cfc/cfc-langgraph/app/graphs/recommend_graph.py

from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage
from app.tools.product_tools import search_product_by_keyword, search_activity_by_keyword, search_article_by_keyword
from app.config import settings
from app.monitoring import monitor_agent
import json
import logging

logger = logging.getLogger(__name__)

SYSTEM_PROMPT = """你是一个儿童成长营养推荐助手。根据用户的需求和营养标签, 推荐合适的商品、活动或文章。

推荐原则:
1. 首先尝试使用搜索工具查找匹配的内容
2. 如果搜索结果为空, 基于你的知识给出建议
3. 每项推荐必须附带推荐理由
4. 以 JSON 格式输出推荐结果

输出格式:
{
    "items": [
        {
            "source": "tool" 或 "knowledge",
            "type": "product" / "activity" / "article",
            "id": 数字,
            "name": "名称",
            "description": "描述",
            "reason": "为什么推荐这个"
        }
    ]
}
"""


class RecommendAgent:
    def __init__(self):
        self.llm = ChatOpenAI(
            model=settings.llm_model,
            api_key=settings.llm_api_key,
            base_url=settings.llm_base_url,
            temperature=settings.llm_temperature,
        )
        self.tools = [
            search_product_by_keyword,
            search_activity_by_keyword,
            search_article_by_keyword,
        ]
        self.llm_with_tools = self.llm.bind_tools(self.tools)

    @monitor_agent("recommend")
    async def run(self, query: str, tags: list[str], limit: int = 5) -> dict:
        """执行推荐 Agent, 返回推荐结果"""
        # 如果传入了 tags, 构造搜索关键词
        search_query = query or " ".join(tags)

        messages = [
            SystemMessage(content=SYSTEM_PROMPT),
            HumanMessage(content=f"用户需求: {search_query}\n最大返回数量: {limit}\n请搜索并推荐合适的内容。"),
        ]

        # LangChain Tool calling 自动完成: LLM 决定调哪个 Tool → 工具返回结果 → LLM 组织回答
        response = await self.llm_with_tools.ainvoke(messages)

        # 尝试解析 JSON 输出
        content = response.content
        try:
            # 提取 JSON 块
            if "```json" in content:
                json_str = content.split("```json")[1].split("```")[0].strip()
            elif "```" in content:
                json_str = content.split("```")[1].split("```")[0].strip()
            else:
                json_str = content.strip()
            result = json.loads(json_str)
            return result
        except (json.JSONDecodeError, IndexError):
            # 非 JSON 输出, 包装为文本回答
            logger.warning("Agent 输出非 JSON, raw: %s", content[:200])
            return {"items": [], "text": content}