agents/langgraph.py

from typing import TypedDict, Dict, Any, List, Annotated
import time, uuid, os
from langgraph.graph import StateGraph, END
from langchain_core.messages import HumanMessage, AIMessage, BaseMessage
from agents.simple_tools import (
    generate_notes_full_pipeline_from_path,
    generate_balance_sheet,
    generate_pnl_statement,
    generate_cash_flow_statement,
    generate_llm_notes,
)

class FinancialAgentState(TypedDict):
    messages: Annotated[List[BaseMessage], "History"]
    file_path: str
    result: Dict[str, Any]
    status: str
    start_time: float
    end_time: float
    error: str

def make_workflow(tool_func):
    def node(state: FinancialAgentState) -> FinancialAgentState:
        state["start_time"] = time.time()
        try:
            # Prepare parameters for tool invocation
            tool_params = {"file_path": state["file_path"]}
            # Add feedback_context if available
            if "feedback_context" in state:
                tool_params["feedback_context"] = state["feedback_context"]
            # Use .invoke() to avoid deprecation warning
            result = tool_func.invoke(tool_params)
            state["result"] = result
            state["status"] = "success" if result.get("status") == "success" else "error"
            state["error"] = result.get("error", "")
        except Exception as e:
            state["status"] = "error"
            state["error"] = str(e)
        state["end_time"] = time.time()
        return state

    wf = StateGraph(FinancialAgentState)
    wf.add_node("run", node)
    wf.set_entry_point("run")
    wf.add_edge("run", END)
    return wf.compile()

workflows = {
    "notes": make_workflow(generate_notes_full_pipeline_from_path),
    "pnl": make_workflow(generate_pnl_statement),
    "bs": make_workflow(generate_balance_sheet),
    "cf": make_workflow(generate_cash_flow_statement),
    "notes-llm": make_workflow(generate_llm_notes),
}

def run_workflow(file_path: str, kind: str, **kwargs) -> Dict[str, Any]:
    state = FinancialAgentState(
        messages=[HumanMessage(content=f"Run {kind} for {file_path}")],
        file_path=file_path,
        result={},
        status="",
        start_time=0,
        end_time=0,
        error="",
    )
    # Add feedback_context if provided
    if "feedback_context" in kwargs:
        state["feedback_context"] = kwargs["feedback_context"]   
    final = workflows[kind].invoke(state)
    return final