aiagent/backend/app/api/agent_chat.py

"""
Agent 独立聊天 API — 不依赖工作流 DAG，直接与 Agent Runtime 对话。

POST /api/v1/agent-chat/bare
    {"message": "你好，帮我..."}
    → {"content": "...", "iterations": 3, "tool_calls": 5}
"""
from __future__ import annotations

import logging
from typing import Any, Dict, Optional
from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel

from app.core.database import get_db
from sqlalchemy.orm import Session
from app.api.auth import get_current_user
from app.models.user import User
from app.models.agent import Agent
from app.agent_runtime import (
    AgentRuntime,
    AgentConfig,
    AgentLLMConfig,
    AgentToolConfig,
)
from app.core.config import settings

logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/v1/agent-chat", tags=["agent-chat"])


class ChatRequest(BaseModel):
    message: str
    session_id: Optional[str] = None
    model: Optional[str] = None
    temperature: Optional[float] = None
    max_iterations: Optional[int] = None


class ChatResponse(BaseModel):
    content: str
    iterations_used: int
    tool_calls_made: int
    truncated: bool
    session_id: str
    agent_id: Optional[str] = None


@router.post("/bare", response_model=ChatResponse)
async def chat_bare(
    req: ChatRequest,
    current_user: User = Depends(get_current_user),
):
    """无需 Agent 配置，使用默认设置直接对话。"""
    config = AgentConfig(
        name="bare_agent",
        system_prompt="你是一个有用的AI助手。请使用可用工具来帮助用户完成任务。",
        llm=AgentLLMConfig(
            model=req.model or (
                "gpt-4o-mini" if settings.OPENAI_API_KEY and settings.OPENAI_API_KEY != "your-openai-api-key"
                else "deepseek-v4-flash"
            ),
            temperature=req.temperature or 0.7,
            max_iterations=req.max_iterations or 10,
        ),
        user_id=current_user.id,
    )
    runtime = AgentRuntime(config=config)
    result = await runtime.run(req.message)

    return ChatResponse(
        content=result.content,
        iterations_used=result.iterations_used,
        tool_calls_made=result.tool_calls_made,
        truncated=result.truncated,
        session_id=runtime.context.session_id,
    )


@router.post("/{agent_id}", response_model=ChatResponse)
async def chat_with_agent(
    agent_id: str,
    req: ChatRequest,
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db),
):
    """与指定的 Agent 对话。Agent 的工作流配置会用于构建 Runtime。"""
    agent = db.query(Agent).filter(Agent.id == agent_id).first()
    if not agent:
        raise HTTPException(status_code=404, detail="Agent 不存在")
    if agent.user_id and agent.user_id != current_user.id and current_user.role != "admin":
        raise HTTPException(status_code=403, detail="无权访问该 Agent")

    # 从 Agent 配置构建 Runtime
    wc = agent.workflow_config or {}
    nodes = wc.get("nodes", [])
    # 查找 agent 节点的配置（或第一个 llm 节点的配置）
    agent_node_cfg = _find_agent_node_config(nodes)

    config = AgentConfig(
        name=agent.name,
        system_prompt=agent_node_cfg.get("system_prompt") or agent.description or "你是一个有用的AI助手。",
        llm=AgentLLMConfig(
            provider=agent_node_cfg.get("provider", "openai"),
            model=req.model or agent_node_cfg.get("model", "gpt-4o-mini"),
            temperature=req.temperature or float(agent_node_cfg.get("temperature", 0.7)),
            max_iterations=req.max_iterations or int(agent_node_cfg.get("max_iterations", 10)),
        ),
        tools=AgentToolConfig(
            include_tools=agent_node_cfg.get("tools", []),
            exclude_tools=agent_node_cfg.get("exclude_tools", []),
        ),
        user_id=current_user.id,
    )

    runtime = AgentRuntime(config=config)
    result = await runtime.run(req.message)

    return ChatResponse(
        content=result.content,
        iterations_used=result.iterations_used,
        tool_calls_made=result.tool_calls_made,
        truncated=result.truncated,
        session_id=runtime.context.session_id,
        agent_id=agent_id,
    )


def _find_agent_node_config(nodes: list) -> Dict[str, Any]:
    """从工作流节点列表中查找第一个 agent 类型或 llm 类型的节点配置。"""
    if not nodes:
        return {}
    for node in nodes:
        typ = node.get("type", "")
        if typ in ("agent", "llm", "template"):
            return node.get("data") or {}
    return {}