weijinglin commented on code in PR #302: URL: https://github.com/apache/incubator-hugegraph-ai/pull/302#discussion_r2454079766
########## hugegraph-llm/src/hugegraph_llm/flows/scheduler.py: ########## @@ -0,0 +1,189 @@ +# 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 GPipeline, GPipelineManager +from hugegraph_llm.flows import FlowName +from hugegraph_llm.flows.build_vector_index import BuildVectorIndexFlow +from hugegraph_llm.flows.common import BaseFlow +from hugegraph_llm.flows.build_example_index import BuildExampleIndexFlow +from hugegraph_llm.flows.graph_extract import GraphExtractFlow +from hugegraph_llm.flows.import_graph_data import ImportGraphDataFlow +from hugegraph_llm.flows.update_vid_embeddings import UpdateVidEmbeddingsFlow +from hugegraph_llm.flows.get_graph_index_info import GetGraphIndexInfoFlow +from hugegraph_llm.flows.build_schema import BuildSchemaFlow +from hugegraph_llm.flows.prompt_generate import PromptGenerateFlow +from hugegraph_llm.flows.rag_flow_raw import RAGRawFlow +from hugegraph_llm.flows.rag_flow_vector_only import RAGVectorOnlyFlow +from hugegraph_llm.flows.rag_flow_graph_only import RAGGraphOnlyFlow +from hugegraph_llm.flows.rag_flow_graph_vector import RAGGraphVectorFlow +from hugegraph_llm.state.ai_state import WkFlowInput +from hugegraph_llm.utils.log import log +from hugegraph_llm.flows.text2gremlin import Text2GremlinFlow + + +class Scheduler: + pipeline_pool: Dict[str, Any] + 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[FlowName.BUILD_VECTOR_INDEX] = { + "manager": GPipelineManager(), + "flow": BuildVectorIndexFlow(), + } + self.pipeline_pool[FlowName.GRAPH_EXTRACT] = { + "manager": GPipelineManager(), + "flow": GraphExtractFlow(), + } + self.pipeline_pool[FlowName.IMPORT_GRAPH_DATA] = { + "manager": GPipelineManager(), + "flow": ImportGraphDataFlow(), + } + self.pipeline_pool[FlowName.UPDATE_VID_EMBEDDINGS] = { + "manager": GPipelineManager(), + "flow": UpdateVidEmbeddingsFlow(), + } + self.pipeline_pool[FlowName.GET_GRAPH_INDEX_INFO] = { + "manager": GPipelineManager(), + "flow": GetGraphIndexInfoFlow(), + } + self.pipeline_pool[FlowName.BUILD_SCHEMA] = { + "manager": GPipelineManager(), + "flow": BuildSchemaFlow(), + } + self.pipeline_pool[FlowName.PROMPT_GENERATE] = { + "manager": GPipelineManager(), + "flow": PromptGenerateFlow(), + } + self.pipeline_pool[FlowName.TEXT2GREMLIN] = { + "manager": GPipelineManager(), + "flow": Text2GremlinFlow(), + } + # New split rag pipelines + self.pipeline_pool[FlowName.RAG_RAW] = { + "manager": GPipelineManager(), + "flow": RAGRawFlow(), + } + self.pipeline_pool[FlowName.RAG_VECTOR_ONLY] = { + "manager": GPipelineManager(), + "flow": RAGVectorOnlyFlow(), + } + self.pipeline_pool[FlowName.RAG_GRAPH_ONLY] = { + "manager": GPipelineManager(), + "flow": RAGGraphOnlyFlow(), + } + self.pipeline_pool[FlowName.RAG_GRAPH_VECTOR] = { + "manager": GPipelineManager(), + "flow": RAGGraphVectorFlow(), + } + self.pipeline_pool[FlowName.BUILD_EXAMPLES_INDEX] = { + "manager": GPipelineManager(), + "flow": BuildExampleIndexFlow(), + } + self.max_pipeline = max_pipeline + + # TODO: Implement Agentic Workflow + def agentic_flow(self): + pass + + def schedule_flow(self, flow_name: str, *args, **kwargs): + if flow_name not in self.pipeline_pool: + raise ValueError(f"Unsupported workflow {flow_name}") + manager: GPipelineManager = self.pipeline_pool[flow_name]["manager"] + flow: BaseFlow = self.pipeline_pool[flow_name]["flow"] + pipeline: GPipeline = 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(): + manager.add(pipeline) Review Comment: pipeline包含两种状态:WkInput,WkState，这两种状态一个在pipeline.run()启动的时候重置，一个在pipeline.run()结束时重置，即使Pipeline.run返回错误码，这两个状态也会重置，这点确认过了 -- This is an automated message from the Apache Git Service. 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