[Refactor] Add Fixed Workflow Execution Engine: Flow, Node, and Scheduler Architecture

[weijinglin]

No description provided.

[Copilot]

Pull Request Overview

This pull request refactors the HugeGraph-AI workflow execution system by introducing a fixed workflow execution engine with a new scheduler-based architecture.

• Introduces new scheduler architecture with Flow, Node, and operator separation
• Upgrades project version from 1.5.0 to 1.7.0 across multiple modules
• Migrates existing workflows to use the new Node/Operator pattern for better modularity

Reviewed Changes

Copilot reviewed 134 out of 135 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
pyproject.toml	Updates version to 1.7.0
spec/hugegraph-llm/fixed_flow/*.md	Design documents for new workflow architecture
hugegraph-llm/src/hugegraph_llm/flows/	New scheduler and flow implementations
hugegraph-llm/src/hugegraph_llm/nodes/	New Node layer for operator lifecycle management
hugegraph-llm/src/hugegraph_llm/state/ai_state.py	Workflow state management classes
hugegraph-python-client/	Code formatting improvements and version updates

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[imbajin]

⚠️ Important: 新增大量flow文件缺少单元测试覆盖

本PR新增了以下核心flow实现:

• RAGRawFlow, RAGVectorOnlyFlow, RAGGraphOnlyFlow, RAGGraphVectorFlow
• Text2GremlinFlow, BuildExampleIndexFlow
• 以及多个其他flow类

但从diff来看,缺少对应的测试文件。

建议:

1. 为每个flow类添加单元测试
2. 测试应覆盖:
   • 正常执行路径
   • 错误处理路径
   • 边界条件(空输入、超大输入等)
   • Pipeline状态转换
3. 集成测试验证flow之间的协作
4. 考虑添加性能基准测试

这对于如此大规模的架构重构至关重要!