feat: 新增 core/agents 模块和 nanobot
- 新增 agents 模块，包含 agent、api、skills 等子模块 - 新增 nanobot 项目，支持多渠道集成 - 添加启动脚本 start-all.bat 和 start-all.sh Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
482
core/agents/llm.py
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482
core/agents/llm.py
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"""LLM Provider base classes and implementations."""
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import asyncio
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import json
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import logging
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Any, AsyncGenerator
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logger = logging.getLogger(__name__)
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@dataclass
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class ToolCallRequest:
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"""A tool call request from the LLM."""
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id: str
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name: str
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arguments: dict[str, Any]
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def to_dict(self) -> dict[str, Any]:
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"""Serialize to dict."""
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return {
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"id": self.id,
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"type": "function",
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"function": {
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"name": self.name,
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"arguments": json.dumps(self.arguments, ensure_ascii=False),
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},
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}
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@dataclass
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class LLMResponse:
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"""Response from an LLM provider."""
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content: str | None
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tool_calls: list[ToolCallRequest] = field(default_factory=list)
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finish_reason: str = "stop"
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usage: dict[str, int] = field(default_factory=dict)
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reasoning_content: str | None = None # For reasoning models
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@property
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def has_tool_calls(self) -> bool:
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"""Check if response contains tool calls."""
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return len(self.tool_calls) > 0
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@dataclass
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class GenerationSettings:
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"""Default generation parameters for LLM calls."""
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temperature: float = 0.7
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max_tokens: int = 4096
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class LLMProvider(ABC):
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"""Abstract base class for LLM providers."""
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_CHAT_RETRY_DELAYS = (1, 2, 4)
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_TRANSIENT_ERROR_MARKERS = (
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"429", "rate limit", "500", "502", "503", "504",
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"overloaded", "timeout", "timed out", "connection",
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"server error", "temporarily unavailable",
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)
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def __init__(
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self,
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api_key: str | None = None,
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api_base: str | None = None,
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):
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self.api_key = api_key
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self.api_base = api_base
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self.generation = GenerationSettings()
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@staticmethod
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def _sanitize_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Sanitize messages to remove empty content that causes provider errors."""
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result = []
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for msg in messages:
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content = msg.get("content")
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if isinstance(content, str) and not content:
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clean = dict(msg)
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if msg.get("role") == "assistant" and msg.get("tool_calls"):
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clean["content"] = None
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else:
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clean["content"] = "(empty)"
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result.append(clean)
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continue
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result.append(msg)
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return result
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@abstractmethod
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async def chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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stream: bool = False,
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) -> LLMResponse | AsyncGenerator[str, None]:
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"""Send a chat completion request."""
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pass
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@classmethod
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def _is_transient_error(cls, content: str | None) -> bool:
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err = (content or "").lower()
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return any(marker in err for marker in cls._TRANSIENT_ERROR_MARKERS)
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async def chat_with_retry(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int | None = None,
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temperature: float | None = None,
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) -> LLMResponse:
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"""Call chat() with retry on transient provider failures."""
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max_tokens = max_tokens or self.generation.max_tokens
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temperature = temperature or self.generation.temperature
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messages = self._sanitize_messages(messages)
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for attempt, delay in enumerate(self._CHAT_RETRY_DELAYS, start=1):
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try:
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response = await self.chat(
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messages=messages,
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tools=tools,
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model=model,
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max_tokens=max_tokens,
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temperature=temperature,
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)
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except asyncio.CancelledError:
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raise
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except Exception as exc:
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response = LLMResponse(
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content=f"Error calling LLM: {exc}",
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finish_reason="error",
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)
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if response.finish_reason != "error":
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return response
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if not self._is_transient_error(response.content):
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return response
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logger.warning(
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"LLM transient error (attempt {}/{}), retrying in {}s",
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attempt,
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len(self._CHAT_RETRY_DELAYS),
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delay,
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)
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await asyncio.sleep(delay)
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# Last attempt
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try:
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return await self.chat(
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messages=messages,
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tools=tools,
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model=model,
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max_tokens=max_tokens,
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temperature=temperature,
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)
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except asyncio.CancelledError:
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raise
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except Exception as exc:
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return LLMResponse(
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content=f"Error calling LLM: {exc}",
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finish_reason="error",
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)
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@abstractmethod
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def get_default_model(self) -> str:
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"""Get the default model for this provider."""
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pass
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# OpenAI Provider
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class OpenAIProvider(LLMProvider):
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"""OpenAI LLM provider."""
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def __init__(
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self,
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api_key: str | None = None,
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api_base: str | None = None,
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):
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super().__init__(api_key, api_base)
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self._client = None
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@property
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def client(self):
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"""Lazy load OpenAI client."""
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if self._client is None:
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try:
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from openai import AsyncOpenAI
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self._client = AsyncOpenAI(
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api_key=self.api_key,
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base_url=self.api_base,
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)
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except ImportError:
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raise ImportError("openai package required: pip install openai")
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return self._client
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async def chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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stream: bool = False,
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) -> LLMResponse:
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model = model or self.get_default_model()
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params = {
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"model": model,
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"messages": messages,
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"max_tokens": max_tokens,
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"temperature": temperature,
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}
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if tools:
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params["tools"] = tools
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params["tool_choice"] = "auto"
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try:
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response = await self.client.chat.completions.create(**params)
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choice = response.choices[0]
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msg = choice.message
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tool_calls = []
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if msg.tool_calls:
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for tc in msg.tool_calls:
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args = tc.function.arguments
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if isinstance(args, str):
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args = json.loads(args)
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tool_calls.append(ToolCallRequest(
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id=tc.id,
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name=tc.function.name,
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arguments=args,
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))
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return LLMResponse(
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content=msg.content,
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tool_calls=tool_calls,
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finish_reason=choice.finish_reason,
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usage={
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"prompt_tokens": response.usage.prompt_tokens if response.usage else 0,
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"completion_tokens": response.usage.completion_tokens if response.usage else 0,
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},
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)
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except Exception as exc:
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logger.error(f"OpenAI API error: {exc}")
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return LLMResponse(
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content=f"Error: {exc}",
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finish_reason="error",
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)
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async def chat_stream(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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) -> AsyncGenerator[str, None]:
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"""Stream chat completions."""
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model = model or self.get_default_model()
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params = {
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"model": model,
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"messages": messages,
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"max_tokens": max_tokens,
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"temperature": temperature,
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"stream": True,
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}
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if tools:
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params["tools"] = tools
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try:
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response = await self.client.chat.completions.create(**params)
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async for chunk in response:
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if chunk.choices and chunk.choices[0].delta.content:
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yield chunk.choices[0].delta.content
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except Exception as exc:
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yield f"Error: {exc}"
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def get_default_model(self) -> str:
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return "gpt-4o"
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# Anthropic Provider
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class AnthropicProvider(LLMProvider):
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"""Anthropic Claude LLM provider."""
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def __init__(
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self,
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api_key: str | None = None,
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api_base: str | None = None,
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):
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super().__init__(api_key, api_base)
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self._client = None
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@property
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def client(self):
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"""Lazy load Anthropic client."""
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if self._client is None:
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try:
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from anthropic import AsyncAnthropic
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self._client = AsyncAnthropic(
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api_key=self.api_key,
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base_url=self.api_base,
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)
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except ImportError:
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raise ImportError("anthropic package required: pip install anthropic")
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return self._client
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def _convert_messages(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Convert messages to Anthropic format."""
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converted = []
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for msg in messages:
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role = msg.get("role")
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if role == "system":
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# Anthropic puts system in first user message
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content = msg.get("content", "")
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if converted and converted[0].get("role") == "user":
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converted[0]["content"] = f"{content}\n\n{converted[0].content}"
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else:
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converted.append({"role": "user", "content": f"{content}"})
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else:
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# Handle tool results
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if role == "tool":
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converted.append({
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"role": "user",
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"content": [
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{
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"type": "tool_result",
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"tool_use_id": msg.get("tool_call_id"),
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"content": msg.get("content", ""),
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}
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],
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})
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else:
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converted.append(msg)
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return converted
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def _convert_tools(self, tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Convert OpenAI-style tools to Anthropic format."""
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anthropic_tools = []
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for tool in tools:
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func = tool.get("function", {})
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anthropic_tools.append({
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"name": func.get("name"),
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"description": func.get("description"),
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"input_schema": func.get("parameters", {}),
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})
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return anthropic_tools
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async def chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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stream: bool = False,
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) -> LLMResponse:
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model = model or self.get_default_model()
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params = {
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"model": model,
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"max_tokens": max_tokens,
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"temperature": temperature,
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"messages": self._convert_messages(messages),
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}
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if tools:
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params["tools"] = self._convert_tools(tools)
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try:
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response = await self.client.messages.create(**params)
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tool_calls = []
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for tc in response.tool_calls:
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args = tc.input
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if isinstance(args, str):
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args = json.loads(args)
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tool_calls.append(ToolCallRequest(
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id=tc.id,
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name=tc.name,
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arguments=args,
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))
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# Get content text
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content_text = ""
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thinking = None
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if response.content:
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for block in response.content:
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if block.type == "text":
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content_text = block.text
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elif block.type == "thinking":
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thinking = block.thinking
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return LLMResponse(
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content=content_text,
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tool_calls=tool_calls,
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finish_reason="stop" if not tool_calls else "tool_use",
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reasoning_content=thinking,
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usage={
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"input_tokens": response.usage.input_tokens,
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"output_tokens": response.usage.output_tokens,
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},
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)
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except Exception as exc:
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logger.error(f"Anthropic API error: {exc}")
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return LLMResponse(
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content=f"Error: {exc}",
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finish_reason="error",
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)
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async def chat_stream(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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) -> AsyncGenerator[str, None]:
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"""Stream chat completions."""
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model = model or self.get_default_model()
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params = {
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"model": model,
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"max_tokens": max_tokens,
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"temperature": temperature,
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"messages": self._convert_messages(messages),
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"stream": True,
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}
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if tools:
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params["tools"] = self._convert_tools(tools)
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try:
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async with self.client.messages.stream(**params) as stream:
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async for text in stream.text_stream:
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yield text
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except Exception as exc:
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yield f"Error: {exc}"
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def get_default_model(self) -> str:
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return "claude-sonnet-4-20250514"
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# Provider factory
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class ProviderFactory:
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"""Factory for creating LLM providers."""
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_PROVIDERS = {
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"openai": OpenAIProvider,
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"anthropic": AnthropicProvider,
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}
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@classmethod
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def create(
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cls,
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provider: str,
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api_key: str | None = None,
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api_base: str | None = None,
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) -> LLMProvider:
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"""Create an LLM provider instance.
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Args:
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provider: Provider name (openai, anthropic)
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api_key: API key
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api_base: Optional base URL for API
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Returns:
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LLM provider instance
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"""
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provider_cls = cls._PROVIDERS.get(provider.lower())
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if not provider_cls:
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raise ValueError(f"Unknown provider: {provider}. Available: {list(cls._PROVIDERS.keys())}")
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return provider_cls(api_key=api_key, api_base=api_base)
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