from __future__ import annotations from uuid import UUID import json import asyncio from typing import Any, Dict, List, Optional from langchain.callbacks import AsyncIteratorCallbackHandler from langchain.schema import AgentFinish, AgentAction from langchain_core.outputs import LLMResult def dumps(obj: Dict) -> str: return json.dumps(obj, ensure_ascii=False) class Status: start: int = 1 running: int = 2 complete: int = 3 agent_action: int = 4 agent_finish: int = 5 error: int = 6 tool_finish: int = 7 class CustomAsyncIteratorCallbackHandler(AsyncIteratorCallbackHandler): def __init__(self): super().__init__() self.queue = asyncio.Queue() self.done = asyncio.Event() self.cur_tool = {} self.out = True async def on_tool_start(self, serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: UUID | None = None, tags: List[str] | None = None, metadata: Dict[str, Any] | None = None, **kwargs: Any) -> None: self.cur_tool = { "tool_name": serialized["name"], "input_str": input_str, "output_str": "", "status": Status.agent_action, "run_id": run_id.hex, "llm_token": "", "final_answer": "", "error": "", } self.queue.put_nowait(dumps(self.cur_tool)) async def on_tool_end(self, output: str, *, run_id: UUID, parent_run_id: UUID | None = None, tags: List[str] | None = None, **kwargs: Any) -> None: self.out = True self.cur_tool.update( status=Status.tool_finish, output_str=output.replace("Answer:", ""), ) self.queue.put_nowait(dumps(self.cur_tool)) async def on_tool_error(self, error: Exception | KeyboardInterrupt, *, run_id: UUID, parent_run_id: UUID | None = None, tags: List[str] | None = None, **kwargs: Any) -> None: self.cur_tool.update( status=Status.error, error=str(error), ) self.queue.put_nowait(dumps(self.cur_tool)) async def on_llm_new_token(self, token: str, **kwargs: Any) -> None: special_tokens = ["Action", "<|observation|>"] for stoken in special_tokens: if stoken in token: before_action = token.split(stoken)[0] self.cur_tool.update( status=Status.running, llm_token=before_action + "\n", ) self.queue.put_nowait(dumps(self.cur_tool)) self.out = False break if token is not None and token != "" and self.out: self.cur_tool.update( status=Status.running, llm_token=token, ) self.queue.put_nowait(dumps(self.cur_tool)) async def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None: self.cur_tool.update( status=Status.start, llm_token="", ) self.queue.put_nowait(dumps(self.cur_tool)) async def on_chat_model_start( self, serialized: Dict[str, Any], messages: List[List], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> None: self.cur_tool.update( status=Status.start, llm_token="", ) self.queue.put_nowait(dumps(self.cur_tool)) async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: self.cur_tool.update( status=Status.complete, llm_token="", ) self.out = True self.queue.put_nowait(dumps(self.cur_tool)) async def on_llm_error(self, error: Exception | KeyboardInterrupt, **kwargs: Any) -> None: self.cur_tool.update( status=Status.error, error=str(error), ) self.queue.put_nowait(dumps(self.cur_tool)) async def on_agent_action( self, action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any, ) -> None: self.cur_tool.update( status=Status.agent_action, tool_name=action.tool, tool_input=action.tool_input, ) self.queue.put_nowait(dumps(self.cur_tool)) async def on_agent_finish( self, finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any, ) -> None: if "Thought:" in finish.return_values["output"]: finish.return_values["output"] = finish.return_values["output"].replace("Thought:", "") self.cur_tool.update( status=Status.agent_finish, agent_finish=finish.return_values["output"], ) self.queue.put_nowait(dumps(self.cur_tool))