File size: 4,499 Bytes
41fe3fc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | """Stage 2-3 of the pipeline: rule-based pre-classifier and the GPT-4o-mini intent classifier."""
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 (
CURRENT_INFO_RE,
GREETING_FOLLOWUP_RE,
HISTORY_WINDOW,
HOURS_RE,
KU_CAMPUS_RE,
LIBRARY_CUE_RE,
MEDICAL_KEYWORDS,
MEDICAL_SEARCH_RE,
RESEARCH_CUE_RE,
SOCIAL_RE,
SUMMARY_RE,
_CLASSIFIER_SYSTEM,
)
from src.agentcore.utils import _get_llm
logger = logging.getLogger(__name__)
def _is_summary_request(question: str) -> bool:
return bool(SUMMARY_RE.search(question or ""))
def _is_greeting_menu_followup(question: str, history: List[ChatMessage]) -> bool:
if not GREETING_FOLLOWUP_RE.match((question or "").strip()):
return False
for m in reversed(history):
if m.role == "assistant":
last = m.content.lower()
return "i'm libbee" in last or "are you looking for one of these" in last
return False
def _looks_library_hours_question(question: str) -> bool:
q = (question or "").strip().lower()
return bool(HOURS_RE.search(q)) and any(
kw in q for kw in (
"library", "campus", "habshan", "san", "hours", "open", "close",
"schedule", "till", "until", "today", "tomorrow", "friday",
"saturday", "sunday", "week", "ramadan",
)
)
def _looks_campus_question(question: str) -> bool:
return bool(KU_CAMPUS_RE.search(question or ""))
def _looks_medical_search(question: str) -> bool:
q = (question or "").lower()
if not any(kw in q for kw in MEDICAL_KEYWORDS):
return False
return bool(MEDICAL_SEARCH_RE.search(q) or RESEARCH_CUE_RE.search(q) or SUMMARY_RE.search(q))
def _looks_research_question(question: str) -> bool:
q = question or ""
return bool(RESEARCH_CUE_RE.search(q) or SUMMARY_RE.search(q))
def _rule_based_classify(question: str) -> Dict[str, str]:
q = (question or "").lower()
if SOCIAL_RE.match((question or "").strip()):
return {
"intent": "social",
"casual_answer": (
"Hello! I'm LibBee, the KU Library AI Assistant. "
"I'm here to help you with articles, books, databases, and library services. "
"What would you like to find today?"
),
}
if _looks_medical_search(question):
return {"intent": "search_medical"}
if _looks_research_question(question):
return {"intent": "search_academic"}
if LIBRARY_CUE_RE.search(q):
return {"intent": "library_info"}
if CURRENT_INFO_RE.search(q):
return {"intent": "general_recent"}
return {"intent": "general"}
async def _llm_classify(question: str, history: List[ChatMessage], model: str) -> Dict[str, str]:
settings = get_settings()
if not settings.openai_api_key and not settings.anthropic_api_key:
return _rule_based_classify(question)
try:
ctx_parts = [
f"{m.role}: {m.content[:100]}"
for m in history[-HISTORY_WINDOW:]
if m.role in ("user", "assistant")
]
ctx = " | ".join(ctx_parts) if ctx_parts else "none"
classify_model = "gpt" if settings.openai_api_key else model
llm = _get_llm(classify_model, temperature=0, max_tokens=120)
response = await llm.ainvoke([
{"role": "system", "content": _CLASSIFIER_SYSTEM},
{"role": "user", "content": f"Message: {question[:400]}\nContext: {ctx}\nJSON:"},
])
raw = response.content.strip()
if raw.startswith("```"):
raw = raw.split("\n", 1)[1].rsplit("```", 1)[0].strip()
result = json.loads(raw)
intent = result.get("intent", "general")
if intent not in {"social", "library_info", "search_academic", "search_medical", "general_recent", "general", "sensitive"}:
intent = "general"
return {"intent": intent, "casual_answer": result.get("casual_answer", "")}
except Exception as e:
logger.warning(f"LLM classifier error: {e} — falling back to rule-based")
return _rule_based_classify(question)
|