Add pre-commit setup documentation

PRE_COMMIT_SETUP.md

@@ -0,0 +1,76 @@
+# Pre-Commit Hook Setup
+
+This repository uses [pre-commit](https://pre-commit.com/) to enforce code formatting and secret scanning before each commit.
+
+## Prerequisites
+
+- Python 3.x
+- `pip`
+- For C/C++ formatting: `clang-format` (optional — pre-commit will install it via the mirror hook)
+
+## Installation
+
+1. **Install pre-commit:**
+
+   ```bash
+   pip install pre-commit
+   ```
+
+2. **Install the hooks into your local git repo:**
+
+   ```bash
+   pre-commit install
+   ```
+
+   This adds a `.git/hooks/pre-commit` script that runs automatically on every `git commit`.
+
+## Hooks Configured
+
+| Hook | What it does |
+|------|-------------|
+| `autopep8` | Auto-formats Python files to PEP 8 style (max line length 79). Excludes `tools/submission/power/power_checker.py`. |
+| `clang-format` | Auto-formats C and C++ files using the repo's `.clang-format` style file. |
+| `gitleaks` | Scans for hardcoded secrets, API keys, and credentials before they are committed. |
+
+## Usage
+
+Once installed, hooks run automatically on `git commit`. If a hook modifies files (e.g., autopep8 reformats a Python file), the commit is aborted so you can review the changes, then re-stage and commit:
+
+```bash
+git add <reformatted files>
+git commit -m "your message"
+```
+
+## Running Hooks Manually
+
+Run all hooks against all files:
+
+```bash
+pre-commit run --all-files
+```
+
+Run a specific hook:
+
+```bash
+pre-commit run autopep8
+pre-commit run clang-format
+pre-commit run gitleaks
+```
+
+## Skipping Hooks (Not Recommended)
+
+In exceptional cases you can bypass hooks with:
+
+```bash
+git commit --no-verify -m "your message"
+```
+
+Use this sparingly — the hooks exist to keep the codebase consistent and secrets-free.
+
+## Updating Hooks
+
+To update hook versions to the latest revisions specified in `.pre-commit-config.yaml`:
+
+```bash
+pre-commit autoupdate
+```

e2e-rag/QSL.py

@@ -85,7 +85,8 @@ def __init__(self, dataset_path, perf_count=None, skip_qsl=False):
 
         print(f"Dataset loaded: {self.count} queries")
         if perf_count is not None:
-            print(f"  (limited to first {perf_count} queries for performance testing)")
+            print(
+                f"  (limited to first {perf_count} queries for performance testing)")
 
     def load_query_samples(self, sample_list):
         """
@@ -161,15 +162,12 @@ def __init__(self, dataset_path, perf_count=None):
 # limitations under the License.
 # =============================================================================
 
+
 """
 Query Sample Library for RAG-QnA workload.
 Loads queries from frames_dataset.tsv and provides them to MLPerf Loadgen.
 """
 
-import os
-import pandas as pd
-import mlperf_loadgen as lg
-
 
 class E2EQSL:
     """Query Sample Library for RAG-QnA multi-hop RAG benchmark."""
@@ -233,7 +231,8 @@ def __init__(self, dataset_path, perf_count=None, skip_qsl=False):
 
         print(f"Dataset loaded: {self.count} queries")
         if perf_count is not None:
-            print(f"  (limited to first {perf_count} queries for performance testing)")
+            print(
+                f"  (limited to first {perf_count} queries for performance testing)")
 
     def load_query_samples(self, sample_list):
         """

e2e-rag/accuracy_eval.py

@@ -34,9 +34,10 @@
 # OpenRouter configuration
 DEFAULT_JUDGE_URL = "http://127.0.0.1:8123/v1/chat/completions"
 DEFAULT_JUDGE_MODEL = "gpt-oss-20b"
-# Masked API key (set OPENROUTER_API_KEY environment variable to use OpenRouter)
+# Masked API key (set OPENROUTER_API_KEY environment variable to use
+# OpenRouter)
 OPENROUTER_API_KEY = os.environ.get('OPENROUTER_API_KEY',
-    'sk-or-v1-****')
+                                    'sk-or-v1-****')
 
 
 JUDGE_PROMPT = """You are an expert evaluator comparing LLM-generated answers to ground truth answers.
@@ -83,7 +84,11 @@ def call_judge(question: str, ground_truth: str, llm_answer: str,
     }
 
     try:
-        response = requests.post(service_url, json=payload, headers=headers, timeout=60)
+        response = requests.post(
+            service_url,
+            json=payload,
+            headers=headers,
+            timeout=60)
         response.raise_for_status()
         result = response.json()
 
@@ -105,7 +110,8 @@ def call_judge(question: str, ground_truth: str, llm_answer: str,
         return {"correct": False, "reasoning": f"Judge error: {e}"}
 
 
-def calculate_retrieval_metrics(retrieved_urls: List[str], expected_urls: List[str]) -> Dict:
+def calculate_retrieval_metrics(
+        retrieved_urls: List[str], expected_urls: List[str]) -> Dict:
     """Calculate precision, recall, F1 for retrieval."""
 
     retrieved_set = set(retrieved_urls)
@@ -118,7 +124,8 @@ def calculate_retrieval_metrics(retrieved_urls: List[str], expected_urls: List[s
 
     precision = len(correct) / len(retrieved_set) if retrieved_set else 0.0
     recall = len(correct) / len(expected_set) if expected_set else 0.0
-    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
+    f1 = 2 * precision * recall / \
+        (precision + recall) if (precision + recall) > 0 else 0.0
 
     return {
         "precision": precision,
@@ -128,8 +135,8 @@ def calculate_retrieval_metrics(retrieved_urls: List[str], expected_urls: List[s
 
 
 def evaluate_results(results: Dict, dataset_path: str, num_workers: int = 4,
-                    judge_service_url: str = DEFAULT_JUDGE_URL,
-                    judge_model: str = DEFAULT_JUDGE_MODEL) -> Dict:
+                     judge_service_url: str = DEFAULT_JUDGE_URL,
+                     judge_model: str = DEFAULT_JUDGE_MODEL) -> Dict:
     """
     Evaluate loadgen results.
 
@@ -190,12 +197,13 @@ def evaluate_single_query(query_id, result):
         expected_urls = gt_data['expected_urls']
 
         # Calculate retrieval metrics
-        retrieval_metrics = calculate_retrieval_metrics(retrieved_urls, expected_urls)
+        retrieval_metrics = calculate_retrieval_metrics(
+            retrieved_urls, expected_urls)
 
         # Judge answer correctness
         judge_result = call_judge(query, ground_truth, llm_answer,
-                                 service_url=judge_service_url,
-                                 model_name=judge_model)
+                                  service_url=judge_service_url,
+                                  model_name=judge_model)
         answer_correct = judge_result.get('correct', False)
 
         return {
@@ -229,7 +237,8 @@ def evaluate_single_query(query_id, result):
                     total_queries += 1
 
                     if total_queries % 10 == 0:
-                        print(f"  Evaluated {total_queries}/{len(results)} queries...")
+                        print(
+                            f"  Evaluated {total_queries}/{len(results)} queries...")
             except Exception as e:
                 print(f"Error evaluating query: {e}")
 
@@ -257,14 +266,37 @@ def evaluate_single_query(query_id, result):
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Evaluate RAG-QnA loadgen accuracy")
-    parser.add_argument('--log_dir', required=True, help='Loadgen log directory')
-    parser.add_argument('--results_file', required=True, help='SUT results JSON file')
-    parser.add_argument('--dataset_path', required=True, help='Path to frames_dataset.tsv')
-    parser.add_argument('--num_workers', type=int, default=4, help='Number of parallel judge workers')
-    parser.add_argument('--output', default='accuracy_results.json', help='Output file for detailed results')
-    parser.add_argument('--judge_service_url', default=DEFAULT_JUDGE_URL, help='Judge LLM service URL')
-    parser.add_argument('--judge_model', default=DEFAULT_JUDGE_MODEL, help='Judge LLM model name')
+    parser = argparse.ArgumentParser(
+        description="Evaluate RAG-QnA loadgen accuracy")
+    parser.add_argument(
+        '--log_dir',
+        required=True,
+        help='Loadgen log directory')
+    parser.add_argument(
+        '--results_file',
+        required=True,
+        help='SUT results JSON file')
+    parser.add_argument(
+        '--dataset_path',
+        required=True,
+        help='Path to frames_dataset.tsv')
+    parser.add_argument(
+        '--num_workers',
+        type=int,
+        default=4,
+        help='Number of parallel judge workers')
+    parser.add_argument(
+        '--output',
+        default='accuracy_results.json',
+        help='Output file for detailed results')
+    parser.add_argument(
+        '--judge_service_url',
+        default=DEFAULT_JUDGE_URL,
+        help='Judge LLM service URL')
+    parser.add_argument(
+        '--judge_model',
+        default=DEFAULT_JUDGE_MODEL,
+        help='Judge LLM model name')
     args = parser.parse_args()
 
     # Load results
@@ -280,17 +312,17 @@ def main():
                                judge_model=args.judge_model)
 
     # Print summary
-    print("\n" + "="*80)
+    print("\n" + "=" * 80)
     print("ACCURACY EVALUATION RESULTS")
-    print("="*80)
+    print("=" * 80)
     print(f"Total Queries:        {metrics['total_queries']}")
     print(f"\nRetrieval Metrics:")
     print(f"  Precision@N:        {metrics['retrieval_precision']:.3f}")
     print(f"  Recall@N:           {metrics['retrieval_recall']:.3f}")
     print(f"  F1@N:               {metrics['retrieval_f1']:.3f}")
     print(f"\nAnswer Quality:")
     print(f"  LLM Judge Accuracy: {metrics['answer_accuracy']:.3f}")
-    print("="*80 + "\n")
+    print("=" * 80 + "\n")
 
     # Save detailed results
     with open(args.output, 'w') as f: