feat: 报销预审会话状态管理与工作台交互增强
- 新增差旅报销会话状态管理与对话模型重构 - 增强风险观测服务与运行时聊天上下文作用域 - 优化工作台图标资源、助理意图识别与摘要工具 - 完善报销创建视图样式与差旅详情页标准调整交互 - 补充风险观测、运行时聊天与报销端点测试覆盖
This commit is contained in:
caoxiaozhu
@@ -1,5 +1,6 @@
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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from http import HTTPStatus
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from time import monotonic, sleep
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@@ -61,6 +62,23 @@ class RuntimeChatResult:
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return [item.model_dump() for item in self.calls]
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@dataclass(slots=True)
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class RuntimeChatToolCall:
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name: str
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arguments: dict[str, Any]
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call_id: str | None = None
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raw_arguments: str = ""
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@dataclass(slots=True)
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class RuntimeToolCallResult:
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tool_call: RuntimeChatToolCall | None
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calls: list[RuntimeChatCallTrace]
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def calls_as_dicts(self) -> list[dict[str, Any]]:
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return [item.model_dump() for item in self.calls]
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class RuntimeChatService:
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def __init__(self, db: Session) -> None:
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self.db = db
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@@ -208,6 +226,131 @@ class RuntimeChatService:
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return RuntimeChatResult(None, calls)
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def complete_with_tool_call(
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self,
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messages: list[dict[str, Any]],
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*,
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tools: list[dict[str, Any]],
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tool_choice: dict[str, Any] | str | None = None,
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slot_priority: tuple[str, ...] = ("main", "backup"),
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max_tokens: int = 1200,
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temperature: float = 0.1,
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timeout_seconds: int | None = None,
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slot_timeouts: dict[str, int] | None = None,
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max_attempts: int | None = None,
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) -> RuntimeToolCallResult:
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configs: list[dict[str, str]] = []
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calls: list[RuntimeChatCallTrace] = []
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for slot in slot_priority:
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config = self._load_chat_slot(slot)
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if config is None:
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calls.append(
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RuntimeChatCallTrace(
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slot=slot,
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provider="",
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model="",
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attempt=0,
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status="skipped",
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skipped_reason="not_configured",
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)
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)
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continue
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configs.append(config)
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if not configs:
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return RuntimeToolCallResult(None, calls)
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resolved_timeout_seconds = timeout_seconds or DEFAULT_RUNTIME_CHAT_TIMEOUT_SECONDS
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resolved_slot_timeouts = dict(slot_timeouts or {})
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resolved_max_attempts = max_attempts or DEFAULT_RUNTIME_CHAT_RETRY_ATTEMPTS
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for attempt in range(1, resolved_max_attempts + 1):
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for config in configs:
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cache_key = self._build_slot_cache_key(config)
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if _slot_failure_until.get(cache_key, 0.0) > monotonic():
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logger.info(
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"Skip runtime chat tool slot=%s provider=%s because it is in cooldown",
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config["slot"],
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config["provider"],
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)
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calls.append(
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RuntimeChatCallTrace(
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slot=config["slot"],
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provider=config["provider"],
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model=config["model"],
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attempt=attempt,
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status="skipped",
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skipped_reason="cooldown",
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)
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)
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continue
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started = monotonic()
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try:
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tool_call = self._request_chat_tool_call(
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config,
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messages,
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tools=tools,
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tool_choice=tool_choice,
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max_tokens=max_tokens,
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temperature=temperature,
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timeout_seconds=resolved_slot_timeouts.get(
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config["slot"],
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resolved_timeout_seconds,
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),
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)
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duration_ms = int((monotonic() - started) * 1000)
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if tool_call is not None:
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_slot_failure_until.pop(cache_key, None)
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calls.append(
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RuntimeChatCallTrace(
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slot=config["slot"],
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provider=config["provider"],
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model=config["model"],
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attempt=attempt,
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status="succeeded",
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duration_ms=duration_ms,
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)
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)
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return RuntimeToolCallResult(tool_call, calls)
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calls.append(
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RuntimeChatCallTrace(
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slot=config["slot"],
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provider=config["provider"],
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model=config["model"],
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attempt=attempt,
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status="empty",
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duration_ms=duration_ms,
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error_message="模型未返回工具调用。",
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)
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)
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except Exception as exc:
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duration_ms = int((monotonic() - started) * 1000)
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_slot_failure_until[cache_key] = (
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monotonic() + DEFAULT_RUNTIME_CHAT_FAILURE_COOLDOWN_SECONDS
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)
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calls.append(
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RuntimeChatCallTrace(
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slot=config["slot"],
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provider=config["provider"],
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model=config["model"],
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attempt=attempt,
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status="failed",
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duration_ms=duration_ms,
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error_message=str(exc),
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)
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)
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logger.warning(
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"Runtime chat tool request failed slot=%s provider=%s attempt=%s/%s: %s",
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config["slot"],
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config["provider"],
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attempt,
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resolved_max_attempts,
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exc,
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)
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if attempt < resolved_max_attempts:
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sleep(DEFAULT_RUNTIME_CHAT_RETRY_DELAY_SECONDS)
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return RuntimeToolCallResult(None, calls)
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@staticmethod
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def _build_slot_cache_key(config: dict[str, str]) -> str:
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return "|".join(
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@@ -295,6 +438,51 @@ class RuntimeChatService:
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timeout_seconds=timeout_seconds,
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)
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def _request_chat_tool_call(
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self,
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config: dict[str, str],
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messages: list[dict[str, Any]],
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*,
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tools: list[dict[str, Any]],
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tool_choice: dict[str, Any] | str | None,
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max_tokens: int,
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temperature: float,
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timeout_seconds: int,
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) -> RuntimeChatToolCall | None:
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provider = config["provider"]
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endpoint = config["endpoint"]
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model = config["model"]
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api_key = config["apiKey"]
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if provider == "Azure OpenAI":
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return self._request_azure_openai_tool_call(
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endpoint=endpoint,
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model=model,
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api_key=api_key,
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messages=messages,
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tools=tools,
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tool_choice=tool_choice,
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max_tokens=max_tokens,
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temperature=temperature,
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timeout_seconds=timeout_seconds,
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)
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if provider == "Ollama":
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raise ConnectivityCheckError("Ollama 暂不支持小财管家 function calling。")
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return self._request_openai_compatible_tool_call(
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provider=provider,
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endpoint=endpoint,
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model=model,
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api_key=api_key,
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messages=messages,
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tools=tools,
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tool_choice=tool_choice,
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max_tokens=max_tokens,
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temperature=temperature,
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timeout_seconds=timeout_seconds,
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)
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def _request_openai_compatible(
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self,
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*,
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@@ -331,6 +519,46 @@ class RuntimeChatService:
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)
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return self._extract_openai_text(payload)
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def _request_openai_compatible_tool_call(
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self,
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*,
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provider: str,
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endpoint: str,
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model: str,
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api_key: str,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]],
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tool_choice: dict[str, Any] | str | None,
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max_tokens: int,
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temperature: float,
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timeout_seconds: int,
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) -> RuntimeChatToolCall | None:
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url = _ensure_path(_normalize_endpoint(endpoint), "chat/completions")
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request_payload: dict[str, Any] = {
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"model": model,
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"messages": messages,
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"tools": tools,
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"tool_choice": tool_choice or "auto",
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"max_tokens": max_tokens,
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"temperature": temperature,
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}
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if provider == "GLM":
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request_payload["thinking"] = {"type": "disabled"}
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status_code, payload = _send_json_request(
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"POST",
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url,
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headers=_build_headers(api_key=api_key, use_bearer=True),
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payload=request_payload,
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timeout_seconds=timeout_seconds,
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)
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if status_code >= HTTPStatus.BAD_REQUEST:
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raise ConnectivityCheckError(
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f"模型接口返回异常状态 {status_code}。",
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status_code=status_code,
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)
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return self._extract_openai_tool_call(payload)
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def _request_ollama(
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self,
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*,
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@@ -396,6 +624,41 @@ class RuntimeChatService:
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)
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return self._extract_openai_text(payload)
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def _request_azure_openai_tool_call(
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self,
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*,
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endpoint: str,
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model: str,
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api_key: str,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]],
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tool_choice: dict[str, Any] | str | None,
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max_tokens: int,
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temperature: float,
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timeout_seconds: int,
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) -> RuntimeChatToolCall | None:
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deployment_base = _build_azure_deployment_base(endpoint, model)
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url = f"{deployment_base}/chat/completions?api-version={AZURE_API_VERSION}"
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status_code, payload = _send_json_request(
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"POST",
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url,
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headers=_build_headers(api_key=api_key, use_bearer=False, use_api_key=True),
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payload={
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"messages": messages,
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"tools": tools,
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"tool_choice": tool_choice or "auto",
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"max_tokens": max_tokens,
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"temperature": temperature,
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},
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timeout_seconds=timeout_seconds,
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)
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if status_code >= HTTPStatus.BAD_REQUEST:
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raise ConnectivityCheckError(
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f"Azure OpenAI 返回异常状态 {status_code}。",
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status_code=status_code,
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)
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return self._extract_openai_tool_call(payload)
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@staticmethod
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def _extract_openai_text(payload: Any) -> str:
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if not isinstance(payload, dict):
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@@ -426,3 +689,74 @@ class RuntimeChatService:
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return text.strip()
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return ""
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@staticmethod
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def _extract_openai_tool_call(payload: Any) -> RuntimeChatToolCall | None:
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if not isinstance(payload, dict):
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return None
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choices = payload.get("choices")
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if not isinstance(choices, list) or not choices:
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return None
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first_choice = choices[0]
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if not isinstance(first_choice, dict):
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return None
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message = first_choice.get("message")
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if not isinstance(message, dict):
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return None
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tool_calls = message.get("tool_calls")
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if isinstance(tool_calls, list) and tool_calls:
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first_tool = tool_calls[0]
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if isinstance(first_tool, dict):
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function_payload = first_tool.get("function")
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if isinstance(function_payload, dict):
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return RuntimeChatService._build_runtime_tool_call(
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name=function_payload.get("name"),
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arguments=function_payload.get("arguments"),
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call_id=first_tool.get("id"),
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)
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function_call = message.get("function_call")
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if isinstance(function_call, dict):
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return RuntimeChatService._build_runtime_tool_call(
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name=function_call.get("name"),
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arguments=function_call.get("arguments"),
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call_id=None,
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)
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return None
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@staticmethod
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def _build_runtime_tool_call(
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*,
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name: Any,
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arguments: Any,
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call_id: Any,
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) -> RuntimeChatToolCall | None:
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tool_name = str(name or "").strip()
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if not tool_name:
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return None
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raw_arguments = ""
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if isinstance(arguments, dict):
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parsed_arguments = arguments
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raw_arguments = json.dumps(arguments, ensure_ascii=False)
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else:
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raw_arguments = str(arguments or "").strip()
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if not raw_arguments:
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parsed_arguments = {}
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else:
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parsed = json.loads(raw_arguments)
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if not isinstance(parsed, dict):
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raise ValueError("工具调用参数必须是 JSON object。")
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parsed_arguments = parsed
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return RuntimeChatToolCall(
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name=tool_name,
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arguments=parsed_arguments,
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call_id=str(call_id).strip() if call_id else None,
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raw_arguments=raw_arguments,
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)
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