Eric Wang
8323d53a70
Architecture (mirrors workflow/20261016 chatNode structure): - events.py: client markers parsed to typed ClientEvent (sources verified against workflow JSON); plain text like '拍好了' is not an event - transitions.py: LEGAL_TRANSITIONS whitelist drafted from chatNode prompts and Condition edges (16 entries, each with source_ref); is_legal_transition rejects unlisted/terminal-violating moves - graph.py: multi-node graph (collect/single_photo/dual_photo/... matching workflow chatNodes), route_by_stage conditional edges, _fake_llm_propose simulates LLM stage-code proposals (assignment 4 replaces with real LLM); code only keeps context bookkeeping (no_reply_count) and validation - state.py: expanded domain fields (pending_question, accident/ person fields, clarify/no-reply counts, turn_patch, state_version) Key finding: 单车拍照#6/双车拍照#7 are browser_addon branches ending at 0000, so 2005/2015 allow both 300x (direct) and 0000. Verified: pytest 87 passed (24 transition/event tests newly green). Known limit: stage proposals come from a fake LLM; per-stage scripts and real LLM integration are assignments 4-5.
44 lines
1.9 KiB
Python
44 lines
1.9 KiB
Python
"""图状态定义。
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作业 1 的五个基础字段之上，作业 3 扩充领域状态：内部阶段、原始状态码、
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待回答问题、事故字段、两方人员字段、字段确认状态、澄清计数、无回复计数、
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本轮 patch 和业务版本。
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约束（迁移计划作业 3-2）：所有字段必须可序列化，
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不保存 HTTP client、数据库连接、音频或图片 base64。
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字段读取一律用 ``state.get(...)`` 并提供默认值——首轮会话没有
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历史 checkpoint，字段可能尚未初始化。
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"""
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from typing import TypedDict
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class AgentState(TypedDict):
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"""一次对话轮次在图中流动的状态。
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``turn_count``、``stage_code`` 等由同一 ``thread_id`` 的
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checkpoint 承载跨轮记忆；节点返回字段增量，LangGraph 合并写回。
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"""
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# ---- 基础（作业 1） ----
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session_id: str
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turn_count: int
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input_text: str
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response_text: str
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stage_code: str
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# ---- 客户端事件（作业 3） ----
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# ClientEvent.value 或 ""（普通输入）。由 normalize_input 每轮解析。
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client_event: str
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# ---- 领域状态（作业 3，作业 4—6 逐步充实） ----
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raw_status_code: str # 原始状态码（外部归一化前的内部码）
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pending_question: str # 当前待回答问题 ID（如 "driver_identity"）
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accident_fields: dict # 事故字段：jdcsl/ywrysw/sgfssj/...
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person_fields: dict # 两方人员字段：xm1/2、hphm1/2、sfzmwh1/2...
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confirm_status: dict # 字段确认状态：unknown/candidate/confirmed
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clarify_count: int # 澄清计数（阈值按阶段配置，作业 4-9）
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no_reply_count: int # 连续无回复计数（两次 → 0004）
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turn_patch: dict # 本轮输出字段差量；每轮开始清空（作业 3-7）
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state_version: int # 业务版本（作业 7 并发冲突检测）
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