feat: 增强日历功能并集成 AgentScope 代理服务
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qzl
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from core.agentscope.runtime.orchestrator import AgentScopeRuntimeOrchestrator
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from core.agentscope.runtime.react_runner import AgentScopeReActRunner
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__all__ = ["AgentScopeRuntimeOrchestrator", "AgentScopeReActRunner"]
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from __future__ import annotations
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from dataclasses import dataclass
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from core.agent.domain.system_agent_config import SystemAgentLLMConfig
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from models.llm import Llm
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from models.llm_factory import LlmFactory
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from models.system_agents import SystemAgents
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@dataclass(frozen=True)
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class RuntimeStageConfig:
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stage: str
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model_code: str
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provider_name: str
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llm_config: SystemAgentLLMConfig
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_LEGACY_AGENT_TYPE_TO_STAGE: dict[str, str] = {
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"INTENT_RECOGNITION": "intent",
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"TASK_EXECUTION": "execution",
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"RESULT_REPORTING": "report",
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}
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def _normalize_stage(raw_agent_type: str) -> str | None:
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lowered = raw_agent_type.strip().lower()
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if lowered in {"intent", "execution", "report"}:
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return lowered
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return _LEGACY_AGENT_TYPE_TO_STAGE.get(raw_agent_type.strip().upper())
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async def load_runtime_stage_configs(
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*, session: AsyncSession
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) -> dict[str, RuntimeStageConfig]:
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stmt = (
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select(
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SystemAgents.agent_type,
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Llm.model_code,
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LlmFactory.name,
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SystemAgents.config,
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)
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.join(Llm, Llm.id == SystemAgents.llm_id)
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.join(LlmFactory, LlmFactory.id == Llm.factory_id)
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.where(SystemAgents.status == "active")
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)
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rows = (await session.execute(stmt)).all()
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by_stage: dict[str, RuntimeStageConfig] = {}
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for agent_type, model_code, provider_name, raw_config in rows:
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stage = _normalize_stage(str(agent_type))
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if stage is None:
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continue
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if stage in by_stage:
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raise ValueError(f"duplicate active system agent config for stage: {stage}")
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llm_config = SystemAgentLLMConfig.model_validate(raw_config or {})
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by_stage[stage] = RuntimeStageConfig(
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stage=stage,
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model_code=str(model_code),
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provider_name=str(provider_name),
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llm_config=llm_config,
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)
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missing = [
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stage for stage in ("intent", "execution", "report") if stage not in by_stage
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]
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if missing:
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raise ValueError(
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f"missing active system agent configs for stages: {','.join(missing)}"
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)
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return by_stage
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from __future__ import annotations
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from typing import Any, Awaitable, Callable
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from uuid import UUID
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from sqlalchemy.ext.asyncio import AsyncSession
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from core.agent.domain.user_context import UserAgentContext
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from core.agentscope.prompts import (
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build_execution_user_prompt,
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build_intent_user_prompt,
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build_report_user_prompt,
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build_system_prompt,
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)
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from core.agentscope.runtime.config_loader import (
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RuntimeStageConfig,
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load_runtime_stage_configs,
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)
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from core.agentscope.runtime.react_runner import AgentScopeReActRunner
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from core.agentscope.schemas import (
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ExecutionBatchOutput,
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ExecutionTaskOutput,
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IntentOutput,
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ReportOutput,
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RuntimeOutput,
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)
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from core.agentscope.tools.toolkit import build_stage_toolkit
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def _tools_payload_from_schema(
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schemas: list[dict[str, object]],
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) -> list[dict[str, object]]:
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payload: list[dict[str, object]] = []
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for item in schemas:
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function = item.get("function")
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if not isinstance(function, dict):
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continue
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name = function.get("name")
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if not isinstance(name, str) or not name:
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continue
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description = function.get("description")
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parameters = function.get("parameters")
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payload.append(
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{
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"name": name,
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"description": description if isinstance(description, str) else "",
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"parameters": (
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parameters if isinstance(parameters, dict) else {"type": "object"}
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),
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}
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)
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return payload
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class AgentScopeRuntimeOrchestrator:
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_runner: Any
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_config_loader: Callable[[AsyncSession], Awaitable[dict[str, RuntimeStageConfig]]]
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def __init__(
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self,
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*,
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runner: Any | None = None,
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config_loader: Callable[
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[AsyncSession], Awaitable[dict[str, RuntimeStageConfig]]
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]
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| None = None,
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) -> None:
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self._runner = runner or AgentScopeReActRunner()
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if config_loader is not None:
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self._config_loader = config_loader
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else:
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self._config_loader = self._default_config_loader
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@staticmethod
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async def _default_config_loader(
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session: AsyncSession,
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) -> dict[str, RuntimeStageConfig]:
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return await load_runtime_stage_configs(session=session)
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async def run(
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self,
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*,
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session: AsyncSession,
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owner_id: UUID,
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user_token: str,
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user_context: UserAgentContext,
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user_input: str | list[dict[str, Any]],
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) -> RuntimeOutput:
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stage_config = await self._config_loader(session)
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intent_toolkit = build_stage_toolkit(
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stage="intent",
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session=session,
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owner_id=owner_id,
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user_token=user_token,
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enable_hitl=False,
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)
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intent_tools_schema = intent_toolkit.get_json_schemas()
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intent_prompt = build_system_prompt(
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stage="intent",
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user_context=user_context,
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tools=_tools_payload_from_schema(intent_tools_schema),
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)
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intent_payload = await self._runner.run_json_stage(
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stage_config=stage_config["intent"],
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agent_name="intent-agent",
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system_prompt=intent_prompt,
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user_prompt=build_intent_user_prompt(user_input=user_input),
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toolkit=intent_toolkit,
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)
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intent_output = IntentOutput.model_validate(intent_payload)
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execution_output: ExecutionBatchOutput | None = None
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if intent_output.route == "TASK_EXECUTION":
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execution_toolkit = build_stage_toolkit(
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stage="execution",
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session=session,
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owner_id=owner_id,
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user_token=user_token,
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enable_hitl=True,
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)
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execution_tools_schema = execution_toolkit.get_json_schemas()
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execution_prompt = build_system_prompt(
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stage="execution",
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user_context=user_context,
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tools=_tools_payload_from_schema(execution_tools_schema),
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)
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task_results: list[ExecutionTaskOutput] = []
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for task in intent_output.tasks:
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task_payload = await self._runner.run_json_stage(
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stage_config=stage_config["execution"],
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agent_name="execution-agent",
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system_prompt=execution_prompt,
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user_prompt=build_execution_user_prompt(
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task_id=task.task_id,
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task_title=task.title,
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task_objective=task.objective,
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user_input=user_input,
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intent_summary=intent_output.intent_summary,
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),
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toolkit=execution_toolkit,
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)
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if "task_id" not in task_payload:
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task_payload["task_id"] = task.task_id
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task_results.append(ExecutionTaskOutput.model_validate(task_payload))
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statuses = {item.status for item in task_results}
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if statuses == {"SUCCESS"}:
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overall_status = "SUCCESS"
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elif "FAILED" in statuses:
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overall_status = "PARTIAL" if "SUCCESS" in statuses else "FAILED"
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else:
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overall_status = "PARTIAL"
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execution_output = ExecutionBatchOutput(
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task_results=task_results,
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overall_status=overall_status,
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aggregate_summary="; ".join(
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item.execution_summary for item in task_results
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),
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)
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report_prompt = build_system_prompt(
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stage="report",
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user_context=user_context,
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tools=[],
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)
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report_payload = await self._runner.run_json_stage(
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stage_config=stage_config["report"],
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agent_name="report-agent",
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system_prompt=report_prompt,
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user_prompt=build_report_user_prompt(
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user_input=user_input,
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intent_payload=intent_output.model_dump(mode="json"),
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execution_payload=(
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execution_output.model_dump(mode="json")
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if execution_output
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else None
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),
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),
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toolkit=None,
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)
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report_output = ReportOutput.model_validate(report_payload)
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return RuntimeOutput(
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intent=intent_output,
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execution=execution_output,
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report=report_output,
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)
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@@ -0,0 +1,98 @@
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from __future__ import annotations
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import json
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from typing import Any, cast
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from core.agentscope.runtime.config_loader import RuntimeStageConfig
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from core.config.settings import config
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from core.logging import get_logger
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logger = get_logger("core.agentscope.runtime.react_runner")
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def _to_litellm_model(*, provider_name: str, model_code: str) -> str:
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normalized_model = model_code.strip()
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if "/" in normalized_model:
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return normalized_model
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return f"{provider_name.strip().lower()}/{normalized_model}"
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def _parse_json_text(raw_text: str) -> dict[str, Any]:
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text = raw_text.strip()
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if text.startswith("```"):
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text = text.strip("`")
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if text.startswith("json"):
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text = text[4:].strip()
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parsed = json.loads(text)
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if not isinstance(parsed, dict):
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raise ValueError("model output must be a JSON object")
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return cast(dict[str, Any], parsed)
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class AgentScopeReActRunner:
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def _build_model(self, *, stage_config: RuntimeStageConfig) -> Any:
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from agentscope.model import OpenAIChatModel
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from agentscope.types import JSONSerializableObject
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generate_kwargs: dict[str, JSONSerializableObject] = {
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"response_format": {"type": "json_object"},
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}
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if stage_config.llm_config.temperature is not None:
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generate_kwargs["temperature"] = stage_config.llm_config.temperature
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if stage_config.llm_config.max_tokens is not None:
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generate_kwargs["max_tokens"] = stage_config.llm_config.max_tokens
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if stage_config.llm_config.timeout_seconds is not None:
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generate_kwargs["timeout"] = stage_config.llm_config.timeout_seconds
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return OpenAIChatModel(
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model_name=_to_litellm_model(
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provider_name=stage_config.provider_name,
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model_code=stage_config.model_code,
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),
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api_key=config.litellm.api_key,
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stream=False,
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client_kwargs={"base_url": config.litellm.base_url},
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generate_kwargs=cast(dict[str, JSONSerializableObject], generate_kwargs),
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)
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async def run_json_stage(
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self,
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*,
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stage_config: RuntimeStageConfig,
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agent_name: str,
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system_prompt: str,
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user_prompt: str,
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toolkit: Any | None,
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) -> dict[str, Any]:
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from agentscope.agent import ReActAgent
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from agentscope.formatter import OpenAIChatFormatter
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from agentscope.memory import InMemoryMemory
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from agentscope.message import Msg
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agent = ReActAgent(
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name=agent_name,
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sys_prompt=system_prompt,
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model=self._build_model(stage_config=stage_config),
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formatter=OpenAIChatFormatter(),
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toolkit=toolkit,
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memory=InMemoryMemory(),
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max_iters=6,
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)
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try:
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response = await agent(Msg(name="user", content=user_prompt, role="user"))
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text_content = response.get_text_content() or "{}"
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return _parse_json_text(text_content)
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except json.JSONDecodeError as exc:
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logger.exception(
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"agentscope stage output is not valid json",
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stage=stage_config.stage,
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agent_name=agent_name,
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)
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raise RuntimeError("agent output format invalid") from exc
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except Exception as exc:
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logger.exception(
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"agentscope stage execution failed",
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stage=stage_config.stage,
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agent_name=agent_name,
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)
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raise RuntimeError("agent execution failed") from exc
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