fix: resolve all ruff linting errors and add lint CI check
- Fix ambiguous fullwidth characters (commas, parentheses) in strings and comments - Replace Chinese comments with English equivalents - Fix unused imports with proper noqa annotations for intentional imports - Fix bare except clauses with specific exception types - Fix redefined variables and undefined names - Add ruff noqa annotations for generated protobuf files - Add lint and format check to GitHub Actions CI pipeline
This commit is contained in:
Andy Lee
@@ -5,24 +5,21 @@ It correctly compares results by fetching the text content for both the new sear
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results and the golden standard results, making the comparison robust to ID changes.
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"""
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import json
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import argparse
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import json
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import sys
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import time
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from pathlib import Path
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import sys
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import numpy as np
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from typing import List
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from leann.api import LeannSearcher, LeannBuilder
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import numpy as np
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from leann.api import LeannBuilder, LeannSearcher
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def download_data_if_needed(data_root: Path, download_embeddings: bool = False):
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"""Checks if the data directory exists, and if not, downloads it from HF Hub."""
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if not data_root.exists():
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print(f"Data directory '{data_root}' not found.")
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print(
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"Downloading evaluation data from Hugging Face Hub... (this may take a moment)"
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)
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print("Downloading evaluation data from Hugging Face Hub... (this may take a moment)")
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try:
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from huggingface_hub import snapshot_download
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@@ -63,7 +60,7 @@ def download_data_if_needed(data_root: Path, download_embeddings: bool = False):
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sys.exit(1)
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def download_embeddings_if_needed(data_root: Path, dataset_type: str = None):
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def download_embeddings_if_needed(data_root: Path, dataset_type: str | None = None):
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"""Download embeddings files specifically."""
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embeddings_dir = data_root / "embeddings"
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@@ -101,7 +98,7 @@ def download_embeddings_if_needed(data_root: Path, dataset_type: str = None):
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# --- Helper Function to get Golden Passages ---
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def get_golden_texts(searcher: LeannSearcher, golden_ids: List[int]) -> set:
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def get_golden_texts(searcher: LeannSearcher, golden_ids: list[int]) -> set:
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"""
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Retrieves the text for golden passage IDs directly from the LeannSearcher's
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passage manager.
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@@ -113,24 +110,20 @@ def get_golden_texts(searcher: LeannSearcher, golden_ids: List[int]) -> set:
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passage_data = searcher.passage_manager.get_passage(str(gid))
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golden_texts.add(passage_data["text"])
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except KeyError:
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print(
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f"Warning: Golden passage ID '{gid}' not found in the index's passage data."
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)
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print(f"Warning: Golden passage ID '{gid}' not found in the index's passage data.")
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return golden_texts
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def load_queries(file_path: Path) -> List[str]:
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def load_queries(file_path: Path) -> list[str]:
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queries = []
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with open(file_path, "r", encoding="utf-8") as f:
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with open(file_path, encoding="utf-8") as f:
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for line in f:
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data = json.loads(line)
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queries.append(data["query"])
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return queries
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def build_index_from_embeddings(
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embeddings_file: str, output_path: str, backend: str = "hnsw"
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):
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def build_index_from_embeddings(embeddings_file: str, output_path: str, backend: str = "hnsw"):
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"""
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Build a LEANN index from pre-computed embeddings.
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@@ -173,9 +166,7 @@ def build_index_from_embeddings(
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def main():
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parser = argparse.ArgumentParser(
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description="Run recall evaluation on a LEANN index."
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)
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parser = argparse.ArgumentParser(description="Run recall evaluation on a LEANN index.")
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parser.add_argument(
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"index_path",
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type=str,
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@@ -202,9 +193,7 @@ def main():
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parser.add_argument(
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"--num-queries", type=int, default=10, help="Number of queries to evaluate."
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)
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parser.add_argument(
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"--top-k", type=int, default=3, help="The 'k' value for recall@k."
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)
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parser.add_argument("--top-k", type=int, default=3, help="The 'k' value for recall@k.")
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parser.add_argument(
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"--ef-search", type=int, default=120, help="The 'efSearch' parameter for HNSW."
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)
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@@ -219,9 +208,7 @@ def main():
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# Download data based on mode
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if args.mode == "build":
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# For building mode, we need embeddings
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download_data_if_needed(
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data_root, download_embeddings=False
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) # Basic data first
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download_data_if_needed(data_root, download_embeddings=False) # Basic data first
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# Auto-detect dataset type and download embeddings
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if args.embeddings_file:
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@@ -262,9 +249,7 @@ def main():
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print(f"Index built successfully: {built_index_path}")
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# Ask if user wants to run evaluation
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eval_response = (
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input("Run evaluation on the built index? (y/n): ").strip().lower()
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)
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eval_response = input("Run evaluation on the built index? (y/n): ").strip().lower()
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if eval_response != "y":
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print("Index building complete. Exiting.")
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return
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@@ -293,12 +278,8 @@ def main():
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break
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if not args.index_path:
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print(
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"No indices found. The data download should have included pre-built indices."
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)
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print(
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"Please check the data/indices/ directory or provide --index-path manually."
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)
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print("No indices found. The data download should have included pre-built indices.")
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print("Please check the data/indices/ directory or provide --index-path manually.")
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sys.exit(1)
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# Detect dataset type from index path to select the correct ground truth
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@@ -310,14 +291,10 @@ def main():
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else:
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# Fallback: try to infer from the index directory name
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dataset_type = Path(args.index_path).name
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print(
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f"WARNING: Could not detect dataset type from path, inferred '{dataset_type}'."
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)
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print(f"WARNING: Could not detect dataset type from path, inferred '{dataset_type}'.")
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queries_file = data_root / "queries" / "nq_open.jsonl"
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golden_results_file = (
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data_root / "ground_truth" / dataset_type / "flat_results_nq_k3.json"
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)
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golden_results_file = data_root / "ground_truth" / dataset_type / "flat_results_nq_k3.json"
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print(f"INFO: Detected dataset type: {dataset_type}")
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print(f"INFO: Using queries file: {queries_file}")
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@@ -327,7 +304,7 @@ def main():
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searcher = LeannSearcher(args.index_path)
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queries = load_queries(queries_file)
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with open(golden_results_file, "r") as f:
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with open(golden_results_file) as f:
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golden_results_data = json.load(f)
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num_eval_queries = min(args.num_queries, len(queries))
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@@ -339,9 +316,7 @@ def main():
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for i in range(num_eval_queries):
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start_time = time.time()
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new_results = searcher.search(
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queries[i], top_k=args.top_k, ef=args.ef_search
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)
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new_results = searcher.search(queries[i], top_k=args.top_k, ef=args.ef_search)
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search_times.append(time.time() - start_time)
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# Correct Recall Calculation: Based on TEXT content
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