src/agents/lesson_practice/func.py

from typing import (
    Annotated,
    Sequence,
    TypedDict,
)
from langchain_core.messages import ToolMessage, AnyMessage, RemoveMessage
from langgraph.graph.message import add_messages
import json
from .prompt import conversation_prompt
from src.config.llm import model


class State(TypedDict):
    """The state of the agent."""

    unit: str
    vocabulary: list
    key_structures: list
    practice_questions: list
    student_level: list
    messages: Annotated[Sequence[AnyMessage], add_messages]


tools = []

tools_by_name = {tool.name: tool for tool in tools}


def trim_history(state: State):
    if not state.get("active_agent"):
        state["active_agent"] = "Roleplay Agent"
    history = state.get("messages", [])
    if len(history) > 25:
        num_to_remove = len(history) - 5
        remove_messages = [
            RemoveMessage(id=history[i].id) for i in range(num_to_remove)
        ]
        state["messages"] = remove_messages
    return state


# Define our tool node
def tool_node(state: State):
    outputs = []
    for tool_call in state["messages"][-1].tool_calls:
        tool_result = tools_by_name[tool_call["name"]].invoke(tool_call["args"])
        outputs.append(
            ToolMessage(
                content=json.dumps(tool_result),
                name=tool_call["name"],
                tool_call_id=tool_call["id"],
            )
        )
    return {"messages": outputs}


async def agent(state: State):
    llm = conversation_prompt | model
    response = await llm.ainvoke(state)
    return {"messages": response}