Ask question

Skill Makiato1999/agentic-travel/.claude/skills/ask-question

An LLM-powered multi-agent business travel planning system that integrates RAG knowledge retrieval, user preference memory, real-time information querying, and itinerary generation.

Install

npx -y skills add Makiato1999/agentic-travel --skill ask-question

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Use this skill when the user asks questions about travel policies, reimbursement, booking guides, city information, or any travel-related questions. Triggers when user asks "XX标准是多少", "如何XX", "XX怎么办", or any question format. This skill uses RAGKnowledgeAgent to retrieve answers from the knowledge base.

SKILL.md

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Ask Travel Question (RAG 知识库问答)

回答用户关于差旅政策、报销、预订、城市指南等的问题，使用 RAGKnowledgeAgent 从本地知识库检索并生成答案。

When to Use

用户问「XX标准是多少」「如何报销」「航班延误怎么办」等
需要基于企业/项目知识文档回答时

Agent

RAGKnowledgeAgent (agents/rag_knowledge_agent.py)
所有子 Agent 均使用 model 对象（非 model_config_name），需先创建 OpenAIChatModel
异步：reply() 为 async，需 await

初始化与调用

import asyncio
from agentscope.message import Msg
from agentscope.model import OpenAIChatModel
from config_agentscope import init_agentscope
from config import LLM_CONFIG
from agents.rag_knowledge_agent import RAGKnowledgeAgent
import json

async def ask_question(user_query: str):
    init_agentscope()
    model = OpenAIChatModel(
        model_name=LLM_CONFIG["model_name"],
        api_key=LLM_CONFIG["api_key"],
        client_kwargs={"base_url": LLM_CONFIG["base_url"], "timeout": 60},
        temperature=LLM_CONFIG.get("temperature", 0.7),
        max_tokens=LLM_CONFIG.get("max_tokens", 2000),
    )
    # 嵌入模型路径从 config.RAG_CONFIG 读取，默认 data/models/bge-small-zh-v1.5
    rag_agent = RAGKnowledgeAgent(
        name="RAGKnowledgeAgent",
        model=model,
        knowledge_base_path="./data/rag_knowledge",
        collection_name="business_travel_knowledge",
        top_k=3,
    )
    if not getattr(rag_agent, "initialized", True):
        return {"error": "RAG 未初始化，请先运行 python scripts/init_knowledge_base.py"}
    user_msg = Msg(name="user", content=user_query, role="user")
    result = await rag_agent.reply(user_msg)
    return json.loads(result.content) if isinstance(result.content, str) else result.content

# 使用
data = asyncio.run(ask_question("北京的住宿标准是多少？"))
# data: {"status": "success"|"no_knowledge", "answer": "...", "retrieved_documents": [...], "query": "..."}

返回格式

status: "success" 或 "no_knowledge"
answer: 自然语言答案
retrieved_documents: 列表，每项含 content, metadata
query: 用户问题

知识库

路径：data/rag_knowledge/（Milvus Lite）
源文档：data/documents/，共 8 类（差旅标准、报销、预订、FAQ、紧急处理、平台指南、城市指南、环保）
首次使用前需执行：python scripts/init_knowledge_base.py

回答生成指南

【回答要求】

必须严格基于知识库中的信息进行回答，严禁编造。
如果检索到的知识库信息与问题无关，或者信息不足以回答问题，请直接回答“知识库中没有相关信息”。
回答要准确、简洁、有条理。
如果有多个相关信息，可以分点说明。
如果用户问题同时包含“城市名”和“通用政策词”（如：报销时限、报销材料、审批流程、预订规则），应优先回答知识库中的通用差旅政策，不要因为没有检索到某城市的专属条款就直接说“没有相关信息”。
若知识库中没有该城市的专属差异，但有通用政策，应明确说明： “未检索到该城市的专属差异，以下回答基于通用差旅政策/通用报销规则。”
只有当问题明确要求“城市差异”且知识库中确实没有相关条款时，才回答“知识库中没有相关信息”。
对“报销时限”“报销材料”“审批流程”这类问题，优先输出通用规则；城市名仅作为补充上下文，而不是必须命中的限制条件。

请直接给出答案。