DESKTOP-72TV0V4\caoxiaozhu
5c435ab21e
- Add run_stream method to AgentCore for streaming output - Add base_url parameter to LLM clients for OpenRouter support - Add xbot module for new agent implementation - Refactor Chat.vue into composable + components (ChatHeader, ChatMessage, ChatInput, ChatSidebar, ChatAgentSelector) - Add ChatStream handler for SSE streaming in Go server - Add UseXBot field to chat request Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
137 lines
4.2 KiB
Python
137 lines
4.2 KiB
Python
"""
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Anthropic LLM 实现
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"""
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import os
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from typing import Dict, Any, List, Optional
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from anthropic import AsyncAnthropic
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class AnthropicLLM:
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"""Anthropic Claude LLM"""
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def __init__(self, model_name: str = "claude-3-sonnet-20240229", api_key: Optional[str] = None, base_url: Optional[str] = None):
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self.model_name = model_name
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# 支持自定义 base_url（如 OpenRouter）
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if base_url:
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self.client = AsyncAnthropic(
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api_key=api_key or os.getenv("ANTHROPIC_API_KEY", ""),
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base_url=base_url
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)
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else:
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self.client = AsyncAnthropic(
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api_key=api_key or os.getenv("ANTHROPIC_API_KEY", "")
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)
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async def decide(self, prompt: str) -> Dict[str, Any]:
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"""
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LLM 决策 - 判断是否需要调用技能
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Args:
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prompt: 完整的 Prompt
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Returns:
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Dict: 包含 needs_skill, tool_calls, response 等
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"""
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system_prompt = """你是一个智能助手。请分析用户请求，判断是否需要调用工具来回答问题。
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如果需要调用工具，请按以下格式返回 JSON：
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{"needs_skill": true, "tool_calls": [{"skill_id": "工具名称", "parameters": {"参数": "值"}, "reason": "调用原因"}]}
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如果不需要调用工具，请返回：
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{"needs_skill": false, "response": "直接回答用户的内容"}
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请只返回 JSON，不要其他内容。"""
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try:
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response = await self.client.messages.create(
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model=self.model_name,
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max_tokens=2000,
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system=system_prompt,
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messages=[{"role": "user", "content": prompt}]
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)
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content = response.content[0].text
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# 尝试解析 JSON
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import json
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try:
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result = json.loads(content)
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return result
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except json.JSONDecodeError:
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return {
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"needs_skill": False,
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"response": content
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}
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except Exception as e:
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return {
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"needs_skill": False,
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"response": f"LLM 调用失败: {str(e)}"
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}
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async def generate(self, prompt: str, tool_results: List[Dict]) -> str:
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"""
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生成回复
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Args:
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prompt: 完整的 Prompt
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tool_results: 工具调用结果
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Returns:
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str: 生成的回复
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"""
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user_message = prompt
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# 添加工具结果作为上下文
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if tool_results:
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tool_context = "\n\n工具返回结果:\n"
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for result in tool_results:
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if result.get("success"):
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tool_context += f"- {result.get('skill_id')}: {result.get('result')}\n"
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user_message += tool_context
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try:
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response = await self.client.messages.create(
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model=self.model_name,
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max_tokens=4000,
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messages=[{"role": "user", "content": user_message}]
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)
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return response.content[0].text
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except Exception as e:
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return f"生成回复失败: {str(e)}"
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async def generate_stream(self, prompt: str, tool_results: List[Dict]):
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"""
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流式生成回复
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Args:
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prompt: 完整的 Prompt
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tool_results: 工具调用结果
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Yields:
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str: 生成的回复片段
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"""
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user_message = prompt
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# 添加工具结果作为上下文
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if tool_results:
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tool_context = "\n\n工具返回结果:\n"
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for result in tool_results:
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if result.get("success"):
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tool_context += f"- {result.get('skill_id')}: {result.get('result')}\n"
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user_message += tool_context
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try:
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async with self.client.messages.stream(
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model=self.model_name,
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max_tokens=4000,
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messages=[{"role": "user", "content": user_message}]
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) 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 e:
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yield f"生成回复失败: {str(e)}"
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