feat: integrate nanobot
This commit is contained in:
qixinbo
+124
-6
@@ -2,7 +2,7 @@ import asyncio
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import sys
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import os
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from pathlib import Path
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from typing import List
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from typing import List, Callable, Awaitable
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# Add project root to sys.path to allow importing nanobot
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# Assuming backend/app/core/nanobot.py -> backend/app/core -> backend/app -> backend -> root
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@@ -40,12 +40,24 @@ class NanobotIntegration:
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self.config: Config | None = None
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def initialize(self):
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# Set workspace path to backend/data/workspace
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workspace_path = Path(os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "data", "workspace"))
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workspace_path.mkdir(parents=True, exist_ok=True)
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# Override config workspace path via environment variable (since config is loaded from env)
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os.environ["NANOBOT_AGENTS__DEFAULTS__WORKSPACE"] = str(workspace_path)
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self.config = load_config()
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# No need to set self.config.workspace_path as it's a property that reads from agents.defaults.workspace
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self.bus = MessageBus()
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provider = self._make_provider(self.config)
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cron_store_path = get_cron_dir() / "jobs.json"
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self.cron = CronService(cron_store_path)
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cron_store_path = workspace_path / "cron"
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cron_store_path.mkdir(parents=True, exist_ok=True)
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cron_store_file = cron_store_path / "jobs.json"
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self.cron = CronService(cron_store_file)
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session_manager = SessionManager(self.config.workspace_path)
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@@ -75,6 +87,24 @@ class NanobotIntegration:
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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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@@ -120,11 +150,98 @@ class NanobotIntegration:
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if self.cron:
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self.cron.stop()
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async def process_message(self, message: str, session_id: str = "api:default", skill_ids: List[str] | None = None):
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async def process_message(
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self,
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message: str,
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session_id: str = "api:default",
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skill_ids: List[str] | None = None,
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model_id: str | None = None,
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on_progress: 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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await self.start()
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# Handle dynamic model switching
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# If model_id is provided, we need to fetch its config and create a temporary provider
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# or update the current agent's provider context for this request.
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# Since AgentLoop is stateful and tied to a provider, and we want to avoid recreating the whole agent for every request if possible,
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# but changing the provider/model is a significant change.
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#
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# A simpler approach for this "stateless API" usage pattern:
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# We can instantiate a lightweight version of the agent or provider just for this request if the model differs.
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# OR, since we are using `process_direct`, we can check if `AgentLoop` supports overriding the model.
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# Looking at `nanobot/agent/loop.py` (assumed), it uses `self.provider.completion(...)`.
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# Strategy:
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# 1. Load the model config from our JSON file using `model_id`.
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# 2. Construct a temporary provider instance for this model.
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# 3. Inject this provider into the agent for this request OR (cleaner) instantiate a temporary agent.
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# Instantiating a whole AgentLoop might be heavy due to MCP/Cron etc.
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# BUT `process_direct` is relatively isolated.
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#
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# Let's try to fetch the config first.
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current_provider = self.agent.provider
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temp_provider = None
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if model_id:
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from app.api.llm import _load_data
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llm_configs = _load_data()
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target_config = next((item for item in llm_configs if item["id"] == model_id), None)
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if target_config:
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# Map our DB config to Nanobot Provider
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# We reuse LiteLLMProvider for most cases as it is generic
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# Construct kwargs for LiteLLMProvider
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provider_name = target_config["provider"]
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model_name = target_config["model"]
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# Handle special case where provider might need to be part of model name for LiteLLM if not standard
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# But LiteLLMProvider handles `provider_name` arg.
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temp_provider = LiteLLMProvider(
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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=model_name,
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extra_headers=target_config.get("extra_headers"),
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provider_name=provider_name
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)
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# If we created a temp provider, we need to use it.
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# Since AgentLoop binds the provider, we might need to swap it temporarily or create a new AgentLoop.
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# Swapping is risky for concurrency.
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# Creating a new AgentLoop is safer but heavier.
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#
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# Optimization: If we are just doing a single turn chat (process_direct), maybe we can just use the provider directly?
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# But we want the Agent's reasoning loop (ReAct / tools).
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#
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# Let's try creating a temporary AgentLoop sharing the same components (bus, tools) but different provider.
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agent_to_use = self.agent
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if temp_provider:
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# Shallow copy or new instance
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# We need to pass all dependencies.
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agent_to_use = AgentLoop(
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bus=self.bus,
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provider=temp_provider,
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workspace=self.config.workspace_path,
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model=temp_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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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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restrict_to_workspace=self.config.tools.restrict_to_workspace,
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session_manager=self.agent.sessions,
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mcp_servers=self.config.tools.mcp_servers,
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channels_config=self.config.channels,
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)
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full_message = message
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if skill_ids:
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skills = load_skills()
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@@ -138,11 +255,12 @@ class NanobotIntegration:
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# Append user message after skills
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full_message = f"{skill_context}\n\n{message}"
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response = await self.agent.process_direct(
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response = await agent_to_use.process_direct(
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full_message,
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session_key=session_id,
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channel="api",
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chat_id=session_id
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chat_id=session_id,
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on_progress=on_progress,
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)
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return response
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