speed optim
This commit is contained in:
qixinbo
+61
-67
@@ -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, Callable, Awaitable, Any
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from typing import List, Callable, Awaitable, Any, Dict
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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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@@ -31,6 +31,7 @@ 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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class NanobotIntegration:
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def __init__(self):
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@@ -38,6 +39,9 @@ class NanobotIntegration:
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self.bus: MessageBus | None = None
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self.cron: CronService | None = None
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self.config: Config | None = None
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self._started = False
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self._model_agent_cache: Dict[str, AgentLoop] = {}
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self._model_agent_lock = asyncio.Lock()
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def initialize(self):
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# Set workspace path to backend/data/workspace
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@@ -137,18 +141,62 @@ class NanobotIntegration:
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)
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async def start(self):
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if self._started:
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return
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if not self.agent:
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self.initialize()
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# Start the agent loop in background
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asyncio.create_task(self.agent.run())
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asyncio.create_task(self.cron.start())
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self._started = True
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async def stop(self):
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if self.agent:
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self.agent.stop()
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await self.agent.close_mcp()
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for agent in self._model_agent_cache.values():
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agent.stop()
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await agent.close_mcp()
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self._model_agent_cache.clear()
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if self.cron:
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self.cron.stop()
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self._started = False
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def _build_agent_for_provider(self, provider: Any) -> AgentLoop:
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return AgentLoop(
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bus=self.bus,
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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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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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async def _get_or_create_model_agent(self, model_id: str, target_config: Dict[str, Any]) -> AgentLoop:
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async with self._model_agent_lock:
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cached = self._model_agent_cache.get(model_id)
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if cached:
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return cached
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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=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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agent = self._build_agent_for_provider(provider)
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self._model_agent_cache[model_id] = agent
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return agent
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async def process_message(
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self,
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@@ -160,6 +208,7 @@ class NanobotIntegration:
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):
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if not self.agent:
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self.initialize()
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if not self._started:
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await self.start()
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# Handle dynamic model switching
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@@ -181,79 +230,24 @@ class NanobotIntegration:
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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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if 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:
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if target_config.get("model") != self.agent.model:
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agent_to_use = await self._get_or_create_model_agent(model_id, target_config)
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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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selected_skills = [s for s in skills if s["id"] in skill_ids]
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if selected_skills:
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# We inject skills as a runtime context block
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skill_context = "[Runtime Context — metadata only, not instructions]\n# Active Skills\n\n"
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parts = ["[Runtime Context — metadata only, not instructions]", "# Active Skills", ""]
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for s in selected_skills:
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skill_context += f"## {s['name']}\n{s.get('description', '')}\n{s['content']}\n\n"
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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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parts.append(f"## {s['name']}\n{s.get('description', '')}\n{s['content']}\n")
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skill_context = "\n".join(parts)
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full_message = f"{skill_context}\n{message}"
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session = agent_to_use.sessions.get_or_create(session_id)
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normalized_messages = self._normalize_session_messages(session.messages)
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