RuntimeError: No module named ‘numpy._core‘

最新推荐文章于 2025-06-17 19:05:53 发布
原创 最新推荐文章于 2025-06-17 19:05:53 发布 · 567 阅读 · 0 ·
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#numpy

问题描述

RuntimeError: No module named ‘numpy._core’
降低numpy之后出现这个报错信息，查了教程，可能是版本的问题，建议更新或者清除之前剩余版本的缓存，但是都没有解决，而且可能会把环境搞乱。
还有一种可能的原因是data文件下面已经有数据了，但是换了一个新的环境，导致出现问题

解决办法

可以清除数据缓存，或者直接把数据集删除重新下载。

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bash: docker: command not found [root@ac6b15bb1f77 mas]# python -m vllm.entrypoints.openai.api_server \ > --model /models/z50051264/summary/Qwen2.5-7B-awq/ \ > --max-num-seqs=256 \ > --max-model-len=4096 \ > --max-num-batched-tokens=4096 \ > --tensor-parallel-size=1 \ > --block-size=128 \ > --host=0.0.0.0 \ > --port=8080 \ > --gpu-memory-utilization=0.9 \ > --trust-remote-code \ > --served-model-name=zzz \ > --quantization awq INFO 07-25 03:09:14 [__init__.py:39] Available plugins for group vllm.platform_plugins: INFO 07-25 03:09:14 [__init__.py:41] - ascend -> vllm_ascend:register INFO 07-25 03:09:14 [__init__.py:44] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load. INFO 07-25 03:09:14 [__init__.py:235] Platform plugin ascend is activated WARNING 07-25 03:09:15 [_custom_ops.py:20] Failed to import from vllm._C with ModuleNotFoundError("No module named 'vllm._C'") INFO 07-25 03:09:18 [importing.py:63] Triton not installed or not compatible; certain GPU-related functions will not be available. WARNING 07-25 03:09:19 [registry.py:413] Model architecture DeepSeekMTPModel is already registered, and will be overwritten by the new model class vllm_ascend.models.deepseek_mtp:CustomDeepSeekMTP. WARNING 07-25 03:09:19 [registry.py:413] Model architecture Qwen2VLForConditionalGeneration is already registered, and will be overwritten by the new model class vllm_ascend.models.qwen2_vl:AscendQwen2VLForConditionalGeneration. WARNING 07-25 03:09:19 [registry.py:413] Model architecture Qwen2_5_VLForConditionalGeneration is already registered, and will be overwritten by the new model class vllm_ascend.models.qwen2_5_vl:AscendQwen2_5_VLForConditionalGeneration. WARNING 07-25 03:09:19 [registry.py:413] Model architecture DeepseekV2ForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.deepseek_v2:CustomDeepseekV2ForCausalLM. WARNING 07-25 03:09:19 [registry.py:413] Model architecture DeepseekV3ForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.deepseek_v2:CustomDeepseekV3ForCausalLM. WARNING 07-25 03:09:19 [registry.py:413] Model architecture Qwen3MoeForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.qwen3_moe:CustomQwen3MoeForCausalLM. INFO 07-25 03:09:20 [api_server.py:1395] vLLM API server version 0.9.2 INFO 07-25 03:09:20 [cli_args.py:325] non-default args: {'host': '0.0.0.0', 'port': 8080, 'model': '/models/z50051264/summary/Qwen2.5-7B-awq/', 'trust_remote_code': True, 'max_model_len': 4096, 'quantization': 'awq', 'served_model_name': ['zzz'], 'block_size': 128, 'max_num_batched_tokens': 4096, 'max_num_seqs': 256} INFO 07-25 03:09:34 [config.py:841] This model supports multiple tasks: {'generate', 'classify', 'embed', 'reward'}. Defaulting to 'generate'. INFO 07-25 03:09:34 [config.py:1472] Using max model len 4096 WARNING 07-25 03:09:35 [config.py:960] ascend quantization is not fully optimized yet. The speed can be slower than non-quantized models. INFO 07-25 03:09:35 [config.py:2285] Chunked prefill is enabled with max_num_batched_tokens=4096. INFO 07-25 03:09:35 [platform.py:174] PIECEWISE compilation enabled on NPU. use_inductor not supported - using only ACL Graph mode INFO 07-25 03:09:35 [utils.py:321] Calculated maximum supported batch sizes for ACL graph: 66 INFO 07-25 03:09:35 [utils.py:336] Adjusted ACL graph batch sizes for Qwen2ForCausalLM model (layers: 28): 67 → 66 sizes INFO 07-25 03:09:45 [__init__.py:39] Available plugins for group vllm.platform_plugins: INFO 07-25 03:09:45 [__init__.py:41] - ascend -> vllm_ascend:register INFO 07-25 03:09:45 [__init__.py:44] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load. INFO 07-25 03:09:45 [__init__.py:235] Platform plugin ascend is activated WARNING 07-25 03:09:46 [_custom_ops.py:20] Failed to import from vllm._C with ModuleNotFoundError("No module named 'vllm._C'") INFO 07-25 03:09:50 [importing.py:63] Triton not installed or not compatible; certain GPU-related functions will not be available. INFO 07-25 03:09:50 [core.py:526] Waiting for init message from front-end. WARNING 07-25 03:09:50 [registry.py:413] Model architecture DeepSeekMTPModel is already registered, and will be overwritten by the new model class vllm_ascend.models.deepseek_mtp:CustomDeepSeekMTP. WARNING 07-25 03:09:50 [registry.py:413] Model architecture Qwen2VLForConditionalGeneration is already registered, and will be overwritten by the new model class vllm_ascend.models.qwen2_vl:AscendQwen2VLForConditionalGeneration. WARNING 07-25 03:09:50 [registry.py:413] Model architecture Qwen2_5_VLForConditionalGeneration is already registered, and will be overwritten by the new model class vllm_ascend.models.qwen2_5_vl:AscendQwen2_5_VLForConditionalGeneration. WARNING 07-25 03:09:50 [registry.py:413] Model architecture DeepseekV2ForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.deepseek_v2:CustomDeepseekV2ForCausalLM. WARNING 07-25 03:09:50 [registry.py:413] Model architecture DeepseekV3ForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.deepseek_v2:CustomDeepseekV3ForCausalLM. WARNING 07-25 03:09:50 [registry.py:413] Model architecture Qwen3MoeForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.qwen3_moe:CustomQwen3MoeForCausalLM. INFO 07-25 03:09:50 [core.py:69] Initializing a V1 LLM engine (v0.9.2) with config: model='/models/z50051264/summary/Qwen2.5-7B-awq/', speculative_config=None, tokenizer='/models/z50051264/summary/Qwen2.5-7B-awq/', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config={}, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=True, quantization=ascend, enforce_eager=False, kv_cache_dtype=auto, device_config=npu, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=zzz, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=True, pooler_config=None, compilation_config={"level":3,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":["all"],"splitting_ops":["vllm.unified_attention","vllm.unified_attention_with_output","vllm.unified_ascend_attention_with_output"],"use_inductor":false,"compile_sizes":[],"inductor_compile_config":{},"inductor_passes":{},"use_cudagraph":true,"cudagraph_num_of_warmups":1,"cudagraph_capture_sizes":[512,504,496,488,480,472,464,456,448,440,432,424,416,408,400,392,384,376,368,360,352,344,336,328,320,312,304,296,288,280,272,264,256,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"cudagraph_copy_inputs":false,"full_cuda_graph":false,"max_capture_size":512,"local_cache_dir":null} ERROR 07-25 03:09:53 [core.py:586] EngineCore failed to start. ERROR 07-25 03:09:53 [core.py:586] Traceback (most recent call last): ERROR 07-25 03:09:53 [core.py:586] File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 577, in run_engine_core ERROR 07-25 03:09:53 [core.py:586] engine_core = EngineCoreProc(*args, **kwargs) ERROR 07-25 03:09:53 [core.py:586] File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 404, in __init__ ERROR 07-25 03:09:53 [core.py:586] super().__init__(vllm_config, executor_class, log_stats, ERROR 07-25 03:09:53 [core.py:586] File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 75, in __init__ ERROR 07-25 03:09:53 [core.py:586] self.model_executor = executor_class(vllm_config) ERROR 07-25 03:09:53 [core.py:586] File "/vllm-workspace/vllm/vllm/executor/executor_base.py", line 53, in __init__ ERROR 07-25 03:09:53 [core.py:586] self._init_executor() ERROR 07-25 03:09:53 [core.py:586] File "/vllm-workspace/vllm/vllm/executor/uniproc_executor.py", line 47, in _init_executor ERROR 07-25 03:09:53 [core.py:586] self.collective_rpc("init_device") ERROR 07-25 03:09:53 [core.py:586] File "/vllm-workspace/vllm/vllm/executor/uniproc_executor.py", line 57, in collective_rpc ERROR 07-25 03:09:53 [core.py:586] answer = run_method(self.driver_worker, method, args, kwargs) ERROR 07-25 03:09:53 [core.py:586] File "/vllm-workspace/vllm/vllm/utils/__init__.py", line 2736, in run_method ERROR 07-25 03:09:53 [core.py:586] return func(*args, **kwargs) ERROR 07-25 03:09:53 [core.py:586] File "/vllm-workspace/vllm/vllm/worker/worker_base.py", line 606, in init_device ERROR 07-25 03:09:53 [core.py:586] self.worker.init_device() # type: ignore ERROR 07-25 03:09:53 [core.py:586] File "/vllm-workspace/vllm-ascend/vllm_ascend/worker/worker_v1.py", line 132, in init_device ERROR 07-25 03:09:53 [core.py:586] NPUPlatform.set_device(device) ERROR 07-25 03:09:53 [core.py:586] File "/vllm-workspace/vllm-ascend/vllm_ascend/platform.py", line 98, in set_device ERROR 07-25 03:09:53 [core.py:586] torch.npu.set_device(device) ERROR 07-25 03:09:53 [core.py:586] File "/usr/local/python3.10.17/lib/python3.10/site-packages/torch_npu/npu/utils.py", line 80, in set_device ERROR 07-25 03:09:53 [core.py:586] torch_npu._C._npu_setDevice(device_id) ERROR 07-25 03:09:53 [core.py:586] RuntimeError: SetPrecisionMode:build/CMakeFiles/torch_npu.dir/compiler_depend.ts:156 NPU function error: at_npu::native::AclSetCompileopt(aclCompileOpt::ACL_PRECISION_MODE, precision_mode), error code is 500001 ERROR 07-25 03:09:53 [core.py:586] [ERROR] 2025-07-25-03:09:53 (PID:977, Device:0, RankID:-1) ERR00100 PTA call acl api failed ERROR 07-25 03:09:53 [core.py:586] [Error]: The internal ACL of the system is incorrect. ERROR 07-25 03:09:53 [core.py:586] Rectify the fault based on the error information in the ascend log. ERROR 07-25 03:09:53 [core.py:586] EC0010: [PID: 977] 2025-07-25-03:09:53.177.260 Failed to import Python module [AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead..]. ERROR 07-25 03:09:53 [core.py:586] Solution: Check that all required components are properly installed and the specified Python path matches the Python installation directory. (If the path does not match the directory, run set_env.sh in the installation package.) ERROR 07-25 03:09:53 [core.py:586] TraceBack (most recent call last): ERROR 07-25 03:09:53 [core.py:586] AOE Failed to call InitCannKB[FUNC:Initialize][FILE:python_adapter_manager.cc][LINE:47] ERROR 07-25 03:09:53 [core.py:586] Failed to initialize TeConfigInfo. ERROR 07-25 03:09:53 [core.py:586] [GraphOpt][InitializeInner][InitTbeFunc] Failed to init tbe.[FUNC:InitializeTeFusion][FILE:tbe_op_store_adapter.cc][LINE:1889] ERROR 07-25 03:09:53 [core.py:586] [GraphOpt][InitializeInner][InitTeFusion]: Failed to initialize TeFusion.[FUNC:InitializeInner][FILE:tbe_op_store_adapter.cc][LINE:1856] ERROR 07-25 03:09:53 [core.py:586] [SubGraphOpt][PreCompileOp][InitAdapter] InitializeAdapter adapter [tbe_op_adapter] failed! Ret [4294967295][FUNC:InitializeAdapter][FILE:op_store_adapter_manager.cc][LINE:79] ERROR 07-25 03:09:53 [core.py:586] [SubGraphOpt][PreCompileOp][Init] Initialize op store adapter failed, OpsStoreName[tbe-custom].[FUNC:Initialize][FILE:op_store_adapter_manager.cc][LINE:120] ERROR 07-25 03:09:53 [core.py:586] [FusionMngr][Init] Op store adapter manager init failed.[FUNC:Initialize][FILE:fusion_manager.cc][LINE:115] ERROR 07-25 03:09:53 [core.py:586] PluginManager InvokeAll failed.[FUNC:Initialize][FILE:ops_kernel_manager.cc][LINE:83] ERROR 07-25 03:09:53 [core.py:586] OpsManager initialize failed.[FUNC:InnerInitialize][FILE:gelib.cc][LINE:259] ERROR 07-25 03:09:53 [core.py:586] GELib::InnerInitialize failed.[FUNC:Initialize][FILE:gelib.cc][LINE:184] ERROR 07-25 03:09:53 [core.py:586] GEInitialize failed.[FUNC:GEInitialize][FILE:ge_api.cc][LINE:371] ERROR 07-25 03:09:53 [core.py:586] [Initialize][Ge]GEInitialize failed. ge result = 4294967295[FUNC:ReportCallError][FILE:log_inner.cpp][LINE:161] ERROR 07-25 03:09:53 [core.py:586] [Init][Compiler]Init compiler failed[FUNC:ReportInnerError][FILE:log_inner.cpp][LINE:145] ERROR 07-25 03:09:53 [core.py:586] [Set][Options]OpCompileProcessor init failed![FUNC:ReportInnerError][FILE:log_inner.cpp][LINE:145] ERROR 07-25 03:09:53 [core.py:586] Process EngineCore_0: Traceback (most recent call last): File "/usr/local/python3.10.17/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/local/python3.10.17/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 590, in run_engine_core raise e File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 577, in run_engine_core engine_core = EngineCoreProc(*args, **kwargs) File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 404, in __init__ super().__init__(vllm_config, executor_class, log_stats, File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 75, in __init__ self.model_executor = executor_class(vllm_config) File "/vllm-workspace/vllm/vllm/executor/executor_base.py", line 53, in __init__ self._init_executor() File "/vllm-workspace/vllm/vllm/executor/uniproc_executor.py", line 47, in _init_executor self.collective_rpc("init_device") File "/vllm-workspace/vllm/vllm/executor/uniproc_executor.py", line 57, in collective_rpc answer = run_method(self.driver_worker, method, args, kwargs) File "/vllm-workspace/vllm/vllm/utils/__init__.py", line 2736, in run_method return func(*args, **kwargs) File "/vllm-workspace/vllm/vllm/worker/worker_base.py", line 606, in init_device self.worker.init_device() # type: ignore File "/vllm-workspace/vllm-ascend/vllm_ascend/worker/worker_v1.py", line 132, in init_device NPUPlatform.set_device(device) File "/vllm-workspace/vllm-ascend/vllm_ascend/platform.py", line 98, in set_device torch.npu.set_device(device) File "/usr/local/python3.10.17/lib/python3.10/site-packages/torch_npu/npu/utils.py", line 80, in set_device torch_npu._C._npu_setDevice(device_id) RuntimeError: SetPrecisionMode:build/CMakeFiles/torch_npu.dir/compiler_depend.ts:156 NPU function error: at_npu::native::AclSetCompileopt(aclCompileOpt::ACL_PRECISION_MODE, precision_mode), error code is 500001 [ERROR] 2025-07-25-03:09:53 (PID:977, Device:0, RankID:-1) ERR00100 PTA call acl api failed [Error]: The internal ACL of the system is incorrect. Rectify the fault based on the error information in the ascend log. EC0010: [PID: 977] 2025-07-25-03:09:53.177.260 Failed to import Python module [AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead..]. Solution: Check that all required components are properly installed and the specified Python path matches the Python installation directory. (If the path does not match the directory, run set_env.sh in the installation package.) TraceBack (most recent call last): AOE Failed to call InitCannKB[FUNC:Initialize][FILE:python_adapter_manager.cc][LINE:47] Failed to initialize TeConfigInfo. [GraphOpt][InitializeInner][InitTbeFunc] Failed to init tbe.[FUNC:InitializeTeFusion][FILE:tbe_op_store_adapter.cc][LINE:1889] [GraphOpt][InitializeInner][InitTeFusion]: Failed to initialize TeFusion.[FUNC:InitializeInner][FILE:tbe_op_store_adapter.cc][LINE:1856] [SubGraphOpt][PreCompileOp][InitAdapter] InitializeAdapter adapter [tbe_op_adapter] failed! Ret [4294967295][FUNC:InitializeAdapter][FILE:op_store_adapter_manager.cc][LINE:79] [SubGraphOpt][PreCompileOp][Init] Initialize op store adapter failed, OpsStoreName[tbe-custom].[FUNC:Initialize][FILE:op_store_adapter_manager.cc][LINE:120] [FusionMngr][Init] Op store adapter manager init failed.[FUNC:Initialize][FILE:fusion_manager.cc][LINE:115] PluginManager InvokeAll failed.[FUNC:Initialize][FILE:ops_kernel_manager.cc][LINE:83] OpsManager initialize failed.[FUNC:InnerInitialize][FILE:gelib.cc][LINE:259] GELib::InnerInitialize failed.[FUNC:Initialize][FILE:gelib.cc][LINE:184] GEInitialize failed.[FUNC:GEInitialize][FILE:ge_api.cc][LINE:371] [Initialize][Ge]GEInitialize failed. ge result = 4294967295[FUNC:ReportCallError][FILE:log_inner.cpp][LINE:161] [Init][Compiler]Init compiler failed[FUNC:ReportInnerError][FILE:log_inner.cpp][LINE:145] [Set][Options]OpCompileProcessor init failed![FUNC:ReportInnerError][FILE:log_inner.cpp][LINE:145] Traceback (most recent call last): File "/usr/local/python3.10.17/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/usr/local/python3.10.17/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/vllm-workspace/vllm/vllm/entrypoints/openai/api_server.py", line 1495, in <module> uvloop.run(run_server(args)) File "/usr/local/python3.10.17/lib/python3.10/site-packages/uvloop/__init__.py", line 82, in run return loop.run_until_complete(wrapper()) File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete File "/usr/local/python3.10.17/lib/python3.10/site-packages/uvloop/__init__.py", line 61, in wrapper return await main File "/vllm-workspace/vllm/vllm/entrypoints/openai/api_server.py", line 1431, in run_server await run_server_worker(listen_address, sock, args, **uvicorn_kwargs) File "/vllm-workspace/vllm/vllm/entrypoints/openai/api_server.py", line 1451, in run_server_worker async with build_async_engine_client(args, client_config) as engine_client: File "/usr/local/python3.10.17/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/vllm-workspace/vllm/vllm/entrypoints/openai/api_server.py", line 158, in build_async_engine_client async with build_async_engine_client_from_engine_args( File "/usr/local/python3.10.17/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/vllm-workspace/vllm/vllm/entrypoints/openai/api_server.py", line 194, in build_async_engine_client_from_engine_args async_llm = AsyncLLM.from_vllm_config( File "/vllm-workspace/vllm/vllm/v1/engine/async_llm.py", line 162, in from_vllm_config return cls( File "/vllm-workspace/vllm/vllm/v1/engine/async_llm.py", line 124, in __init__ self.engine_core = EngineCoreClient.make_async_mp_client( File "/vllm-workspace/vllm/vllm/v1/engine/core_client.py", line 96, in make_async_mp_client return AsyncMPClient(*client_args) File "/vllm-workspace/vllm/vllm/v1/engine/core_client.py", line 666, in __init__ super().__init__( File "/vllm-workspace/vllm/vllm/v1/engine/core_client.py", line 403, in __init__ with launch_core_engines(vllm_config, executor_class, File "/usr/local/python3.10.17/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/vllm-workspace/vllm/vllm/v1/engine/utils.py", line 434, in launch_core_engines wait_for_engine_startup( File "/vllm-workspace/vllm/vllm/v1/engine/utils.py", line 484, in wait_for_engine_startup raise RuntimeError("Engine core initialization failed. " RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {} [ERROR] 2025-07-25-03:09:59 (PID:707, Device:-1, RankID:-1) ERR99999 UNKNOWN applicaiton exception 分析报错
最新发布
07-26
错误信息 `ACL_PRECISION_MODE set failed (error 500001)` 表明在初始化 Ascend NPU 设备时，尝试设置计算精度模式失败。该错误通常与以下原因相关： 1. **精度模式不兼容**：设置的精度模式（如 `fp16`/`bf16`）与...
依赖缺失 明确出现 ModuleNotFoundError: No module named 'Cython'，说明系统缺少 Cython 库，导致部分 setup.py 脚本执行失败。 缺少 gtest、gtest_vendor、gmock_vendor 相关文件（如 package.sh），导致 simple_follower_ros2 包构建时无法找到测试框架依赖。 distutils 与 numpy 兼容问题 多次出现 ImportError: cannot import name 'compiler_class' from partially initialized module 'distutils.ccompiler'，这是由于 numpy 的 distutils 模块与系统自带的 distutils 存在循环导入或版本冲突，通常是 numpy 版本与 Python 版本不兼容导致的。 setup.py 脚本不规范 大量 RuntimeError: 'distutils.core.setup()' was never called 错误，说明部分 setup.py 脚本未正确调用 distutils.core.setup() 函数，导致 colcon 的 python_setup_py 扩展无法识别这些包，进而引发识别失败。 包依赖路径问题 日志中大量包从 /opt/ros/humble 读取（系统安装的 ROS 2 Humble），但 simple_follower_ros2 依赖的 gtest 相关文件未找到，说明这些测试框架包未被正确构建或安装。
07-23
首先，用户的问题是关于ROS 2 Humble构建错误。具体错误包括：ModuleNotFoundError for Cython, distutils, numpy兼容问题；...如果问题持续，提供更多日志细节（如CMakeError.log）以便进一步诊断[^1][^4]。
我正在编辑【python】代码，遇到了 【Traceback (most recent call last): File "D:\python\python3.11.5\Lib\site-packages\langchain\_api\module_import.py", line 69, in import_by_name module = importlib.import_module(new_module) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\python\python3.11.5\Lib\importlib\__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "<frozen importlib._bootstrap>", line 1204, in _gcd_import File "<frozen importlib._bootstrap>", line 1176, in _find_and_load File "<frozen importlib._bootstrap>", line 1126, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed File "<frozen importlib._bootstrap>", line 1204, in _gcd_import File "<frozen importlib._bootstrap>", line 1176, in _find_and_load File "<frozen importlib._bootstrap>", line 1140, in _find_and_load_unlocked ModuleNotFoundError: No module named 'langchain_community' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "D:\code\nepu_spider\rag\rag_sys.py", line 11, in <module> from langchain.embeddings import HuggingFaceEmbeddings File "D:\python\python3.11.5\Lib\site-packages\langchain\embeddings\__init__.py", line 167, in __getattr__ return _import_attribute(name) ^^^^^^^^^^^^^^^^^^^^^^^ File "D:\python\python3.11.5\Lib\site-packages\langchain\_api\module_import.py", line 72, in import_by_name raise ModuleNotFoundError( ModuleNotFoundError: Module langchain_community.embeddings not found. Please install langchain-community to access this module. You can install it using `pip install -U langchain-community` 】 ，请帮我检查并改正错误点。我的原始代码如下： 【import os import json import pickle from langchain_core.language_models import LLM import install_faiss import numpy as np import requests from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import HuggingFaceEmbeddings from langchain.vectorstores import FAISS from langchain.chains import RetrievalQA from langchain.prompts import PromptTemplate from langchain.llms import OpenAI from langchain.docstore.document import Document from typing import List, Dict, Any, Tuple, Optional # 自定义智谱AI的LangChain包装器 class ZhipuAILLM(LLM): def __init__(self, api_key: str, model: str = "glm-4", temperature: float = 0.7, zhipuai=None): """初始化智谱AI模型""" self.api_key = api_key self.model = model self.temperature = temperature zhipuai.api_key = api_key # 设置API密钥 def _call(self, prompt: str, stop: Optional[List[str]] = None, zhipuai=None) -> str: """调用智谱AI API生成文本""" try: response = zhipuai.model_api.invoke( model=self.model, prompt=[{"role": "user", "content": prompt}], temperature=self.temperature, top_p=0.7, ) if response["code"] == 200: return response["data"]["choices"][0]["content"] else: raise ValueError(f"智谱AI API错误: {response['msg']}") except Exception as e: raise RuntimeError(f"调用智谱AI失败: {str(e)}") @property def _llm_type(self) -> str: return "zhipuai" class RAGSystem: def __init__(self, config: Dict[str, Any]): """初始化RAG系统""" self.config = config self.embedding_model = self._load_embedding_model() self.llm = self._load_llm() self.vector_db = None self.doc_id_map = {} # 自动加载或创建索引 self._initialize_index() def _load_embedding_model(self) -> HuggingFaceEmbeddings: """加载嵌入模型""" model_name = self.config.get("embedding_model", "sentence-transformers/all-mpnet-base-v2") return HuggingFaceEmbeddings( model_name=model_name, model_kwargs={"device": self.config.get("embedding_device", "cpu")} ) def _load_llm(self): """加载大语言模型 - 使用智谱AI""" llm_provider = self.config.get("llm_provider", "zhipuai") if llm_provider == "zhipuai": return ZhipuAILLM( api_key=self.config["zhipuai_api_key"], # 使用智谱AI密钥 model=self.config.get("llm_model", "glm-4"), temperature=self.config.get("temperature", 0.7) ) elif llm_provider == "openai": # 保留OpenAI支持 from langchain.llms import OpenAI return OpenAI( api_key=self.config["openai_api_key"], model_name=self.config.get("llm_model", "gpt-3.5-turbo"), temperature=self.config.get("temperature", 0.7) ) else: raise ValueError(f"不支持的LLM提供者: {llm_provider}") def _initialize_index(self): """初始化索引：加载现有或创建新索引""" index_path = self.config["index_path"] if os.path.exists(index_path): print(f"加载现有索引: {index_path}") self._load_vector_index() else: print(f"创建新索引: {index_path}") self._create_new_index() def _create_new_index(self): """创建新索引""" data_dir = self.config["data_dir"] if not os.path.exists(data_dir): print(f"数据目录不存在: {data_dir}") if self.config.get("auto_download", False): self._download_sample_data() else: raise FileNotFoundError(f"数据目录不存在: {data_dir}") documents = self._load_and_process_documents() self._create_vector_index(documents) def _download_sample_data(self): """下载示例数据""" print("下载示例数据...") data_dir = self.config["data_dir"] os.makedirs(data_dir, exist_ok=True) sample_urls = [ "https://raw.githubusercontent.com/langchain-ai/langchain/master/docs/docs_skeleton.json" ] for url in sample_urls: response = requests.get(url) filename = os.path.basename(url) file_path = os.path.join(data_dir, filename) with open(file_path, "wb") as f: f.write(response.content) print(f"下载完成: {filename}") def _load_and_process_documents(self) -> List[Document]: """加载并处理文档""" documents = [] data_dir = self.config["data_dir"] # 支持多种文件格式 for filename in os.listdir(data_dir): file_path = os.path.join(data_dir, filename) if filename.endswith(".json") or filename.endswith(".jsonl"): documents.extend(self._load_json_documents(file_path)) elif filename.endswith(".txt"): documents.extend(self._load_text_documents(file_path)) if not documents: raise ValueError(f"在 {data_dir} 中没有找到可处理的文档") # 文本分块 return self._split_documents(documents) def _load_json_documents(self, file_path: str) -> List[Document]: """加载JSON或JSONL文档""" documents = [] with open(file_path, "r") as f: if file_path.endswith(".jsonl"): # 处理JSONL文件 for line in f: try: data = json.loads(line) doc = self._create_document_from_data(data) documents.append(doc) except json.JSONDecodeError: print(f"跳过无效的JSON行: {line.strip()}") else: # 处理JSON文件 try: data = json.load(f) if isinstance(data, list): for item in data: doc = self._create_document_from_data(item) documents.append(doc) elif isinstance(data, dict): doc = self._create_document_from_data(data) documents.append(doc) except json.JSONDecodeError: print(f"无效的JSON文件: {file_path}") return documents def _load_text_documents(self, file_path: str) -> List[Document]: """加载纯文本文档""" with open(file_path, "r", encoding="utf-8") as f: content = f.read() return [Document( page_content=content, metadata={ "source": file_path, "title": os.path.basename(file_path), "category": "text" } )] def _create_document_from_data(self, data: Dict) -> Document: """从数据创建文档对象""" return Document( page_content=data.get("content", data.get("text", "")), metadata={ "source": data.get("url", data.get("source", "")), "title": data.get("title", ""), "category": data.get("category", "unknown"), "timestamp": data.get("timestamp", "") } ) def _split_documents(self, documents: List[Document]) -> List[Document]: """分割文档为块""" text_splitter = RecursiveCharacterTextSplitter( chunk_size=self.config.get("chunk_size", 1000), chunk_overlap=self.config.get("chunk_overlap", 200), length_function=len ) return text_splitter.split_documents(documents) def _create_vector_index(self, documents: List[Document]): """创建FAISS向量索引""" # 创建向量数据库 self.vector_db = FAISS.from_documents( documents=documents, embedding=self.embedding_model ) # 保存索引 os.makedirs(os.path.dirname(self.config["index_path"]), exist_ok=True) self.vector_db.save_local(self.config["index_path"]) # 创建文档ID映射 for idx, doc in enumerate(documents): self.doc_id_map[idx] = { "source": doc.metadata["source"], "title": doc.metadata["title"], "category": doc.metadata["category"] } # 保存映射表 map_path = os.path.join(os.path.dirname(self.config["index_path"]), "doc_id_map.pkl") with open(map_path, "wb") as f: pickle.dump(self.doc_id_map, f) print(f"✅ 向量索引已创建并保存至 {self.config['index_path']}") def _load_vector_index(self): """加载现有的FAISS向量索引""" index_path = self.config["index_path"] # 加载向量数据库 self.vector_db = FAISS.load_local( folder_path=index_path, embeddings=self.embedding_model ) # 加载文档映射表 map_path = os.path.join(os.path.dirname(index_path), "doc_id_map.pkl") if os.path.exists(map_path): with open(map_path, "rb") as f: self.doc_id_map = pickle.load(f) print(f"✅ 文档映射表已加载") else: print("⚠️ 文档映射表未找到，将使用空映射") def ask_question(self, question: str, history: Optional[List] = None) -> Dict: """提问并获取答案""" if self.vector_db is None: raise ValueError("向量数据库未初始化") # 创建检索器 retriever = self.vector_db.as_retriever( search_kwargs={ "k": self.config.get("retrieval_top_k", 5), "score_threshold": self.config.get("score_threshold", 0.4) } ) # 创建问答链 qa_chain = self._create_qa_chain(retriever) # 执行问答 result = qa_chain({"query": question}) # 提取源文档 source_docs = result["source_documents"] doc_ids = [doc.metadata.get("doc_id", idx) for idx, doc in enumerate(source_docs)] # 获取完整上下文 full_contexts = [self.doc_id_map.get(did, {"title": "未知", "source": ""}) for did in doc_ids] return { "question": question, "answer": result["result"], "source_documents": source_docs, "full_contexts": full_contexts } def _create_qa_chain(self, retriever) -> Any: """创建问答链""" # 自定义提示模板 prompt_template = """ 基于以下上下文信息，请以专业、准确的方式回答用户的问题。如果上下文信息不足以回答问题，请如实告知用户。 上下文信息: {context} 问题: {question} 请提供详细的回答: """ QA_PROMPT = PromptTemplate( template=prompt_template, input_variables=["context", "question"] ) # 创建问答链 return RetrievalQA.from_chain_type( llm=self.llm, chain_type="stuff", retriever=retriever, return_source_documents=True, chain_type_kwargs={"prompt": QA_PROMPT} ) def add_document(self, content: str, metadata: Dict): """添加单个文档到索引""" if self.vector_db is None: raise ValueError("向量数据库未初始化") # 创建文档对象 doc = Document(page_content=content, metadata=metadata) # 分割文档 split_docs = self._split_documents([doc]) # 添加到索引 self.vector_db.add_documents(split_docs) # 更新文档映射 start_idx = max(self.doc_id_map.keys()) + 1 if self.doc_id_map else 0 for idx, doc in enumerate(split_docs): self.doc_id_map[start_idx + idx] = { "source": doc.metadata["source"], "title": doc.metadata["title"], "category": doc.metadata["category"] } print(f"✅ 添加了 {len(split_docs)} 个文档块") def save_index(self): """保存索引到磁盘""" if self.vector_db is None: raise ValueError("向量数据库未初始化") # 保存索引 self.vector_db.save_local(self.config["index_path"]) # 保存映射表 map_path = os.path.join(os.path.dirname(self.config["index_path"]), "doc_id_map.pkl") with open(map_path, "wb") as f: pickle.dump(self.doc_id_map, f) print(f"✅ 索引已保存至 {self.config['index_path']}") def create_default_config() -> Dict: """创建默认配置 - 使用智谱AI""" return { "data_dir": "data", "index_path": "index/faiss_index", "embedding_model": "sentence-transformers/all-mpnet-base-v2", "embedding_device": "cpu", "llm_provider": "zhipuai", # 默认使用智谱AI "zhipuai_api_key": "1fc6d23e95224503aa94bfcca6a31903.FXoiEbfDgymrE9FA", # 您的智谱AI密钥 "llm_model": "glm-4", # 智谱AI的GLM-4模型 "temperature": 0.7, "chunk_size": 1000, "chunk_overlap": 200, "retrieval_top_k": 5, "score_threshold": 0.4, "auto_download": True } def interactive_cli(): """交互式命令行界面 - 适配智谱AI""" config = create_default_config() # 设置智谱AI API密钥 api_key = input("请输入智谱AI API密钥（或直接回车使用默认值）: ") if api_key.strip(): config["zhipuai_api_key"] = api_key # 选择模型 model_choice = input("请选择模型 (1=GLM-4, 2=GLM-3-Turbo, 回车使用GLM-4): ") if model_choice == "2": config["llm_model"] = "glm-3-turbo" # 初始化RAG系统 rag = RAGSystem(config) # 交互问答 print("\nRAG系统已就绪（使用智谱AI），输入问题开始查询（输入'q'退出）") history = [] while True: question = input("\n>>> 问题: ") if question.lower() in ['q', 'quit', 'exit']: break try: result = rag.ask_question(question, history) # 显示结果 print(f"\n💡 答案: {result['answer']}") if result["full_contexts"]: print("\n📚 信息来源:") for i, ctx in enumerate(result["full_contexts"]): print(f" {i + 1}. {ctx['title']} ({ctx['category']})") print(f" 来源: {ctx['source']}") # 添加到历史 history.append({"question": question, "answer": result["answer"]}) except Exception as e: print(f"❌ 错误: {str(e)}") # 保存索引 rag.save_index() print("\n索引已保存，再见！") if __name__ == "__main__": interactive_cli() 】
07-08
我们正在解决一个具体的Python模块导入错误：ModuleNotFoundError: No module named 'langchain_community' 用户提到错误发生在导入`langchain.embeddings.HuggingFaceEmbeddings`时，这通常是因为langchain库的...
解决No module named ‘numpy._core.multiarray‘
m0_51965407的博客
12-17 2976
突然想到直接调用这个模块，就是题目中的这个模块进行调用，然后用ctrl+左键点击_core,结果如下,就会发现这个_core下面并没有叫multiarray的文件。由于项目要求，安装了numpy1.23.0 然后一直报错，然后看了很多文章说有残留其他版本。是有这么多文件，你会发现其中就有multiarray这个文件，点进去看，发现并没有和版本有关系。所以把这个文件直接复制到你出问题的环境的numpy._core，如图。把所有有的numpy版本全部删除，然后还是报题目中的错。
Original error was: No module named ‘numpy.core._multiarray_umath‘
风口IT猪的成长录
07-31 1万+
Original error was: No module named 'numpy.core._multiarray_umath'问题描述分析问题解决 问题描述 明明安装了numpy，而且经常使用并没有发生错误，却发生如下错误: No module named 'numpy.core._multiarray_umath 分析问题 可能是由于模型保存时出错，导致模型没有保存成功，此时删掉保存的模型即可 numpy版本太低 解决 显示版本 pip show numpy 升级版本 pip ins
Anaconda 导入模块报错 No module named 'numpy.core.info'
Avrilzyx的博客
12-06 3390
版本： 系统：win10 x64 Anaconda: 5.1.0 Python: 3.6.4 解决方法： numpy出问题，就重装或者更新numpy模块。 由于我使用的是Anaconda，所以在Anaconda Command Prompt中输入conda update numpy，等待numpy及其相关依赖安装完成即可，问题不再出现。 大致就是下图的情况： ...
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