"""general / social intents: web-search answers, general LLM answers, casual persona replies."""
import asyncio
import html
import json
import logging
import os
import re
import time
import uuid
from datetime import datetime
from typing import Any, Dict, List, Literal, Optional, Tuple
from urllib.parse import quote
import httpx
from pydantic import BaseModel, ConfigDict, Field
from src.config import get_settings, LIBBEE_VERSION
from src.agentcore.models import ChatMessage
from src.agentcore.constants import KU_MAIN_URL, _GUARDRAIL, _URL_INSTRUCTION
from src.agentcore.utils import _build_history_messages, _get_llm, _get_runtime_config
logger = logging.getLogger(__name__)
async def _libbee_casual_response(question: str, history: List[ChatMessage], model: str, hint: str = "") -> str:
"""LLM #1 — casual social response. v3.8.1: appends _GUARDRAIL + _URL_INSTRUCTION."""
if hint and len(hint.strip()) > 20:
return hint.strip()
settings = get_settings()
if not settings.openai_api_key and not settings.anthropic_api_key:
return "I'm LibBee, the Khalifa University Library AI Assistant — always happy to help!"
try:
llm = _get_llm(model, temperature=0.6, max_tokens=160)
msgs = [{"role": "system", "content": (
"You are LibBee, the Khalifa University Library AI Assistant. "
"Respond warmly and naturally in 1-3 sentences as a friendly librarian. "
"No markdown, no bullet points. "
+ _GUARDRAIL + "\n\n" + _URL_INSTRUCTION
)}]
_cfg = _get_runtime_config()
_ci = _cfg.get("custom_instructions", "").strip()
if _ci:
msgs[0]["content"] += " " + _ci
msgs.extend(_build_history_messages(history))
msgs.append({"role": "user", "content": question})
response = await llm.ainvoke(msgs)
return response.content.strip()
except Exception:
return "I'm LibBee, the Khalifa University Library AI Assistant — always happy to help!"
async def _llm_general_answer(question: str, history: List[ChatMessage], model: str) -> str:
"""LLM #3 — general factual answer. v3.8.1: appends _GUARDRAIL + _URL_INSTRUCTION."""
settings = get_settings()
if not settings.openai_api_key and not settings.anthropic_api_key:
return (
"I'm LibBee, the KU Library AI Assistant. "
f'For general university information, visit ku.ac.ae.'
)
system = (
"You are LibBee, the Khalifa University Library AI Assistant (Abu Dhabi, UAE). "
"Answer factually and concisely in 3-5 sentences. Use HTML
for line breaks. "
"KU means Khalifa University. If the topic is academic, briefly offer to help find library resources. "
"When referencing KU Library services, include these verified links where relevant: "
"Library homepage: https://library.ku.ac.ae | "
"Ask a Librarian: https://library.ku.ac.ae/AskUs | "
"Library hours: https://library.ku.ac.ae/hours | "
"ILL requests: https://library.ku.ac.ae/ill/ | "
"Study rooms: https://library.ku.ac.ae/rooms/ | "
"E-resources: https://library.ku.ac.ae/eresources | "
"Khazna repository: https://khazna.ku.ac.ae | "
"PRIMO discovery: https://khalifa.primo.exlibrisgroup.com/discovery/search?vid=971KUOSTAR_INST:KU. "
"Only use these exact URLs — never invent others. "
+ _GUARDRAIL + "\n\n" + _URL_INSTRUCTION
)
_cfg = _get_runtime_config()
_ci = _cfg.get("custom_instructions", "").strip()
if _ci:
system += " " + _ci
try:
llm = _get_llm(model, temperature=0.3, max_tokens=360)
msgs = [{"role": "system", "content": system}]
msgs.extend(_build_history_messages(history))
msgs.append({"role": "user", "content": question})
response = await llm.ainvoke(msgs)
return response.content.strip()
except Exception as e:
logger.error(f"_llm_general_answer error: {e}")
return f'Having trouble right now. Visit ku.ac.ae.'
async def _web_search_answer(question: str, history: List[ChatMessage], model: str) -> str:
"""LLM #4 — web search answer. v3.8.1: appends _GUARDRAIL + _URL_INSTRUCTION."""
settings = get_settings()
_WEB_SYSTEM = (
"You are LibBee, the Khalifa University Library AI Assistant (Abu Dhabi, UAE). "
"Answer using current web search. Be concise and factual. Use HTML
for line breaks. "
+ _GUARDRAIL + "\n\n" + _URL_INSTRUCTION
)
if model == "claude" and settings.anthropic_api_key:
try:
import anthropic
client = anthropic.Anthropic(api_key=settings.anthropic_api_key)
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=520,
system=_WEB_SYSTEM,
tools=[{"type": "web_search_20250305", "name": "web_search"}],
messages=[{"role": "user", "content": question}],
)
text = "".join(block.text for block in response.content if hasattr(block, "text"))
if text.strip():
return text.strip()
except Exception as e:
logger.warning(f"Claude web search failed: {e}")
if settings.openai_api_key:
try:
from openai import OpenAI
client = OpenAI(api_key=settings.openai_api_key)
response = client.responses.create(
model="gpt-4o-mini",
tools=[{"type": "web_search_preview"}],
instructions=_WEB_SYSTEM,
input=question,
)
text = ""
for item in response.output:
if hasattr(item, "content"):
for block in item.content:
if hasattr(block, "text"):
text += block.text
if text.strip():
return text.strip()
except Exception as e:
logger.warning(f"GPT web search failed: {e}")
return await _llm_general_answer(question, history, model)
def _general_follow_up(question: str) -> Tuple[str, List[dict]]:
return (
"Would you like a brief explanation, current web information, or KU Library resources on this topic?",
[
{"label": "Shorter explanation", "question": f"Give me a shorter explanation of {question}"},
{"label": "Current web information", "question": f"Show current web information on {question}"},
{"label": "Find KU Library resources", "question": f"Find KU Library resources on {question}"},
],
)
def _social_follow_up() -> Tuple[str, List[dict]]:
return (
"What would you like help with next?",
[
{"label": "Find articles on a topic", "question": "Find articles on a topic"},
{"label": "Check a library service", "question": "Check a library service"},
{"label": "Contact a librarian", "question": "Contact a librarian"},
],
)