imbajin commented on code in PR #301: URL: https://github.com/apache/incubator-hugegraph-ai/pull/301#discussion_r2447071277
########## hugegraph-llm/src/hugegraph_llm/flows/scheduler.py: ########## @@ -0,0 +1,90 @@ +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import threading +from typing import Dict, Any +from PyCGraph import GPipelineManager +from hugegraph_llm.flows.build_vector_index import BuildVectorIndexFlow +from hugegraph_llm.flows.common import BaseFlow +from hugegraph_llm.flows.graph_extract import GraphExtractFlow +from hugegraph_llm.utils.log import log + + +class Scheduler: + pipeline_pool: Dict[str, Any] = None + max_pipeline: int Review Comment: ⚠️ **Important: Unused parameter** The `max_pipeline` parameter is stored in `__init__` but never used anywhere in the class. The `GPipelineManager` is created without any size limits, so this parameter has no effect. **Recommendation:** 1. Either implement pipeline pool size limits using this parameter 2. Or remove it if not needed yet 3. Document the intended behavior for future implementation ########## hugegraph-llm/src/hugegraph_llm/flows/scheduler.py: ########## @@ -0,0 +1,90 @@ +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import threading +from typing import Dict, Any +from PyCGraph import GPipelineManager +from hugegraph_llm.flows.build_vector_index import BuildVectorIndexFlow +from hugegraph_llm.flows.common import BaseFlow +from hugegraph_llm.flows.graph_extract import GraphExtractFlow +from hugegraph_llm.utils.log import log + + +class Scheduler: + pipeline_pool: Dict[str, Any] = None + max_pipeline: int + + def __init__(self, max_pipeline: int = 10): + self.pipeline_pool = {} + # pipeline_pool act as a manager of GPipelineManager which used for pipeline management + self.pipeline_pool["build_vector_index"] = { + "manager": GPipelineManager(), + "flow": BuildVectorIndexFlow(), + } + self.pipeline_pool["graph_extract"] = { + "manager": GPipelineManager(), + "flow": GraphExtractFlow(), + } + self.max_pipeline = max_pipeline + + # TODO: Implement Agentic Workflow + def agentic_flow(self): + pass + + def schedule_flow(self, flow: str, *args, **kwargs): + if flow not in self.pipeline_pool: + raise ValueError(f"Unsupported workflow {flow}") + manager = self.pipeline_pool[flow]["manager"] + flow: BaseFlow = self.pipeline_pool[flow]["flow"] + pipeline = manager.fetch() + if pipeline is None: + # call coresponding flow_func to create new workflow + pipeline = flow.build_flow(*args, **kwargs) + status = pipeline.init() + if status.isErr(): + error_msg = f"Error in flow init: {status.getInfo()}" + log.error(error_msg) + raise RuntimeError(error_msg) + status = pipeline.run() + if status.isErr(): + error_msg = f"Error in flow execution: {status.getInfo()}" + log.error(error_msg) + raise RuntimeError(error_msg) + res = flow.post_deal(pipeline) Review Comment: ⚠️ **Important: Inconsistent error handling** The code uses both `raise ValueError` and `raise RuntimeError` inconsistently. Also, the `post_deal` method can fail (e.g., JSON serialization errors) but is not wrapped in error handling. **Recommendation:** 1. Define a clear exception hierarchy for the flows module 2. Wrap `post_deal` in try-catch to handle serialization errors 3. Document which exceptions callers should expect -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
