246 lines
9.9 KiB
Python
246 lines
9.9 KiB
Python
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"""采集节点 LLM 接入（作业 4）。
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职责边界：
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- LLM 一次调用同时产出 ``<state>XXXX</state>正文``——
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不得为复判状态再调用第二次（计划作业 4-3）；
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- 代码校验前缀合法性、阶段枚举与白名单守卫，
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非法提议不写状态（受控修复重试：前缀/非法提议最多各修复一次）；
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- 字段候选经 fields.validate_candidate 校验后进入
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accident_fields / confirm_status / turn_patch（三态语义）；
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- 澄清/无回复计数是代码侧簿记，只作为 LLM 判断的输入，
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转不转人工由模型按提示词决定（代码不代替模型决定）。
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失败语义（计划作业 4-10）：前缀缺失/非法且修复失败时，抛出
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``CollectReplyError``——不提交业务状态、不泄漏原始模型内容；
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调用方（API 层）将其转换为可识别错误响应。
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"""
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import re
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from .fields import (
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FieldCandidate,
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FieldStatus,
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validate_candidate,
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)
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from .llm import LLMService
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from .prompts import COLLECTION, load_prompt
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from .state import AgentState
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from .transitions import COLLECT_STAGE, is_legal_transition
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STATE_PREFIX_RE = re.compile(r"^\s*<state>\s*(\d{4})\s*</state>")
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_STATE_TAG_RE = re.compile(r"</?state>")
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# 采集阶段澄清阈值（来源：prompts/collection.md）
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COLLECT_FAILURE_LIMIT = 5
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# 受控修复指令（计划作业 4-10）。追加在系统提示词之后，
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# 明确要求输出格式，供模型自我修正。
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_REPAIR_SUFFIX = (
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"\n\n【输出修复】你上一次的输出不合规：必须且只能以"
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" <state>四位状态码</state> 开头（如 <state>1002</state>），"
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"正文部分不得再出现 <state> 或 </state> 标签。"
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"请重新输出一条完全合规的回复。"
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)
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class CollectReplyError(Exception):
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"""采集回复不可用：前缀缺失/非法且修复失败。
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不携带原始模型内容，避免向上层泄漏。
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"""
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def parse_state_reply(text: str) -> tuple[str, str]:
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"""解析 ``<state>XXXX</state>`` 前缀，返回 (stage_code, 正文)。
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严格模式：无有效前缀、或正文再出现 state 标签（泄漏），
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都视为不合规并抛 ValueError，交由 run_collect 决定是否修复。
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"""
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match = STATE_PREFIX_RE.match(text)
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if not match:
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raise ValueError("回复缺少有效的 <state> 前缀")
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body = text[match.end():].strip()
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if _STATE_TAG_RE.search(body):
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raise ValueError("正文中再次出现 state 标签")
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return match.group(1), body
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def _try_parse(text: str) -> tuple[str, str] | None:
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"""宽松入口：不合规返回 None 而不是抛错（供修复流程判断）。"""
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try:
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return parse_state_reply(text)
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except ValueError:
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return None
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# 注入提示词的对话历史轮数上限（每轮 = user + assistant 两条）
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_HISTORY_TURNS = 10
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def _format_history(state: AgentState) -> str:
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"""把 dialogue 格式化为提示词可用的历史文本；为空返回“（无）”。"""
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dialogue = state.get("dialogue") or []
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if not dialogue:
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return "（首轮对话）"
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recent = dialogue[-_HISTORY_TURNS * 2:]
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lines = []
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for msg in recent:
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role = "用户" if msg.get("role") == "user" else "助手"
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content = str(msg.get("content", ""))
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# 助手历史带 <state> 标签，去掉标签避免干扰模型本轮输出格式
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content = STATE_PREFIX_RE.sub("", content).strip()
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lines.append(f"{role}：{content}")
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return "\n".join(lines)
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def _format_known_fields(state: AgentState) -> str:
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"""把已确认字段格式化为提示词摘要；为空返回“（暂无）”。"""
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fields = state.get("accident_fields") or {}
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if not fields:
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return "（暂无）"
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return "；".join(f"{key}={value}" for key, value in fields.items())
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def _build_user_message(state: AgentState) -> str:
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"""把图状态翻译成本轮用户消息（代替工作流的变量注入）。
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注入三部分上下文（计划作业 4-5“跳过已知项”）：
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- 对话历史：模型看不到 checkpoint，历史必须显式注入，
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否则每轮都以为首次提问、重复询问已知信息；
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- 已确认字段：信息齐全时模型按提示词直接分流（2000/2010），
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不追加核对问题；
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- 当前阶段与计数：供模型做转人工等判断。
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"""
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return (
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f"【对话历史】\n{_format_history(state)}\n"
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f"【已确认信息】\n{_format_known_fields(state)}\n"
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f"【当前对话阶段】{state.get('stage_code') or COLLECT_STAGE}\n"
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f"【连续无回复次数】{state.get('no_reply_count') or 0}\n"
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f"【用户最新输入】{state.get('input_text', '')}"
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)
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def _extract_candidates(state: AgentState, body: str) -> list[FieldCandidate]:
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"""从模型正文中提取字段候选（计划作业 4-4）。
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参考版返回空列表：字段提取依赖模型的结构化输出，留给真实
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LLM 接入后实现（同一回复内 JSON 解析优先；确需独立提取调用时，
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记录原因/调用次数/延迟）。validate_candidate 的字段级校验
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已由单元测试覆盖，此处先接通数据流。
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"""
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return []
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def _apply_candidates(
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state: AgentState, candidates: list[FieldCandidate]
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) -> tuple[dict, dict, dict]:
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"""把校验通过的候选写入正式字段。
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返回 (accident_fields, confirm_status, turn_patch)。
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只有 CONFIRMED 的候选才写入正式字段并进入本轮差量；
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CANDIDATE/UNKNOWN 只更新 confirm_status，供后续核实节点消费
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（计划作业 4-5 的三态语义）。
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"""
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accident_fields = dict(state.get("accident_fields") or {})
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confirm_status = dict(state.get("confirm_status") or {})
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patch: dict = {}
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for cand in candidates:
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validated = validate_candidate(cand)
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confirm_status[validated.key] = validated.status
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if validated.status is FieldStatus.CONFIRMED:
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accident_fields[validated.key] = validated.value
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patch[validated.key] = validated.value
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return accident_fields, confirm_status, patch
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def _bookkeeping(state: AgentState) -> tuple[int, int]:
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"""代码侧上下文簿记：无回复计数 + 澄清计数。
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返回 (no_reply_count, clarify_count)。注意这只是给 LLM 的
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判断输入，是否转人工由模型按提示词决定。
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clarify_count 的“计入”需要语义完整性判断（短促词/ASR 乱码
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不计入，见 prompts/collection.md）；参考版用“普通输入”做
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简化近似，接入真实 LLM 后按语义判定细化。
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"""
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event = state.get("client_event") or ""
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no_reply_count = state.get("no_reply_count") or 0
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if event == "no_reply":
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no_reply_count += 1
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elif (state.get("input_text") or "").strip():
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no_reply_count = 0
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clarify_count = state.get("clarify_count") or 0
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if not event and (state.get("input_text") or "").strip():
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clarify_count += 1
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return no_reply_count, clarify_count
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async def run_collect(state: AgentState, llm: LLMService) -> dict:
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"""采集节点核心：一轮对话的 LLM 接入（参考实现）。
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流程：一次模型调用 → 前缀校验（不合规修复一次）→
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白名单守卫（非法提议修复一次）→ 字段候选落库 → 返回状态增量。
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任何一步修复后仍不合规都抛 CollectReplyError：不提交业务状态、
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错误信息不携带原始模型内容。
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"""
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system_prompt = load_prompt(COLLECTION)
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user_message = _build_user_message(state)
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current = state.get("stage_code") or COLLECT_STAGE
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# 1) 第一次模型调用（计划作业 4-3：一次调用同时产出前缀与正文）
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raw = await llm.complete(system_prompt, user_message)
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parsed = _try_parse(raw)
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# 2) 前缀不合规：受控修复一次（最多多一次调用）
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if parsed is None:
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raw = await llm.complete(system_prompt + _REPAIR_SUFFIX, user_message)
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parsed = _try_parse(raw)
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if parsed is None:
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raise CollectReplyError(
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"模型回复缺少合法 <state> 前缀（修复一次后仍不合规）"
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)
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stage, _body = parsed
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# 3) 白名单守卫：LLM 提议的转换必须合法（计划作业 3 校验语义）
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if not is_legal_transition(current, stage):
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repair_hint = (
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"\n另外：你上一条提议的状态码不合规。"
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f"当前阶段是 {current}，只能选择合法的下一步状态码，"
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"不得跳步或使用未定义的状态码。"
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)
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raw = await llm.complete(
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system_prompt + _REPAIR_SUFFIX + repair_hint, user_message
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)
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parsed = _try_parse(raw)
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if parsed is None or not is_legal_transition(current, parsed[0]):
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raise CollectReplyError(
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f"模型提议了非法的阶段转换 {current}→{stage}"
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"（修复一次后仍不合规）"
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)
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stage, _body = parsed
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# 4) 字段候选提取与三态落库（提取本身见 _extract_candidates）
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accident_fields, confirm_status, patch = _apply_candidates(
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state, _extract_candidates(state, raw)
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)
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no_reply_count, clarify_count = _bookkeeping(state)
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# 5) 状态增量：response_text 保留完整前缀文本，API 层负责解析；
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# dialogue 追加本轮消息，供下一轮历史注入（计划作业 4-5）
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dialogue = list(state.get("dialogue") or [])
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dialogue.append({"role": "user", "content": state.get("input_text", "")})
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dialogue.append({"role": "assistant", "content": raw})
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return {
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"turn_count": (state.get("turn_count") or 0) + 1,
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"no_reply_count": no_reply_count,
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"clarify_count": clarify_count,
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"response_text": raw,
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"stage_code": stage,
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"raw_status_code": stage,
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"accident_fields": accident_fields,
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"confirm_status": confirm_status,
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"turn_patch": patch,
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"dialogue": dialogue,
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}
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