Resolve submodule conflict - update to af2a264
This commit is contained in:
yichuan520030910320
@@ -11,122 +11,143 @@ 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, Dict, Any
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import glob
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import pickle
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# Add project root to path to allow importing from leann
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project_root = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(project_root))
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from typing import List
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from leann.api import LeannSearcher
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# --- Configuration ---
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NQ_QUERIES_FILE = Path("/opt/dlami/nvme/scaling_out/examples/nq_open.jsonl")
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# Ground truth files for different datasets
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GROUND_TRUTH_FILES = {
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"rpj_wiki": "/opt/dlami/nvme/scaling_out/indices/rpj_wiki/facebook/contriever-msmarco/flat_results_nq_k3.json",
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"dpr": "/opt/dlami/nvme/scaling_out/indices/dpr/facebook/contriever-msmarco/flat_results_nq_k3.json"
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}
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def download_data_if_needed(data_root: Path):
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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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try:
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from huggingface_hub import snapshot_download
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# Old passages for different datasets
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OLD_PASSAGES_GLOBS = {
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"rpj_wiki": "/opt/dlami/nvme/scaling_out/passages/rpj_wiki/8-shards/raw_passages-*-of-8.pkl.jsonl",
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"dpr": "/opt/dlami/nvme/scaling_out/passages/dpr/1-shards/raw_passages-*-of-1.pkl.jsonl"
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}
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snapshot_download(
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repo_id="LEANN-RAG/leann-rag-evaluation-data",
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repo_type="dataset",
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local_dir=data_root,
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local_dir_use_symlinks=False, # Recommended for Windows compatibility and simpler structure
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)
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print("Data download complete!")
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except ImportError:
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print(
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"Error: huggingface_hub is not installed. Please install it to download the data:"
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)
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print("uv pip install -e '.[dev]'")
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sys.exit(1)
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except Exception as e:
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print(f"An error occurred during data download: {e}")
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sys.exit(1)
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# --- Helper Class to Load Original Passages ---
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class OldPassageLoader:
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"""A simplified version of the old LazyPassages class to fetch golden results by ID."""
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def __init__(self, passages_glob: str):
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self.jsonl_paths = sorted(glob.glob(passages_glob))
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self.offsets = {}
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self.fps = [open(p, "r", encoding="utf-8") for p in self.jsonl_paths]
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print("Building offset map for original passages...")
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for i, shard_path_str in enumerate(self.jsonl_paths):
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old_idx_path = Path(shard_path_str.replace(".jsonl", ".idx"))
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if not old_idx_path.exists(): continue
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with open(old_idx_path, 'rb') as f:
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shard_offsets = pickle.load(f)
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for pid, offset in shard_offsets.items():
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self.offsets[str(pid)] = (i, offset)
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print("Offset map for original passages is ready.")
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def get_passage_by_id(self, pid: str) -> Dict[str, Any]:
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pid = str(pid)
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if pid not in self.offsets:
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raise ValueError(f"Passage ID {pid} not found in offsets")
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file_idx, offset = self.offsets[pid]
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fp = self.fps[file_idx]
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fp.seek(offset)
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return json.loads(fp.readline())
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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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"""
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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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"""
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golden_texts = set()
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for gid in golden_ids:
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try:
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# PassageManager uses string IDs
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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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return golden_texts
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def __del__(self):
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for fp in self.fps:
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fp.close()
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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, "r", 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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queries.append(data["query"])
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return queries
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def main():
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parser = argparse.ArgumentParser(description="Run recall evaluation on a LEANN index.")
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parser.add_argument("index_path", type=str, help="Path to the LEANN index to evaluate.")
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parser.add_argument("--num-queries", type=int, default=10, help="Number of queries to evaluate.")
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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("--ef-search", type=int, default=120, help="The 'efSearch' parameter for HNSW.")
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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.add_argument(
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"index_path", type=str, help="Path to the LEANN index to evaluate."
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)
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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(
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"--ef-search", type=int, default=120, help="The 'efSearch' parameter for HNSW."
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)
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args = parser.parse_args()
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print(f"--- Final, Correct Recall Evaluation (efSearch={args.ef_search}) ---")
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# Detect dataset type from index path
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# --- Path Configuration ---
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# Assumes a project structure where the script is in 'examples/'
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# and data is in 'data/' at the project root.
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project_root = Path(__file__).resolve().parent.parent
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data_root = project_root / "data"
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# Automatically download data if it doesn't exist
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download_data_if_needed(data_root)
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# Detect dataset type from index path to select the correct ground truth
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index_path_str = str(args.index_path)
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if "rpj_wiki" in index_path_str:
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dataset_type = "rpj_wiki"
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elif "dpr" in index_path_str:
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dataset_type = "dpr"
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else:
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print("WARNING: Unknown dataset type, defaulting to rpj_wiki")
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dataset_type = "rpj_wiki"
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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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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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print(f"INFO: Detected dataset type: {dataset_type}")
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print(f"INFO: Using queries file: {queries_file}")
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print(f"INFO: Using ground truth file: {golden_results_file}")
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try:
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searcher = LeannSearcher(args.index_path)
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queries = load_queries(NQ_QUERIES_FILE)
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golden_results_file = GROUND_TRUTH_FILES[dataset_type]
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old_passages_glob = OLD_PASSAGES_GLOBS[dataset_type]
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print(f"INFO: Using ground truth file: {golden_results_file}")
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print(f"INFO: Using old passages glob: {old_passages_glob}")
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with open(golden_results_file, 'r') as f:
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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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golden_results_data = json.load(f)
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old_passage_loader = OldPassageLoader(old_passages_glob)
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num_eval_queries = min(args.num_queries, len(queries))
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queries = queries[:num_eval_queries]
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print(f"\nRunning evaluation on {num_eval_queries} queries...")
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recall_scores = []
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search_times = []
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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(queries[i], top_k=args.top_k, ef=args.ef_search)
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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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search_times.append(time.time() - start_time)
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# Correct Recall Calculation: Based on TEXT content
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new_texts = {result.text for result in new_results}
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golden_ids = golden_results_data["indices"][i][:args.top_k]
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golden_texts = {old_passage_loader.get_passage_by_id(str(gid))['text'] for gid in golden_ids}
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# Get golden texts directly from the searcher's passage manager
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golden_ids = golden_results_data["indices"][i][: args.top_k]
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golden_texts = get_golden_texts(searcher, golden_ids)
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overlap = len(new_texts & golden_texts)
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recall = overlap / len(golden_texts) if golden_texts else 0
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@@ -139,19 +160,21 @@ def main():
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print(f"Overlap: {overlap}")
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print(f"Recall: {recall}")
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print(f"Search Time: {search_times[-1]:.4f}s")
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print(f"--------------------------------")
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print("--------------------------------")
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avg_recall = np.mean(recall_scores) if recall_scores else 0
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avg_time = np.mean(search_times) if search_times else 0
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print(f"\n🎉 --- Evaluation Complete ---")
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print("\n🎉 --- Evaluation Complete ---")
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print(f"Avg. Recall@{args.top_k} (efSearch={args.ef_search}): {avg_recall:.4f}")
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print(f"Avg. Search Time: {avg_time:.4f}s")
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except Exception as e:
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print(f"\n❌ An error occurred during evaluation: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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main()
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main()
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