import asyncio import os import tempfile from pathlib import Path from dotenv import load_dotenv load_dotenv() from reme2.config.config_parser import _load_yaml from reme2.reme import ReMe async def test_full_watcher(): """测试full watcher模式 - 处理所有文件类型""" print("\n=== 测试 Full Watcher 模式 ===") config = _load_yaml("paw") # 设置API密钥 config["components"]["embedding_model"]["default"]["api_key"] = os.environ["EMBEDDING_API_KEY"] config["components"]["embedding_model"]["default"]["base_url"] = os.environ["EMBEDDING_BASE_URL"] config["components"]["as_llm"]["default"]["api_key"] = os.environ["LLM_API_KEY"] config["components"]["as_llm"]["default"]["base_url"] = os.environ["LLM_BASE_URL"] config["components"]["as_llm"]["default"]["model_name"] = os.environ["LLM_MODEL_NAME"] # 创建临时目录用于测试 with tempfile.TemporaryDirectory() as temp_dir: temp_path = Path(temp_dir) # 创建测试文件 md_file = temp_path / "test.md" txt_file = temp_path / "test.txt" py_file = temp_path / "test.py" md_content = """# Test Markdown File This is a test markdown file for full watcher. ## Section Some content here about Python programming. """ txt_content = """Plain text file for testing full watcher. Contains some interesting content about machine learning AI.""" py_content = '''""" Test Python file for full watcher. Demonstrates Python programming concepts. """ def hello_world(): """A simple function.""" print("Hello, world!") # Machine learning related code return True ''' md_file.write_text(md_content) txt_file.write_text(txt_content) py_file.write_text(py_content) # 更新配置使用临时目录 config["components"]["file_watcher"]["default"]["watch_paths"] = [str(temp_path)] config["components"]["file_watcher"]["default"]["recursive"] = False reme = ReMe(**config) # Start components (embedding, file_store, file_parser, file_watcher) await reme.start() # Wait for watcher to scan existing files await asyncio.sleep(3) # Test: list indexed files from reme2.enumeration import ComponentEnum store = reme.context.components[ComponentEnum.FILE_STORE]["default"] files = await store.list_files() print(f"\n=== Indexed files by Full Watcher ({len(files)}) ===") for f in files: meta = await store.get_file_metadata(f) chunks = await store.get_file_chunks(f) print(f" {f} — {meta.chunk_count} chunks, metadata={meta.metadata}") # Test: keyword search results = await store.keyword_search("Python programming", limit=5) print(f"\n=== Keyword search 'Python programming' ({len(results)} results) ===") for r in results: print(f" [{r.score:.2f}] {r.path}:{r.start_line}-{r.end_line} — {r.text[:80]}") # Test: hybrid search results = await store.hybrid_search("machine learning AI", limit=5) print(f"\n=== Hybrid search 'machine learning AI' ({len(results)} results) ===") for r in results: print(f" [{r.score:.2f}] {r.path}:{r.start_line}-{r.end_line} — {r.text[:80]}") await reme.close() print("\n=== Full Watcher 测试完成 ===") async def test_light_watcher(): """测试light watcher模式 - 只处理markdown文件""" print("\n=== 测试 Light Watcher 模式 ===") config = _load_yaml("paw") # 设置API密钥 config["components"]["embedding_model"]["default"]["api_key"] = os.environ["EMBEDDING_API_KEY"] config["components"]["embedding_model"]["default"]["base_url"] = os.environ["EMBEDDING_BASE_URL"] config["components"]["as_llm"]["default"]["api_key"] = os.environ["LLM_API_KEY"] config["components"]["as_llm"]["default"]["base_url"] = os.environ["LLM_BASE_URL"] config["components"]["as_llm"]["default"]["model_name"] = os.environ["LLM_MODEL_NAME"] # 更改配置使用light watcher config["components"]["file_watcher"]["default"]["backend"] = "light" # 创建临时目录用于测试 with tempfile.TemporaryDirectory() as temp_dir: temp_path = Path(temp_dir) # 创建测试文件 - 包含markdown和其他类型文件 md_file = temp_path / "light_test.md" txt_file = temp_path / "light_test.txt" py_file = temp_path / "light_test.py" md_content = """# Light Test Markdown File This is a test markdown file for light watcher. ## Section Only markdown files should be processed by light watcher. """ txt_content = "This text file should be ignored by light watcher." py_content = "# This Python file should be ignored by light watcher" md_file.write_text(md_content) txt_file.write_text(txt_content) py_file.write_text(py_content) # 更新配置使用临时目录 config["components"]["file_watcher"]["default"]["watch_paths"] = [str(temp_path)] config["components"]["file_watcher"]["default"]["recursive"] = False reme = ReMe(**config) # Start components await reme.start() # Wait for watcher to scan existing files await asyncio.sleep(3) # Test: list indexed files - should only contain markdown files from reme2.enumeration import ComponentEnum store = reme.context.components[ComponentEnum.FILE_STORE]["default"] files = await store.list_files() print(f"\n=== Indexed files by Light Watcher ({len(files)}) ===") for f in files: # 验证只有markdown文件被索引 file_path = Path(f) if file_path.suffix.lower() in [".md", ".markdown"]: meta = await store.get_file_metadata(f) chunks = await store.get_file_chunks(f) print(f" {f} — {meta.chunk_count} chunks, metadata={meta.metadata}") else: print(f" ERROR: Non-markdown file indexed: {f}") print(f"\nExpected: Only markdown files should be indexed. Actually indexed: {len(files)} files") # Test: search results = await store.keyword_search("light watcher", limit=5) print(f"\n=== Keyword search 'light watcher' ({len(results)} results) ===") for r in results: print(f" [{r.score:.2f}] {r.path}:{r.start_line}-{r.end_line} — {r.text[:80]}") await reme.close() print("\n=== Light Watcher 测试完成 ===") async def main(): """主函数，运行所有测试""" print("开始测试ReMe系统的Full和Light Watcher模式") try: await test_full_watcher() await test_light_watcher() print("\n✅ 所有测试完成！") except Exception as e: print(f"\n❌ 测试失败: {e}") import traceback traceback.print_exc() if __name__ == "__main__": asyncio.run(main())