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。
128 lines
4.6 KiB
Python
128 lines
4.6 KiB
Python
"""Config loader for per-sandbox Beaver runtime settings."""
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from __future__ import annotations
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import json
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import os
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from pathlib import Path
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from typing import Any
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from .schema import AgentDefaultsConfig, BeaverConfig, EmbeddingConfig, ProviderConfig
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def default_config_path(*, workspace: str | Path | None = None) -> Path:
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"""Resolve the default config path for a single-user sandbox instance.
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Priority:
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1. `BEAVER_CONFIG_PATH`
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2. `NANOBOT_CONFIG_PATH` for compatibility during migration
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3. `BEAVER_HOME/config.json`
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4. `NANOBOT_HOME/config.json` for migration compatibility
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5. `<workspace>/.beaver/config.json`
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6. `./.beaver/config.json`
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"""
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explicit = os.getenv("BEAVER_CONFIG_PATH") or os.getenv("NANOBOT_CONFIG_PATH")
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if explicit:
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return Path(explicit).expanduser()
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beaver_home = os.getenv("BEAVER_HOME")
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if beaver_home:
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return Path(beaver_home).expanduser() / "config.json"
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nanobot_home = os.getenv("NANOBOT_HOME")
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if nanobot_home:
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return Path(nanobot_home).expanduser() / "config.json"
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root = Path(workspace).expanduser() if workspace is not None else Path.cwd()
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return root / ".beaver" / "config.json"
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def load_config(
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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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) -> BeaverConfig:
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"""Load backend config; missing config is treated as an empty config."""
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path = Path(config_path).expanduser() if config_path is not None else default_config_path(workspace=workspace)
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if not path.exists():
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return BeaverConfig(config_path=path)
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data = json.loads(path.read_text(encoding="utf-8"))
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if not isinstance(data, dict):
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raise ValueError(f"Beaver config must be a JSON object: {path}")
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return BeaverConfig(
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agents_defaults=_parse_agent_defaults(data),
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providers=_parse_providers(data.get("providers")),
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embedding=_parse_embedding(data),
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config_path=path,
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)
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def _parse_agent_defaults(data: dict[str, Any]) -> AgentDefaultsConfig:
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agents = _as_dict(data.get("agents"))
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defaults = _as_dict(agents.get("defaults"))
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return AgentDefaultsConfig(
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workspace=_string(defaults.get("workspace") or data.get("workspace")),
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model=_string(defaults.get("model") or data.get("model")),
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provider=_string(defaults.get("provider") or data.get("provider")),
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embedding_model=_string(defaults.get("embeddingModel") or defaults.get("embedding_model") or data.get("embeddingModel")),
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)
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def _parse_providers(raw: Any) -> dict[str, ProviderConfig]:
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providers: dict[str, ProviderConfig] = {}
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for name, payload in _as_dict(raw).items():
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if not isinstance(payload, dict):
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continue
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providers[str(name)] = ProviderConfig(
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api_key=_string(payload.get("apiKey") or payload.get("api_key")),
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api_base=_string(payload.get("apiBase") or payload.get("api_base") or payload.get("baseUrl") or payload.get("base_url")),
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extra_headers=_string_dict(payload.get("extraHeaders") or payload.get("extra_headers") or payload.get("headers")),
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request_timeout_seconds=_float(
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payload.get("requestTimeoutSeconds")
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or payload.get("request_timeout_seconds")
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or payload.get("timeout")
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),
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)
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return providers
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def _parse_embedding(data: dict[str, Any]) -> EmbeddingConfig:
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raw = _as_dict(data.get("embedding") or data.get("embeddings"))
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return EmbeddingConfig(
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provider=_string(raw.get("provider") or raw.get("provider_name")),
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model=_string(raw.get("model") or data.get("embeddingModel") or data.get("embedding_model")),
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api_key=_string(raw.get("apiKey") or raw.get("api_key")),
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api_base=_string(raw.get("apiBase") or raw.get("api_base") or raw.get("baseUrl") or raw.get("base_url")),
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extra_headers=_string_dict(raw.get("extraHeaders") or raw.get("extra_headers") or raw.get("headers")),
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request_timeout_seconds=_float(
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raw.get("requestTimeoutSeconds") or raw.get("request_timeout_seconds") or raw.get("timeout")
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),
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)
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def _as_dict(value: Any) -> dict[str, Any]:
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return value if isinstance(value, dict) else {}
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def _string(value: Any) -> str | None:
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if value is None:
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return None
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value = str(value).strip()
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return value or None
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def _string_dict(value: Any) -> dict[str, str]:
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if not isinstance(value, dict):
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return {}
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return {str(key): str(item) for key, item in value.items() if item is not None}
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def _float(value: Any) -> float | None:
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if value in (None, ""):
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return None
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return float(value)
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