Automatically converting name to email address

from pydantic import Field

from llama_index.core.tools import FunctionTool
from models.agentconfig import AgentConfig

def get_account_ids(email: str = Field(description="An email address, formatted as name@example.com")) -> list[str]:
    """Used to get a list of account IDs for a user for a given email address."""
    return ["1234567890"]

def get_policies_for_account_id(account_id: str) -> list[str]:
    """Used to get a list of policies for a given account ID."""
    return ["Policy_1", "Policy_2"]

def send_cancel_policy_form(account_id: str, policy: str) -> str:
    """Used to send a cancel policy form to a customer."""
    return "Cancel Policy Form sent for Policy: " + policy

get_account_ids_tool = FunctionTool.from_defaults(fn=get_account_ids)
get_policies_for_account_id_tool = FunctionTool.from_defaults(fn=get_policies_for_account_id)
send_cancel_policy_form_tool = FunctionTool.from_defaults(fn=send_cancel_policy_form)

tools = [get_account_ids_tool, get_policies_for_account_id_tool, send_cancel_policy_form_tool]

SendCancelPolicyFormAgentConfig = AgentConfig(
    name="Send Cancel Policy Form Agent",
    description="Used to send a cancel policy form to a customer.",
    system_prompt="You are an agent that can send a cancel policy form to a customer. Do not help the user with anything else.",
    tools=tools,
)

@step 
    async def handle_input(self, ctx: Context, ev: ToolInputEvent) -> ToolCallEvent | ToolCallResultEvent:
        """"Handles the input required for a tool call."""
        if ev.input:
            active_speaker = await ctx.get("active_speaker")
            agent_config = (await ctx.get("agent_configs"))[active_speaker]
            return ToolCallEvent(
                tools=agent_config.tools,
                tool_call=ToolSelection(
                    tool_id=ev.tool_id,
                    tool_name=ev.tool_name,
                    tool_kwargs=ev.tool_kwargs,
                ),
            )
        else:
            return ToolCallResultEvent(
                chat_message=ChatMessage(
                    role="tool",
                    content=ev.response or self.default_tool_reject_str,
                )
            )

async def on_message(chatrequest: ChatRequest) -> str:
  workflow = ConciergeWorkflow()

  handler = workflow.run(
      agent_configs=[MathAgentConfig, SendCancelPolicyFormAgentConfig,TransferToHumanAgentConfig],
      user_msg=chatrequest.message.content,
      chat_history=chatrequest.chat_history,
      initial_state={"favorite number": "5"},
      llm=llm,
  )

  async for event in handler.stream_events():
    if isinstance(event, ProgressEvent):
        print(event.msg)

  print("-----------")

  final_result = await handler
  print(final_result)
  return final_result["response"]

handler.ctx.send_event(ToolApprovedEvent(...))

from llama_index.core.workflow.context_serializers import JsonSerializer, JsonPickleSerializer

ctx_dict = handler.ctx.to_dict(serializer=JsonSerializer())
....
resumed_ctx = Context.from_dict(w, ctx_dict, serializer=JsonSerializer())
handler = w.run(ctx=resumed_ctx)
handler.send_event(...)
...