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2b7b752 | 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 | from dataclasses import dataclass, field
from typing import List, Literal, Optional
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
from langchain.agents import AgentState
from langchain_core.messages import AnyMessage
@dataclass
class AgentContext:
agent_name: str
@dataclass
class LeadAgentQueryResponse:
response: str
language: str
processed_query: str = None
confidence_fallback: bool = False
max_turns_reached: bool = False
should_cache: bool = False
appointment_requested: bool = False
show_booking_widget: bool = False
relevant_programs: List[str] = field(default_factory=list)
class StructuredAgentResponse(BaseModel):
response: str = Field(description="Main response to the query.")
is_context_dependent: bool = Field(
default=True,
description=(
"Set to False only if the question can be answered without using any user-specific "
"information (e.g. name, age, preferences, extracted profile data) and without relying "
"on prior conversation turns or conversation history. "
"Must be True for responses involving eligibility, recommendations, comparisons after prior turns, "
"or any answer influenced by user profile data or conversation context."
)
)
appointment_requested: bool = Field(
default=False,
description="Set to True ONLY if the user explicitly asks to book, schedule, speak with admissions/an advisor, see appointment slots, or accepts a previous consultation offer. Routine pricing, comparisons, recommendations, and exploratory fit questions must be False."
)
show_booking_widget: bool = Field(
default=False,
description="Set to True ONLY when appointment_requested is True and the booking widget should be shown now. Never use this for soft contact mentions or routine informational answers."
)
relevant_programs: Optional[List[Literal["emba", "iemba", "emba_x"]]] = Field(
default=None,
description="If appointment_requested is True, list the programs relevant to the user. Options: 'emba', 'iemba', 'emba_x'. If the user is undecided or general, leave this list empty."
)
class State(TypedDict):
messages: list[AnyMessage]
answer: str
class ConversationState(TypedDict):
"""Tracks user profile and conversation context"""
user_id: str # Unique session identifier
user_language: str | None # Locked after first message
user_name: str | None # User's name extracted from conversation
experience_years: int | None # Years of professional experience
leadership_years: int | None # Years of leadership experience
field: str | None # Professional field/industry
interest: str | None # Content interests
qualification_level: str | None # "bachelor", "master", "MBA", etc.
program_interest: list[str] # ["EMBA", "IEMBA", "EMBAX"]
suggested_program: str | None # Recommended program based on conversation
handover_requested: bool | None # True if appointment requested, False if declined, None if session active
topics_discussed: list[str] # Track what's been covered
preferences_known: bool # Whether we have enough context
class LeadInformationState(AgentState):
lead_name: str
lead_age: int
lead_language_knowledge: list
lead_work_experience: dict
lead_motivation: list
# Enhanced state tracking
conversation_state: ConversationState
|