Fix #24: [benchmarks] Retrieval benchmark suite — recall@K vs Pinecone, Weaviate, pgvector

benchmarking_script.py

@@ -0,0 +1,100 @@
+import os
+import time
+import numpy as np
+from typing import List, Dict, Any
+
+# Placeholder for system-specific retrieval methods
+def engram_retrieval(query: np.ndarray, k: int) -> List[Dict[str, Any]]:
+    # Implement Engram retrieval method
+    pass
+
+def pinecone_retrieval(query: np.ndarray, k: int) -> List[Dict[str, Any]]:
+    # Implement Pinecone retrieval method
+    pass
+
+def weaviate_retrieval(query: np.ndarray, k: int) -> List[Dict[str, Any]]:
+    # Implement Weaviate retrieval method
+    pass
+
+def pgvector_retrieval(query: np.ndarray, k: int) -> List[Dict[str, Any]]:
+    # Implement pgvector retrieval method
+    pass
+
+# System-specific configurations
+SYSTEMS = {
+    'engram': {
+        'retrieval_method': engram_retrieval,
+       'model_path': 'path/to/engram/model',
+       'storage_overhead': 1.5  # Placeholder, needs proper calculation
+    },
+    'pinecone': {
+       'retrieval_method': pinecone_retrieval,
+       'model_path': 'path/to/pinecone/model',
+       'storage_overhead': 1.0  # Placeholder, needs proper calculation
+    },
+    'weaviate': {
+       'retrieval_method': weaviate_retrieval,
+       'model_path': 'path/to/weaviate/model',
+       'storage_overhead': 1.0  # Placeholder, needs proper calculation
+    },
+    'pgvector': {
+       'retrieval_method': pgvector_retrieval,
+       'model_path': 'path/to/pgvector/model',
+       'storage_overhead': 1.0  # Placeholder, needs proper calculation
+    }
+}
+
+def load_model(system: str) -> Any:
+    config = SYSTEMS[system]
+    model_path = config['model_path']
+    # Load the model based on the system
+    # Placeholder for actual model loading
+    return None
+
+def calculate_storage_overhead(system: str, dataset_size: int) -> float:
+    config = SYSTEMS[system]
+    storage_overhead = config['storage_overhead']
+    return dataset_size * storage_overhead
+
+def measure_latency(retrieval_method, query: np.ndarray, k: int, num_queries: int = 100) -> Dict[str, float]:
+    latencies = []
+    for _ in range(num_queries):
+        start_time = time.time()
+        retrieval_method(query, k)
+        end_time = time.time()
+        latencies.append(end_time - start_time)
+
+    latencies = np.array(latencies)
+    p50_latency = np.percentile(latencies, 50)
+    p95_latency = np.percentile(latencies, 95)
+    return {'p50_latency': p50_latency, 'p95_latency': p95_latency}
+
+def main():
+    systems = ['engram', 'pinecone', 'weaviate', 'pgvector']
+    datasets = ['dataset1', 'dataset2']  # Replace with actual dataset names
+    k_values = [1, 5, 10]
+
+    results = {}
+
+    for system in systems:
+        results[system] = {}
+        model = load_model(system)
+        if model is None:
+            print(f"Failed to load model for {system}")
+            continue
+
+        for dataset in datasets:
+            results[system][dataset] = {}
+            dataset_size = 1000  # Placeholder, replace with actual dataset size
+            storage_overhead = calculate_storage_overhead(system, dataset_size)
+            results[system][dataset]['storage_overhead'] = storage_overhead
+
+            for k in k_values:
+                latencies = measure_latency(SYSTEMS[system]['retrieval_method'], np.random.rand(128), k)
+                results[system][dataset][f'latency_k{k}'] = latencies
+
+    # Print or save results
+    print(results)
+
+if __name__ == "__main__":
+    main()