mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-03-19 18:58:47 +08:00
6712965b7f
Rename the v2 search engine package to `codexlens-search` (import as `codexlens_search`) so it can be installed independently and consumed by the original codex-lens as a dependency. This avoids package path conflicts since both previously used `src/codexlens/`. Changes: - Rename src/codexlens/ -> src/codexlens_search/ - Update pyproject.toml: name=codexlens-search, version=0.2.0 - Update all imports across source, tests, and scripts - Add public API exports in __init__.py (Config, SearchPipeline, IndexingPipeline, SearchResult, IndexStats) 37/37 tests pass. No functional changes. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
129 lines
4.5 KiB
Python
129 lines
4.5 KiB
Python
"""
|
||
对 D:/Claude_dms3 仓库进行索引并测试搜索。
|
||
用法: python scripts/index_and_search.py
|
||
"""
|
||
import sys
|
||
import time
|
||
from pathlib import Path
|
||
|
||
# 确保 src 可被导入
|
||
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
|
||
|
||
from codexlens_search.config import Config
|
||
from codexlens_search.core.factory import create_ann_index, create_binary_index
|
||
from codexlens_search.embed.local import FastEmbedEmbedder
|
||
from codexlens_search.indexing import IndexingPipeline
|
||
from codexlens_search.rerank.local import FastEmbedReranker
|
||
from codexlens_search.search.fts import FTSEngine
|
||
from codexlens_search.search.pipeline import SearchPipeline
|
||
|
||
# ─── 配置 ──────────────────────────────────────────────────────────────────
|
||
REPO_ROOT = Path("D:/Claude_dms3")
|
||
INDEX_DIR = Path("D:/Claude_dms3/codex-lens-v2/.index_cache")
|
||
EXTENSIONS = {".py", ".ts", ".js", ".md"}
|
||
MAX_FILE_SIZE = 50_000 # bytes
|
||
MAX_CHUNK_CHARS = 800 # 每个 chunk 的最大字符数
|
||
CHUNK_OVERLAP = 100
|
||
|
||
# ─── 文件收集 ───────────────────────────────────────────────────────────────
|
||
SKIP_DIRS = {
|
||
".git", "node_modules", "__pycache__", ".pytest_cache",
|
||
"dist", "build", ".venv", "venv", ".cache", ".index_cache",
|
||
"codex-lens-v2", # 不索引自身
|
||
}
|
||
|
||
def collect_files(root: Path) -> list[Path]:
|
||
files = []
|
||
for p in root.rglob("*"):
|
||
if any(part in SKIP_DIRS for part in p.parts):
|
||
continue
|
||
if p.is_file() and p.suffix in EXTENSIONS:
|
||
if p.stat().st_size <= MAX_FILE_SIZE:
|
||
files.append(p)
|
||
return files
|
||
|
||
# ─── 主流程 ─────────────────────────────────────────────────────────────────
|
||
def main():
|
||
INDEX_DIR.mkdir(parents=True, exist_ok=True)
|
||
|
||
# 1. 使用小 profile 加快速度
|
||
config = Config(
|
||
embed_model="BAAI/bge-small-en-v1.5",
|
||
embed_dim=384,
|
||
embed_batch_size=32,
|
||
hnsw_ef=100,
|
||
hnsw_M=16,
|
||
binary_top_k=100,
|
||
ann_top_k=30,
|
||
reranker_top_k=10,
|
||
)
|
||
|
||
print("=== codex-lens-v2 索引测试 ===\n")
|
||
|
||
# 2. 收集文件
|
||
print(f"[1/4] 扫描 {REPO_ROOT} ...")
|
||
files = collect_files(REPO_ROOT)
|
||
print(f" 找到 {len(files)} 个文件")
|
||
|
||
# 3. 初始化组件
|
||
print(f"\n[2/4] 加载嵌入模型 (bge-small-en-v1.5, dim=384) ...")
|
||
embedder = FastEmbedEmbedder(config)
|
||
binary_store = create_binary_index(INDEX_DIR, config.embed_dim, config)
|
||
ann_index = create_ann_index(INDEX_DIR, config.embed_dim, config)
|
||
fts = FTSEngine(":memory:") # 内存 FTS，不持久化
|
||
|
||
# 4. 使用 IndexingPipeline 并行索引 (chunk -> embed -> index)
|
||
print(f"[3/4] 并行索引 {len(files)} 个文件 ...")
|
||
pipeline = IndexingPipeline(
|
||
embedder=embedder,
|
||
binary_store=binary_store,
|
||
ann_index=ann_index,
|
||
fts=fts,
|
||
config=config,
|
||
)
|
||
stats = pipeline.index_files(
|
||
files,
|
||
root=REPO_ROOT,
|
||
max_chunk_chars=MAX_CHUNK_CHARS,
|
||
chunk_overlap=CHUNK_OVERLAP,
|
||
max_file_size=MAX_FILE_SIZE,
|
||
)
|
||
print(f" 索引完成: {stats.files_processed} 文件, {stats.chunks_created} chunks ({stats.duration_seconds:.1f}s)")
|
||
|
||
# 5. 搜索测试
|
||
print(f"\n[4/4] 构建 SearchPipeline ...")
|
||
reranker = FastEmbedReranker(config)
|
||
pipeline = SearchPipeline(
|
||
embedder=embedder,
|
||
binary_store=binary_store,
|
||
ann_index=ann_index,
|
||
reranker=reranker,
|
||
fts=fts,
|
||
config=config,
|
||
)
|
||
|
||
queries = [
|
||
"authentication middleware function",
|
||
"def embed_single",
|
||
"RRF fusion weights",
|
||
"fastembed TextCrossEncoder reranker",
|
||
"how to search code semantic",
|
||
]
|
||
|
||
print("\n" + "=" * 60)
|
||
for query in queries:
|
||
t0 = time.time()
|
||
results = pipeline.search(query, top_k=5)
|
||
elapsed = time.time() - t0
|
||
print(f"\nQuery: {query!r} ({elapsed*1000:.0f}ms)")
|
||
if results:
|
||
for r in results:
|
||
print(f" [{r.score:.3f}] {r.path}")
|
||
else:
|
||
print(" (无结果)")
|
||
print("=" * 60)
|
||
print("\n测试完成 ✓")
|
||
|
||
if __name__ == "__main__":
|
||
main()
|