', async () => {
+      const benchmark = new MultiModelBenchmark({
+        ...config,
+        tasks: ['code-generation']
+      });
+      const result = await benchmark.run();
+
+      expect(result.results.length).toBe(6); // 2 models × 1 task × 3 iterations
+      expect(result.results.every(r => r.task === 'code-generation')).toBe(true);
+    });
+
+    it('should handle multiple task types', async () => {
+      const benchmark = new MultiModelBenchmark({
+        ...config,
+        tasks: ['code-generation', 'text-summarization', 'data-analysis', 'creative-writing']
+      });
+      const result = await benchmark.run();
+
+      // 2 models × 4 tasks × 3 iterations = 24 results
+      expect(result.results.length).toBe(24);
+
+      const tasks = new Set(result.results.map(r => r.task));
+      expect(tasks.size).toBe(4);
+    });
+  });
+
+  describe('Model Variations', () => {
+    it('should handle single model benchmark', async () => {
+      const benchmark = new MultiModelBenchmark({
+        ...config,
+        models: [config.models[0]]
+      });
+      const result = await benchmark.run();
+
+      expect(result.results.length).toBe(6); // 1 model × 2 tasks × 3 iterations
+      expect(result.results.every(r => r.provider === ModelProvider.GEMINI)).toBe(true);
+    });
+
+    it('should handle three or more models', async () => {
+      const benchmark = new MultiModelBenchmark({
+        ...config,
+        models: [
+          ...config.models,
+          {
+            provider: ModelProvider.GPT4,
+            model: 'gpt-4-turbo',
+            apiKey: 'test-key-3'
+          }
+        ]
+      });
+      const result = await benchmark.run();
+
+      // 3 models × 2 tasks × 3 iterations = 18 results
+      expect(result.results.length).toBe(18);
+
+      const providers = new Set(result.results.map(r => r.provider));
+      expect(providers.size).toBe(3);
+    });
+  });
+
+  describe('Performance Analysis', () => {
+    it('should track consistency across iterations', async () => {
+      const benchmark = new MultiModelBenchmark({
+        ...config,
+        iterations: 10 // More iterations for consistency check
+      });
+      const result = await benchmark.run();
+
+      // Group results by model and task
+      const groupedResults = result.results.reduce((acc, r) => {
+        const key = `${r.provider}:${r.task}`;
+        if (!acc[key]) acc[key] = [];
+        acc[key].push(r.score);
+        return acc;
+      }, {} as Record<string, number[]>);
+
+      // Check variance isn't too high (scores should be relatively consistent)
+      Object.values(groupedResults).forEach(scores => {
+        const mean = scores.reduce((a, b) => a + b, 0) / scores.length;
+        const variance = scores.reduce((sum, score) => sum + Math.pow(score - mean, 2), 0) / scores.length;
+        const stdDev = Math.sqrt(variance);
+
+        // Standard deviation should be reasonable (not random)
+        expect(stdDev).toBeLessThan(0.3);
+      });
+    });
+
+    it('should identify performance patterns', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      // Verify we can identify which model is better for which task
+      const taskPerformance = result.results.reduce((acc, r) => {
+        if (!acc[r.task]) acc[r.task] = {};
+        if (!acc[r.task][r.provider]) acc[r.task][r.provider] = [];
+        acc[r.task][r.provider].push(r.score);
+        return acc;
+      }, {} as Record<string, Record<string, number[]>>);
+
+      // Each task should have results from both models
+      Object.keys(taskPerformance).forEach(task => {
+        expect(Object.keys(taskPerformance[task]).length).toBe(2);
+      });
+    });
+  });
+
+  describe('Cost Analysis', () => {
+    it('should calculate total cost accurately', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      const manualTotal = result.results.reduce((sum, r) => sum + r.cost, 0);
+      expect(result.summary.totalCost).toBeCloseTo(manualTotal, 2);
+    });
+
+    it('should track cost per model', async () => {
+      const benchmark = new MultiModelBenchmark(config);
+      const result = await benchmark.run();
+
+      const costByModel = result.results.reduce((acc, r) => {
+        const key = `${r.provider}:${r.model}`;
+        acc[key] = (acc[key] || 0) + r.cost;
+        return acc;
+      }, {} as Record<string, number>);
+
+      // Both models should have incurred costs
+      expect(Object.keys(costByModel).length).toBe(2);
+      Object.values(costByModel).forEach(cost => {
+        expect(cost).toBeGreaterThan(0);
+      });
+    });
+  });
+});