chore: update nanobot to 0.1.4.post6
This commit is contained in:
qixinbo
+175
-77
@@ -15,14 +15,14 @@ if str(PROJECT_ROOT / "nanobot") not in sys.path:
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sys.path.append(str(PROJECT_ROOT / "nanobot"))
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from nanobot.agent.loop import AgentLoop
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from nanobot.bus.events import OutboundMessage
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from nanobot.bus.queue import MessageBus
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from nanobot.config.loader import load_config
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from nanobot.config.paths import get_cron_dir
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from nanobot.cron.service import CronService
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from nanobot.providers.openai_compat_provider import OpenAICompatProvider
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from nanobot.providers.openai_codex_provider import OpenAICodexProvider
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from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
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from nanobot.providers.litellm_provider import LiteLLMProvider
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from nanobot.providers.custom_provider import CustomProvider
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from nanobot.providers.base import GenerationSettings
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from nanobot.providers.registry import find_by_name
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from nanobot.session.manager import SessionManager
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from nanobot.config.schema import Config
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@@ -32,10 +32,9 @@ from nanobot.config.schema import Config
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# or just import here if we are confident.
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# Given the structure, importing here should be fine as long as skills.py doesn't import nanobot.py.
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from app.api.skills import load_skills
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from app.services.llm_cache import get_llm_configs
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from app.services.llm_cache import get_llm_configs, get_active_llm_config
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from app.core.data_root import get_workspace_root
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from app.core.streaming_provider import StreamingLiteLLMProvider
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class NanobotIntegration:
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def __init__(self):
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@@ -47,6 +46,75 @@ class NanobotIntegration:
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self._model_agent_cache: Dict[tuple[str | None, int | None], AgentLoop] = {}
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self._model_agent_lock = asyncio.Lock()
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@staticmethod
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def _normalize_config_value(value: Any) -> Any:
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if isinstance(value, str):
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stripped = value.strip()
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return stripped or None
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return value
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@staticmethod
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def _normalize_model_id(value: Any) -> str | None:
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if value is None:
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return None
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if isinstance(value, str):
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stripped = value.strip()
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return stripped or None
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return str(value)
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@staticmethod
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def _extract_response_text(response: Any) -> str:
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if response is None:
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return ""
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if isinstance(response, str):
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return response
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if isinstance(response, OutboundMessage):
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return response.content or ""
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if isinstance(response, dict):
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content = response.get("content")
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if isinstance(content, str):
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return content
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return str(content or "")
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content = getattr(response, "content", None)
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if isinstance(content, str):
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return content
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return str(response)
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def _need_custom_agent_for_target(self, target_config: Dict[str, Any]) -> bool:
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if not self.agent:
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return False
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provider = self.agent.provider
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target_model = self._normalize_config_value(target_config.get("model"))
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current_model = self._normalize_config_value(
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getattr(self.agent, "model", None) or getattr(provider, "default_model", None)
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)
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if target_model != current_model:
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return True
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target_provider = self._normalize_config_value(target_config.get("provider"))
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current_provider = self._normalize_config_value(getattr(provider, "_provider_name_override", None))
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if not current_provider:
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current_provider = self._normalize_config_value(getattr(getattr(provider, "_spec", None), "name", None))
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if not current_provider and current_model and self.config:
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current_provider = self._normalize_config_value(self.config.get_provider_name(current_model))
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if target_provider != current_provider:
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return True
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target_api_base = self._normalize_config_value(target_config.get("api_base"))
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current_api_base = self._normalize_config_value(getattr(provider, "api_base", None))
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if target_api_base != current_api_base:
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return True
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target_api_key = self._normalize_config_value(target_config.get("api_key"))
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current_api_key = self._normalize_config_value(getattr(provider, "api_key", None))
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if target_api_key != current_api_key:
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return True
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target_headers = target_config.get("extra_headers") or {}
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current_headers = getattr(provider, "extra_headers", None) or {}
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return target_headers != current_headers
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def initialize(self):
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workspace_path = get_workspace_root()
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workspace_path.mkdir(parents=True, exist_ok=True)
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@@ -74,12 +142,9 @@ class NanobotIntegration:
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provider=provider,
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workspace=self.config.workspace_path,
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model=self.config.agents.defaults.model,
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temperature=self.config.agents.defaults.temperature,
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max_tokens=self.config.agents.defaults.max_tokens,
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max_iterations=self.config.agents.defaults.max_tool_iterations,
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memory_window=self.config.agents.defaults.memory_window,
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reasoning_effort=self.config.agents.defaults.reasoning_effort,
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brave_api_key=self.config.tools.web.search.api_key or None,
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context_window_tokens=self.config.agents.defaults.context_window_tokens,
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web_search_config=self.config.tools.web.search,
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web_proxy=self.config.tools.web.proxy or None,
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exec_config=self.config.tools.exec,
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cron_service=self.cron,
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@@ -87,6 +152,7 @@ class NanobotIntegration:
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session_manager=session_manager,
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mcp_servers=self.config.tools.mcp_servers,
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channels_config=self.config.channels,
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timezone=self.config.agents.defaults.timezone,
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)
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self._register_custom_tools(self.agent)
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@@ -105,68 +171,94 @@ class NanobotIntegration:
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target_dir.mkdir(parents=True, exist_ok=True)
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shutil.copy2(source_skill_file, target_dir / "SKILL.md")
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def _register_custom_tools(self, agent: AgentLoop):
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def _register_custom_tools(self, agent: AgentLoop, project_id: int | None = None):
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from app.tools.nl2sql import NL2SQLTool
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from app.tools.visualization import VisualizationTool
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from app.tools.get_schema import GetDatabaseSchemaTool
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from app.tools.subagent import ListSubagentsTool, InvokeSubagentTool
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agent.tools.register(NL2SQLTool())
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agent.tools.register(VisualizationTool())
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agent.tools.register(GetDatabaseSchemaTool())
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if project_id is not None:
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agent.tools.register(ListSubagentsTool(project_id=project_id))
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agent.tools.register(InvokeSubagentTool(project_id=project_id))
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def _build_provider(
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self,
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model: str,
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provider_name: str | None,
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api_key: str | None,
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api_base: str | None,
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extra_headers: dict[str, Any] | None = None,
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):
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spec = find_by_name(provider_name) if provider_name else None
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backend = spec.backend if spec else "openai_compat"
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if backend == "openai_codex" or model.startswith("openai-codex/"):
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return OpenAICodexProvider(default_model=model)
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if backend == "azure_openai":
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if not api_key or not api_base:
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raise ValueError("Azure OpenAI requires api_key and api_base.")
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return AzureOpenAIProvider(
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api_key=api_key,
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api_base=api_base,
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default_model=model,
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)
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if backend == "anthropic":
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from nanobot.providers.anthropic_provider import AnthropicProvider
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return AnthropicProvider(
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api_key=api_key,
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api_base=api_base,
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default_model=model,
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extra_headers=extra_headers,
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)
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return OpenAICompatProvider(
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api_key=api_key,
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api_base=api_base,
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default_model=model,
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extra_headers=extra_headers,
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spec=spec,
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)
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def _make_provider(self, config: Config):
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# Logic adapted from nanobot/cli/commands.py
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model = config.agents.defaults.model
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provider_name = config.get_provider_name(model)
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p = config.get_provider(model)
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# Check if model is using an ID from our database configuration
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# This requires accessing the database or a cache of LLM configs
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# Since we are inside NanobotIntegration, we can try to load from the JSON file directly for simplicity
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# or rely on the caller to have injected the right config if they used environment variables.
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# But here we need to support dynamic loading based on the `model` string if it matches a stored config ID.
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# However, typically the `model` passed here comes from `config.agents.defaults.model`.
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# If we want to support dynamic switching per request, we should look at `agent.process_direct` arguments.
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# The `AgentLoop` initializes with a provider, but `LiteLLMProvider` might be able to handle dynamic models if we pass them.
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# BUT `LiteLLMProvider` is initialized with a specific `default_model`.
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# To support per-request model changes, we need to ensure the `provider` object or the `agent` can accept a model override.
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# `AgentLoop` methods like `process_direct` don't typically take a `model` argument to override the provider's default.
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# We might need to reinstantiate the provider or use a "DynamicProvider" that delegates based on context.
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# For now, let's assume standard initialization.
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# If the user provides a `model_id` in `process_message`, we will handle it there by creating a temporary provider/agent or updating the current one.
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if provider_name == "openai_codex" or model.startswith("openai-codex/"):
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return OpenAICodexProvider(default_model=model)
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if provider_name == "custom":
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return CustomProvider(
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api_key=p.api_key if p else "no-key",
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api_base=config.get_api_base(model) or "http://localhost:8000/v1",
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default_model=model,
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)
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if provider_name == "azure_openai":
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if not p or not p.api_key or not p.api_base:
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raise ValueError("Azure OpenAI requires api_key and api_base.")
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return AzureOpenAIProvider(
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api_key=p.api_key,
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api_base=p.api_base,
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default_model=model,
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)
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spec = find_by_name(provider_name)
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# Skip API key check for now to allow initialization without full config
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return StreamingLiteLLMProvider(
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provider = self._build_provider(
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model=model,
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provider_name=provider_name,
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api_key=p.api_key if p else None,
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api_base=config.get_api_base(model),
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default_model=model,
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extra_headers=p.extra_headers if p else None,
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provider_name=provider_name,
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)
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provider.generation = GenerationSettings(
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temperature=config.agents.defaults.temperature,
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max_tokens=config.agents.defaults.max_tokens,
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reasoning_effort=config.agents.defaults.reasoning_effort,
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)
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return provider
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def _make_provider_from_target(self, target_config: Dict[str, Any]):
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model = self._normalize_config_value(target_config.get("model")) or self.config.agents.defaults.model
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provider_name = self._normalize_config_value(target_config.get("provider"))
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if not provider_name and model and self.config:
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provider_name = self._normalize_config_value(self.config.get_provider_name(model))
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provider = self._build_provider(
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model=model,
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provider_name=provider_name,
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api_key=self._normalize_config_value(target_config.get("api_key")),
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api_base=self._normalize_config_value(target_config.get("api_base")),
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extra_headers=target_config.get("extra_headers"),
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)
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provider.generation = GenerationSettings(
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temperature=self.config.agents.defaults.temperature,
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max_tokens=self.config.agents.defaults.max_tokens,
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reasoning_effort=self.config.agents.defaults.reasoning_effort,
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)
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return provider
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async def start(self):
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if self._started:
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@@ -195,12 +287,9 @@ class NanobotIntegration:
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provider=provider,
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workspace=self.config.workspace_path,
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model=provider.default_model,
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temperature=self.config.agents.defaults.temperature,
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max_tokens=self.config.agents.defaults.max_tokens,
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max_iterations=self.config.agents.defaults.max_tool_iterations,
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memory_window=self.config.agents.defaults.memory_window,
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reasoning_effort=self.config.agents.defaults.reasoning_effort,
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brave_api_key=self.config.tools.web.search.api_key or None,
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context_window_tokens=self.config.agents.defaults.context_window_tokens,
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web_search_config=self.config.tools.web.search,
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web_proxy=self.config.tools.web.proxy or None,
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exec_config=self.config.tools.exec,
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cron_service=self.cron,
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@@ -208,23 +297,19 @@ class NanobotIntegration:
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session_manager=self.agent.sessions if self.agent else None,
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mcp_servers=mcp_servers if mcp_servers is not None else self.config.tools.mcp_servers,
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channels_config=self.config.channels,
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timezone=self.config.agents.defaults.timezone,
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)
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async def _get_or_create_model_agent(self, model_id: str | None, target_config: Dict[str, Any] | None, project_id: int | None = None) -> AgentLoop:
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cache_key = (model_id, project_id)
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normalized_model_id = self._normalize_model_id(model_id)
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cache_key = (normalized_model_id, project_id)
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async with self._model_agent_lock:
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cached = self._model_agent_cache.get(cache_key)
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if cached:
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return cached
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if target_config:
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provider = StreamingLiteLLMProvider(
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api_key=target_config.get("api_key"),
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api_base=target_config.get("api_base"),
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default_model=target_config.get("model"),
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extra_headers=target_config.get("extra_headers"),
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provider_name=target_config.get("provider"),
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)
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provider = self._make_provider_from_target(target_config)
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else:
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provider = self._make_provider(self.config)
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@@ -245,7 +330,7 @@ class NanobotIntegration:
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mcp_servers_dict[s["name"]] = cfg
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agent = self._build_agent_for_provider(provider, mcp_servers=mcp_servers_dict)
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self._register_custom_tools(agent)
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self._register_custom_tools(agent, project_id=project_id)
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self._model_agent_cache[cache_key] = agent
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return agent
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@@ -257,6 +342,7 @@ class NanobotIntegration:
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model_id: str | None = None,
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project_id: int | None = None,
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on_progress: Callable[[str], Awaitable[None]] | None = None,
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on_stream: Callable[[str], Awaitable[None]] | None = None,
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):
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if not self.agent:
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self.initialize()
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@@ -273,17 +359,28 @@ class NanobotIntegration:
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need_custom_agent = False
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target_config = None
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if model_id:
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selected_model_id = self._normalize_model_id(model_id)
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if selected_model_id:
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llm_configs = get_llm_configs()
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target_config = next((item for item in llm_configs if item.get("id") == model_id), None)
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if target_config and target_config.get("model") != self.agent.model:
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need_custom_agent = True
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target_config = next(
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(item for item in llm_configs if self._normalize_model_id(item.get("id")) == selected_model_id),
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None,
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)
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if target_config is None:
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active_config = get_active_llm_config()
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if active_config and active_config.get("id"):
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selected_model_id = self._normalize_model_id(active_config.get("id"))
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target_config = active_config
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if target_config and self._need_custom_agent_for_target(target_config):
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need_custom_agent = True
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if project_id is not None:
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need_custom_agent = True
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if need_custom_agent:
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agent_to_use = await self._get_or_create_model_agent(model_id, target_config, project_id)
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agent_to_use = await self._get_or_create_model_agent(selected_model_id, target_config, project_id)
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full_message = message
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# We no longer inject the full skill content into the user's message here,
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@@ -303,8 +400,9 @@ class NanobotIntegration:
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channel="api",
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chat_id=session_id,
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on_progress=on_progress,
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on_stream=on_stream,
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)
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return response
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return self._extract_response_text(response)
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def _normalize_session_messages(self, messages: List[Any]) -> List[dict[str, Any]]:
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normalized: List[dict[str, Any]] = []
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