steven_li
5ba5c7e4c1
集成新的Beaver后端服务到应用实例中，替换原有的nanobot实现。 主要变更包括： - 在Dockerfile和环境配置中添加Beaver相关路径和配置变量 - 更新工作目录结构从.nanobot到.beaver - 实现Beaver引擎加载器，支持配置文件加载和工具组装 - 添加内置工具如ListDirectoryTool、ReadFileTool、SearchFilesTool - 更新消息处理流程，支持通道适配器和网关模式 - 重构技能系统，支持显式工具提示和嵌入式检索 - 改进错误处理和生命周期管理 此变更使应用实例能够使用统一的Beaver后端进行AI代理运行时管理。
209 lines
7.3 KiB
Python
209 lines
7.3 KiB
Python
"""FastAPI app factory for Beaver."""
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from __future__ import annotations
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from collections.abc import AsyncIterator, Callable
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from contextlib import asynccontextmanager, suppress
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Any
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from beaver.services.agent_service import AgentService
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from .deps import get_agent_service
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from .schemas import WebChatRequest, WebChatResponse, WebErrorResponse, WebStatusResponse
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try:
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from fastapi import FastAPI, HTTPException, Request
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except ModuleNotFoundError: # pragma: no cover - fallback for skeleton-only environments
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class HTTPException(Exception):
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"""Minimal fallback exception matching FastAPI's constructor shape."""
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def __init__(self, status_code: int, detail: str) -> None:
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super().__init__(detail)
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self.status_code = status_code
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self.detail = detail
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class Request: # type: ignore[override]
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"""Fallback request shim used only for import-time compatibility."""
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def __init__(self, app: Any) -> None:
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self.app = app
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class FastAPI: # type: ignore[override]
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"""Small fallback shim so the package can import before dependencies are installed."""
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def __init__(self, *, title: str, lifespan: Callable[..., Any] | None = None) -> None:
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self.title = title
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self.lifespan = lifespan
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self.state = SimpleNamespace()
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def get(self, _path: str, **_kwargs: Any) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
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def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
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return func
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return decorator
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def post(self, _path: str, **_kwargs: Any) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
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def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
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return func
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return decorator
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@asynccontextmanager
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async def _app_lifespan(
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app: FastAPI,
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*,
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workspace: str | Path | None,
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config_path: str | Path | None,
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service: AgentService | None,
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manage_service_lifecycle: bool | None,
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shutdown_timeout_seconds: float | None,
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shutdown_force: bool,
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) -> AsyncIterator[None]:
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"""把 Web app 接到 AgentService lifecycle 上。"""
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attached_service = service or AgentService(workspace=workspace, config_path=config_path)
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owns_service = manage_service_lifecycle if manage_service_lifecycle is not None else service is None
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app.state.agent_service = attached_service
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started = False
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if owns_service:
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try:
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await attached_service.start()
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started = True
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except BaseException:
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with suppress(BaseException):
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if attached_service.is_running:
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await attached_service.shutdown(
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timeout_seconds=shutdown_timeout_seconds,
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force=shutdown_force,
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)
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else:
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attached_service.close()
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raise
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try:
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yield
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finally:
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if owns_service and started:
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await attached_service.shutdown(
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timeout_seconds=shutdown_timeout_seconds,
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force=shutdown_force,
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)
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def create_app(
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*,
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workspace: str | Path | None = None,
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config_path: str | Path | None = None,
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service: AgentService | None = None,
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manage_service_lifecycle: bool | None = None,
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shutdown_timeout_seconds: float | None = 5.0,
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shutdown_force: bool = True,
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) -> FastAPI:
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"""Create a Beaver web app hosted by AgentService running mode.
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默认 ownership 语义：
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- 未传 `service`：app 自己创建并接管其 lifecycle
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- 传入外部 `service`：默认只挂载，不自动 start/shutdown
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如果确实需要覆盖默认行为，可以显式传 `manage_service_lifecycle=True/False`。
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"""
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app = FastAPI(
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title="Beaver Backend",
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lifespan=lambda fastapi_app: _app_lifespan(
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fastapi_app,
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workspace=workspace,
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config_path=config_path,
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service=service,
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manage_service_lifecycle=manage_service_lifecycle,
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shutdown_timeout_seconds=shutdown_timeout_seconds,
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shutdown_force=shutdown_force,
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),
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)
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@app.get("/api/ping", response_model=WebStatusResponse)
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async def ping(request: Request) -> WebStatusResponse:
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agent_service = get_agent_service(request)
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running = agent_service.is_running
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return WebStatusResponse(
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status="ok",
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running=running,
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mode="running" if running else ("direct" if agent_service.has_loop else "idle"),
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)
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@app.post(
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"/api/chat",
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response_model=WebChatResponse,
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responses={
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400: {"model": WebErrorResponse},
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409: {"model": WebErrorResponse},
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503: {"model": WebErrorResponse},
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},
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)
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async def chat(request: Request, payload: WebChatRequest) -> WebChatResponse:
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agent_service = get_agent_service(request)
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message = payload.message.strip()
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if not message:
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raise HTTPException(status_code=400, detail="'message' is required")
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fallback_target = _model_dump(payload.fallback_target)
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auxiliary_target = _model_dump(payload.auxiliary_target)
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embedding_target = _model_dump(payload.embedding_target)
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try:
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result = await agent_service.submit_direct(
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message,
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session_id=payload.session_id,
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source="web",
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user_id=payload.user_id,
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title=payload.title,
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execution_context=payload.execution_context,
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model=payload.model,
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provider_name=payload.provider_name,
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embedding_model=payload.embedding_model,
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temperature=payload.temperature,
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max_tokens=payload.max_tokens,
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max_tool_iterations=payload.max_tool_iterations,
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fallback_target=fallback_target,
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auxiliary_target=auxiliary_target,
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embedding_target=embedding_target,
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)
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except ValueError as exc:
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raise HTTPException(status_code=400, detail=str(exc)) from exc
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except RuntimeError as exc:
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detail = str(exc)
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if "requires an active run() loop" in detail or "not ready" in detail:
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status_code = 503
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elif "submit_direct" in detail or "running" in detail:
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status_code = 409
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else:
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status_code = 503
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raise HTTPException(status_code=status_code, detail=detail) from exc
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return WebChatResponse(
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session_id=result.session_id,
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run_id=result.run_id,
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output_text=result.output_text,
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finish_reason=result.finish_reason,
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tool_iterations=result.tool_iterations,
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provider_name=result.provider_name,
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model=result.model,
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usage=result.usage,
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)
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return app
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def _model_dump(value: Any) -> dict[str, Any] | None:
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"""兼容 Pydantic v1/v2 的最小导出辅助。"""
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if value is None:
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return None
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if hasattr(value, "model_dump"):
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return value.model_dump(exclude_none=True)
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if hasattr(value, "dict"):
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return value.dict(exclude_none=True)
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return dict(value)
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