Merge pull request #1093 from Yash-2707/patch-2

Update test_prompt_benchmark_test.py

tests/models/test_prompt_benchmark_test.py

@@ -1,35 +1,34 @@
-
 """This runs a benchmark test dataset against a series of prompts.  It can be used to test any model type for
-    longer running series of prompts, as well as the fact-checking capability.
-
-    This test uses the RAG Benchmark test set, which can be pulled down from the LLMWare repository on
-    Huggingface at: www.huggingface.co/llmware/rag_instruct_benchmark_tester, or by using the
-    datasets library, which can be installed with:
+longer running series of prompts, as well as the fact-checking capability.
 
-     `pip3 install datasets`
- """
+This test uses the RAG Benchmark test set, which can be pulled down from the LLMWare repository on
+Huggingface at: www.huggingface.co/llmware/rag_instruct_benchmark_tester, or by using the
+datasets library, which can be installed with:
 
+ `pip3 install datasets`
+"""
 
 import time
 import random
-
+import logging
+import numpy as np
+import matplotlib.pyplot as plt
 from llmware.prompts import Prompt
 
 # The datasets package is not installed automatically by llmware
 try:
     from datasets import load_dataset
 except ImportError:
-    raise ImportError ("This test requires the 'datasets' Python package. "
-                       "You can install it with 'pip3 install datasets'")
-
+    raise ImportError("This test requires the 'datasets' Python package. "
+                      "You can install it with 'pip3 install datasets'")
 
+# Set up logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 def load_rag_benchmark_tester_dataset():
-
-    """ Loads benchmark dataset used in the prompt test. """
-
+    """Loads benchmark dataset used in the prompt test."""
     dataset_name = "llmware/rag_instruct_benchmark_tester"
-    print(f"\n > Loading RAG dataset '{dataset_name}'...")
+    logging.info(f"Loading RAG dataset '{dataset_name}'...")
     dataset = load_dataset(dataset_name)
 
     test_set = []
@@ -38,61 +37,92 @@ def load_rag_benchmark_tester_dataset():
 
     return test_set
 
+def load_models(models):
+    """Load a list of models dynamically."""
+    for model in models:
+        try:
+            logging.info(f"Loading model '{model}'")
+            yield Prompt().load_model(model)
+        except Exception as e:
+            logging.error(f"Failed to load model '{model}': {e}")
 
-# Run the benchmark test
-def test_prompt_rag_benchmark():
-
+def test_prompt_rag_benchmark(selected_test_models):
     test_dataset = load_rag_benchmark_tester_dataset()
 
-    # SELECTED MODELS
-
-    selected_test_models = ["llmware/bling-1b-0.1", "llmware/bling-1.4b-0.1",  "llmware/bling-falcon-1b-0.1",
-                            "llmware/bling-tiny-llama-v0",
-                            "bling-phi-3-gguf", "bling-answer-tool", "dragon-yi-answer-tool",
-                            "dragon-llama-answer-tool", "dragon-mistral-answer-tool"]
-
-    # randomly select one model from the list
-    r = random.randint(0,len(selected_test_models)-1)
-
+    # Randomly select one model from the list
+    r = random.randint(0, len(selected_test_models) - 1)
     model_name = selected_test_models[r]
 
-    print(f"\n > Loading model '{model_name}'")
-    prompter = Prompt().load_model(model_name)
+    logging.info(f"Selected model: {model_name}")
+    prompter = next(load_models([model_name]))
 
-    print(f"\n > Running RAG Benchmark Test against '{model_name}' - 200 questions")
+    logging.info(f"Running RAG Benchmark Test against '{model_name}' - 200 questions")
+    results = []
     for i, entry in enumerate(test_dataset):
-
-        start_time = time.time()
-
-        prompt = entry["query"]
-        context = entry["context"]
-        response = prompter.prompt_main(prompt, context=context, prompt_name="default_with_context", temperature=0.3)
-
-        assert response is not None
-
-        # Print results
-        time_taken = round(time.time() - start_time, 2)
-        print("\n")
-        print(f"{i + 1}. llm_response - {response['llm_response']}")
-        print(f"{i + 1}. gold_answer - {entry['answer']}")
-        print(f"{i + 1}. time_taken - {time_taken}")
-
-        # Fact checking
-        fc = prompter.evidence_check_numbers(response)
-        sc = prompter.evidence_comparison_stats(response)
-        sr = prompter.evidence_check_sources(response)
-
-        for fc_entry in fc:
-            for f, facts in enumerate(fc_entry["fact_check"]):
-                print(f"{i + 1}. fact_check - {f} {facts}")
-
-        for sc_entry in sc:
-            print(f"{i + 1}. comparison_stats - {sc_entry['comparison_stats']}")
-
-        for sr_entry in sr:
-            for s, source in enumerate(sr_entry["source_review"]):
-                print(f"{i + 1}. source - {s} {source}")
-
-    return 0
-
-
+        try:
+            start_time = time.time()
+
+            prompt = entry["query"]
+            context = entry["context"]
+            response = prompter.prompt_main(prompt, context=context, prompt_name="default_with_context", temperature=0.3)
+
+            assert response is not None
+
+            # Print results
+            time_taken = round(time.time() - start_time, 2)
+            logging.info(f"{i + 1}. llm_response - {response['llm_response']}")
+            logging.info(f"{i + 1}. gold_answer - {entry['answer']}")
+            logging.info(f"{i + 1}. time_taken - {time_taken}")
+
+            # Fact checking
+            fc = prompter.evidence_check_numbers(response)
+            sc = prompter.evidence_comparison_stats(response)
+            sr = prompter.evidence_check_sources(response)
+
+            for fc_entry in fc:
+                for f, facts in enumerate(fc_entry["fact_check"]):
+                    logging.info(f"{i + 1}. fact_check - {f} {facts}")
+
+            for sc_entry in sc:
+                logging.info(f"{i + 1}. comparison_stats - {sc_entry['comparison_stats']}")
+
+            for sr_entry in sr:
+                for s, source in enumerate(sr_entry["source_review"]):
+                    logging.info(f"{i + 1}. source - {s} {source}")
+
+            results.append({
+                "llm_response": response["llm_response"],
+                "gold_answer": entry["answer"],
+                "time_taken": time_taken,
+                "fact_check": fc,
+                "comparison_stats": sc,
+                "source_review": sr
+            })
+
+        except Exception as e:
+            logging.error(f"Error processing entry {i}: {e}")
+
+    # Performance metrics
+    total_time = sum(result["time_taken"] for result in results)
+    average_time = total_time / len(results) if results else 0
+    logging.info(f"Total time taken: {total_time} seconds")
+    logging.info(f"Average time per question: {average_time} seconds")
+
+    # Visualization
+    time_taken_list = [result["time_taken"] for result in results]
+    plt.plot(range(1, len(time_taken_list) + 1), time_taken_list, marker='o')
+    plt.xlabel('Question Number')
+    plt.ylabel('Time Taken (seconds)')
+    plt.title('Time Taken per Question')
+    plt.show()
+
+    return results
+
+# Example usage
+if __name__ == "__main__":
+    selected_test_models = [
+        "llmware/bling-1b-0.1", "llmware/bling-1.4b-0.1", "llmware/bling-falcon-1b-0.1",
+        "llmware/bling-tiny-llama-v0", "bling-phi-3-gguf", "bling-answer-tool",
+        "dragon-yi-answer-tool", "dragon-llama-answer-tool", "dragon-mistral-answer-tool"
+    ]
+    test_prompt_rag_benchmark(selected_test_models)