116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
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"""
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Agent Runtime ⇄ WorkflowEngine 桥接。
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让 workflow_engine.execute_node() 通过寥寥几行调用 Agent Runtime。
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"""
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from __future__ import annotations
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import logging
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from typing import Any, Dict, Optional
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from app.agent_runtime.core import AgentRuntime
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from app.agent_runtime.schemas import (
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AgentConfig,
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AgentLLMConfig,
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AgentToolConfig,
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)
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logger = logging.getLogger(__name__)
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async def run_agent_node(
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node_data: Dict[str, Any],
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input_data: Dict[str, Any],
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execution_logger: Optional[Any] = None,
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user_id: Optional[str] = None,
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on_tool_executed: Optional[Any] = None,
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) -> Dict[str, Any]:
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"""
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在工作流中执行 Agent 节点。
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node_data 支持的字段：
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system_prompt — Agent 人格/指令（支持 {{variable}} 模板）
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tools — 可选工具白名单，默认全部
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exclude_tools — 可选工具黑名单
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model — 模型名称
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provider — 提供商（openai/deepseek）
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temperature — 温度
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max_iterations — ReAct 最大步数
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memory — 是否启用长期记忆
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input_data 中的 "query" 或 "input" 字段作为用户输入。
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"""
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# 1. 解析配置
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query = (
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input_data.get("query")
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or input_data.get("input")
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or input_data.get("text", "")
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)
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if not isinstance(query, str):
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query = str(query) if query else ""
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if not query:
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return {"output": "错误：Agent 节点未收到用户输入", "status": "error"}
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# 2. 解析 system_prompt（支持模板变量）
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raw_prompt = node_data.get("system_prompt", "你是一个有用的AI助手。")
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try:
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formatted_prompt = raw_prompt.format(**input_data)
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except (KeyError, ValueError):
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formatted_prompt = raw_prompt
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# 3. 构建 Agent 配置
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llm_config = AgentLLMConfig(
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provider=node_data.get("provider", "openai"),
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model=node_data.get("model", "gpt-4o-mini"),
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temperature=float(node_data.get("temperature", 0.7)),
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max_iterations=int(node_data.get("max_iterations", 10)),
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)
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# 允许节点内联 api_key/base_url
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if node_data.get("api_key"):
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llm_config.api_key = node_data["api_key"]
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if node_data.get("base_url"):
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llm_config.base_url = node_data["base_url"]
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agent_config = AgentConfig(
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name=node_data.get("label", "agent_node"),
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system_prompt=formatted_prompt,
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llm=llm_config,
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tools=AgentToolConfig(
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include_tools=node_data.get("tools", []),
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exclude_tools=node_data.get("exclude_tools", []),
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),
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memory={
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"enabled": node_data.get("memory", True),
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"persist_to_db": node_data.get("memory", True),
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},
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user_id=user_id,
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)
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# 4. 执行 Agent
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runtime = AgentRuntime(
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config=agent_config,
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execution_logger=execution_logger,
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on_tool_executed=on_tool_executed,
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)
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result = await runtime.run(query)
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# 5. 返回结果（兼容工作流引擎的输出格式）
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if result.success:
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return {
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"output": result.content,
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"status": "success",
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"agent_meta": {
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"iterations": result.iterations_used,
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"tool_calls": result.tool_calls_made,
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"truncated": result.truncated,
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},
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}
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else:
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return {
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"output": result.content,
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"status": "error",
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"error": result.error,
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}
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