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from langchain_core.messages import AIMessage, HumanMessage
from langgraph.graph import START, END, StateGraph
from typing import Any, Dict, List, Literal, Optional, TypedDict
import logging
from pathlib import Path
from args import Args
class State(TypedDict):
"""State class for the agent graph."""
initial_query: str
messages: List[Dict[str, Any]]
nr_interactions: int
final_response: Optional[str]
class Nodes:
"""
Collection of node functions for the agent graph.
"""
def manager_node(self, state: State) -> State:
"""
Orchestrates the workflow by delegating tasks to specialized nodes and integrating their outputs
"""
# TODO: To implement...
pass
def final_answer_node(self, state: State) -> State:
"""
Formats and delivers the final response to the user
"""
# TODO: To implement...
pass
def auditor_node(self, state: State) -> State:
"""
Reviews manager's outputs for accuracy, safety, and quality
"""
# TODO: To implement...
pass
def solver_node(self, state: State) -> State:
"""
Central problem-solving node that coordinates with specialized experts based on task requirements
"""
# TODO: To implement...
pass
def researcher_node(self, state: State) -> State:
"""
Retrieves and synthesizes information from various sources to answer knowledge-based questions
"""
# TODO: To implement...
pass
def encryption_expert_node(self, state: State) -> State:
"""
Handles encryption/decryption tasks and encoding/decoding operations
"""
# TODO: To implement...
pass
def encryption_advisor_node(self, state: State) -> State:
"""
Provides specialized guidance on complex encryption problems to the encryption expert
"""
# TODO: To implement...
pass
def math_expert_node(self, state: State) -> State:
"""
Performs mathematical calculations and solves numerical problems
"""
# TODO: To implement...
pass
def math_advisor_node(self, state: State) -> State:
"""
Provides specialized guidance on complex mathematical problems to the math expert
"""
# TODO: To implement...
pass
def reasoner_node(self, state: State) -> State:
"""
Performs logical reasoning, inference, and step-by-step problem-solving
"""
# TODO: To implement...
pass
def image_handler_node(self, state: State) -> State:
"""
Processes, analyzes, and generates information related to images
"""
# TODO: To implement...
pass
def video_handler_node(self, state: State) -> State:
"""
Processes, analyzes, and generates information related to videos
"""
# TODO: To implement...
pass
class Edges:
"""
Collection of conditional edge functions for the agent graph.
"""
def manager_edge(self, state: State) -> Literal["solver", "auditor", "final_answer"]:
"""
Conditional edge for manager node.
Returns one of: "solver", "auditor", "final_answer"
"""
# TODO: To implement...
pass
def solver_edge(self, state: State) -> Literal["manager", "researcher", "encryption_expert", "math_expert", "reasoner", "image_handler", "video_handler"]:
"""
Conditional edge for solver node.
Returns one of: "manager", "researcher", "encryption_expert", "math_expert", "reasoner", "image_handler", "video_handler"
"""
# TODO: To implement...
pass
def encryption_expert_edge(self, state: State) -> Literal["solver", "encryption_advisor"]:
"""
Conditional edge for encryption_expert node.
Returns one of: "solver", "encryption_advisor"
"""
# TODO: To implement...
pass
def math_expert_edge(self, state: State) -> Literal["solver", "math_advisor"]:
"""
Conditional edge for math_expert node.
Returns one of: "solver", "math_advisor"
"""
# TODO: To implement...
pass |