from __future__ import annotations
import asyncio
import logging
import time
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Dict,
Iterator,
List,
Optional,
Tuple,
Union,
)
from uuid import UUID
from langchain_core.agents import (
AgentAction,
AgentFinish,
AgentStep,
)
from langchain_core.callbacks import (
AsyncCallbackManager,
AsyncCallbackManagerForChainRun,
CallbackManager,
CallbackManagerForChainRun,
Callbacks,
)
from langchain_core.load.dump import dumpd
from langchain_core.outputs import RunInfo
from langchain_core.runnables.utils import AddableDict
from langchain_core.tools import BaseTool
from langchain_core.utils.input import get_color_mapping
from langchain.schema import RUN_KEY
from langchain.utilities.asyncio import asyncio_timeout
if TYPE_CHECKING:
from langchain.agents.agent import AgentExecutor, NextStepOutput
logger = logging.getLogger(__name__)
[docs]class AgentExecutorIterator:
"""Iterator for AgentExecutor."""
[docs] def __init__(
self,
agent_executor: AgentExecutor,
inputs: Any,
callbacks: Callbacks = None,
*,
tags: Optional[list[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
run_name: Optional[str] = None,
run_id: Optional[UUID] = None,
include_run_info: bool = False,
yield_actions: bool = False,
):
"""
Initialize the AgentExecutorIterator with the given AgentExecutor,
inputs, and optional callbacks.
Args:
agent_executor (AgentExecutor): The AgentExecutor to iterate over.
inputs (Any): The inputs to the AgentExecutor.
callbacks (Callbacks, optional): The callbacks to use during iteration.
Defaults to None.
tags (Optional[list[str]], optional): The tags to use during iteration.
Defaults to None.
metadata (Optional[Dict[str, Any]], optional): The metadata to use
during iteration. Defaults to None.
run_name (Optional[str], optional): The name of the run. Defaults to None.
run_id (Optional[UUID], optional): The ID of the run. Defaults to None.
include_run_info (bool, optional): Whether to include run info
in the output. Defaults to False.
yield_actions (bool, optional): Whether to yield actions as they
are generated. Defaults to False.
"""
self._agent_executor = agent_executor
self.inputs = inputs
self.callbacks = callbacks
self.tags = tags
self.metadata = metadata
self.run_name = run_name
self.run_id = run_id
self.include_run_info = include_run_info
self.yield_actions = yield_actions
self.reset()
_inputs: Dict[str, str]
callbacks: Callbacks
tags: Optional[list[str]]
metadata: Optional[Dict[str, Any]]
run_name: Optional[str]
run_id: Optional[UUID]
include_run_info: bool
yield_actions: bool
@property
def inputs(self) -> Dict[str, str]:
"""The inputs to the AgentExecutor."""
return self._inputs
@inputs.setter
def inputs(self, inputs: Any) -> None:
self._inputs = self.agent_executor.prep_inputs(inputs)
@property
def agent_executor(self) -> AgentExecutor:
"""The AgentExecutor to iterate over."""
return self._agent_executor
@agent_executor.setter
def agent_executor(self, agent_executor: AgentExecutor) -> None:
self._agent_executor = agent_executor
# force re-prep inputs in case agent_executor's prep_inputs fn changed
self.inputs = self.inputs
@property
def name_to_tool_map(self) -> Dict[str, BaseTool]:
"""A mapping of tool names to tools."""
return {tool.name: tool for tool in self.agent_executor.tools}
@property
def color_mapping(self) -> Dict[str, str]:
"""A mapping of tool names to colors."""
return get_color_mapping(
[tool.name for tool in self.agent_executor.tools],
excluded_colors=["green", "red"],
)
[docs] def reset(self) -> None:
"""
Reset the iterator to its initial state, clearing intermediate steps,
iterations, and time elapsed.
"""
logger.debug("(Re)setting AgentExecutorIterator to fresh state")
self.intermediate_steps: list[tuple[AgentAction, str]] = []
self.iterations = 0
# maybe better to start these on the first __anext__ call?
self.time_elapsed = 0.0
self.start_time = time.time()
[docs] def update_iterations(self) -> None:
"""
Increment the number of iterations and update the time elapsed.
"""
self.iterations += 1
self.time_elapsed = time.time() - self.start_time
logger.debug(
f"Agent Iterations: {self.iterations} ({self.time_elapsed:.2f}s elapsed)"
)
[docs] def make_final_outputs(
self,
outputs: Dict[str, Any],
run_manager: Union[CallbackManagerForChainRun, AsyncCallbackManagerForChainRun],
) -> AddableDict:
# have access to intermediate steps by design in iterator,
# so return only outputs may as well always be true.
prepared_outputs = AddableDict(
self.agent_executor.prep_outputs(
self.inputs, outputs, return_only_outputs=True
)
)
if self.include_run_info:
prepared_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
return prepared_outputs
def __iter__(self: "AgentExecutorIterator") -> Iterator[AddableDict]:
logger.debug("Initialising AgentExecutorIterator")
self.reset()
callback_manager = CallbackManager.configure(
self.callbacks,
self.agent_executor.callbacks,
self.agent_executor.verbose,
self.tags,
self.agent_executor.tags,
self.metadata,
self.agent_executor.metadata,
)
run_manager = callback_manager.on_chain_start(
dumpd(self.agent_executor),
self.inputs,
self.run_id,
name=self.run_name,
)
try:
while self.agent_executor._should_continue(
self.iterations, self.time_elapsed
):
# take the next step: this plans next action, executes it,
# yielding action and observation as they are generated
next_step_seq: NextStepOutput = []
for chunk in self.agent_executor._iter_next_step(
self.name_to_tool_map,
self.color_mapping,
self.inputs,
self.intermediate_steps,
run_manager,
):
next_step_seq.append(chunk)
# if we're yielding actions, yield them as they come
# do not yield AgentFinish, which will be handled below
if self.yield_actions:
if isinstance(chunk, AgentAction):
yield AddableDict(actions=[chunk], messages=chunk.messages)
elif isinstance(chunk, AgentStep):
yield AddableDict(steps=[chunk], messages=chunk.messages)
# convert iterator output to format handled by _process_next_step_output
next_step = self.agent_executor._consume_next_step(next_step_seq)
# update iterations and time elapsed
self.update_iterations()
# decide if this is the final output
output = self._process_next_step_output(next_step, run_manager)
is_final = "intermediate_step" not in output
# yield the final output always
# for backwards compat, yield int. output if not yielding actions
if not self.yield_actions or is_final:
yield output
# if final output reached, stop iteration
if is_final:
return
except BaseException as e:
run_manager.on_chain_error(e)
raise
# if we got here means we exhausted iterations or time
yield self._stop(run_manager)
async def __aiter__(self) -> AsyncIterator[AddableDict]:
"""
N.B. __aiter__ must be a normal method, so need to initialize async run manager
on first __anext__ call where we can await it
"""
logger.debug("Initialising AgentExecutorIterator (async)")
self.reset()
callback_manager = AsyncCallbackManager.configure(
self.callbacks,
self.agent_executor.callbacks,
self.agent_executor.verbose,
self.tags,
self.agent_executor.tags,
self.metadata,
self.agent_executor.metadata,
)
run_manager = await callback_manager.on_chain_start(
dumpd(self.agent_executor),
self.inputs,
self.run_id,
name=self.run_name,
)
try:
async with asyncio_timeout(self.agent_executor.max_execution_time):
while self.agent_executor._should_continue(
self.iterations, self.time_elapsed
):
# take the next step: this plans next action, executes it,
# yielding action and observation as they are generated
next_step_seq: NextStepOutput = []
async for chunk in self.agent_executor._aiter_next_step(
self.name_to_tool_map,
self.color_mapping,
self.inputs,
self.intermediate_steps,
run_manager,
):
next_step_seq.append(chunk)
# if we're yielding actions, yield them as they come
# do not yield AgentFinish, which will be handled below
if self.yield_actions:
if isinstance(chunk, AgentAction):
yield AddableDict(
actions=[chunk], messages=chunk.messages
)
elif isinstance(chunk, AgentStep):
yield AddableDict(
steps=[chunk], messages=chunk.messages
)
# convert iterator output to format handled by _process_next_step
next_step = self.agent_executor._consume_next_step(next_step_seq)
# update iterations and time elapsed
self.update_iterations()
# decide if this is the final output
output = await self._aprocess_next_step_output(
next_step, run_manager
)
is_final = "intermediate_step" not in output
# yield the final output always
# for backwards compat, yield int. output if not yielding actions
if not self.yield_actions or is_final:
yield output
# if final output reached, stop iteration
if is_final:
return
except (TimeoutError, asyncio.TimeoutError):
yield await self._astop(run_manager)
return
except BaseException as e:
await run_manager.on_chain_error(e)
raise
# if we got here means we exhausted iterations or time
yield await self._astop(run_manager)
def _process_next_step_output(
self,
next_step_output: Union[AgentFinish, List[Tuple[AgentAction, str]]],
run_manager: CallbackManagerForChainRun,
) -> AddableDict:
"""
Process the output of the next step,
handling AgentFinish and tool return cases.
"""
logger.debug("Processing output of Agent loop step")
if isinstance(next_step_output, AgentFinish):
logger.debug(
"Hit AgentFinish: _return -> on_chain_end -> run final output logic"
)
return self._return(next_step_output, run_manager=run_manager)
self.intermediate_steps.extend(next_step_output)
logger.debug("Updated intermediate_steps with step output")
# Check for tool return
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
tool_return = self.agent_executor._get_tool_return(next_step_action)
if tool_return is not None:
return self._return(tool_return, run_manager=run_manager)
return AddableDict(intermediate_step=next_step_output)
async def _aprocess_next_step_output(
self,
next_step_output: Union[AgentFinish, List[Tuple[AgentAction, str]]],
run_manager: AsyncCallbackManagerForChainRun,
) -> AddableDict:
"""
Process the output of the next async step,
handling AgentFinish and tool return cases.
"""
logger.debug("Processing output of async Agent loop step")
if isinstance(next_step_output, AgentFinish):
logger.debug(
"Hit AgentFinish: _areturn -> on_chain_end -> run final output logic"
)
return await self._areturn(next_step_output, run_manager=run_manager)
self.intermediate_steps.extend(next_step_output)
logger.debug("Updated intermediate_steps with step output")
# Check for tool return
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
tool_return = self.agent_executor._get_tool_return(next_step_action)
if tool_return is not None:
return await self._areturn(tool_return, run_manager=run_manager)
return AddableDict(intermediate_step=next_step_output)
def _stop(self, run_manager: CallbackManagerForChainRun) -> AddableDict:
"""
Stop the iterator and raise a StopIteration exception with the stopped response.
"""
logger.warning("Stopping agent prematurely due to triggering stop condition")
# this manually constructs agent finish with output key
output = self.agent_executor._action_agent.return_stopped_response(
self.agent_executor.early_stopping_method,
self.intermediate_steps,
**self.inputs,
)
return self._return(output, run_manager=run_manager)
async def _astop(self, run_manager: AsyncCallbackManagerForChainRun) -> AddableDict:
"""
Stop the async iterator and raise a StopAsyncIteration exception with
the stopped response.
"""
logger.warning("Stopping agent prematurely due to triggering stop condition")
output = self.agent_executor._action_agent.return_stopped_response(
self.agent_executor.early_stopping_method,
self.intermediate_steps,
**self.inputs,
)
return await self._areturn(output, run_manager=run_manager)
def _return(
self, output: AgentFinish, run_manager: CallbackManagerForChainRun
) -> AddableDict:
"""
Return the final output of the iterator.
"""
returned_output = self.agent_executor._return(
output, self.intermediate_steps, run_manager=run_manager
)
returned_output["messages"] = output.messages
run_manager.on_chain_end(returned_output)
return self.make_final_outputs(returned_output, run_manager)
async def _areturn(
self, output: AgentFinish, run_manager: AsyncCallbackManagerForChainRun
) -> AddableDict:
"""
Return the final output of the async iterator.
"""
returned_output = await self.agent_executor._areturn(
output, self.intermediate_steps, run_manager=run_manager
)
returned_output["messages"] = output.messages
await run_manager.on_chain_end(returned_output)
return self.make_final_outputs(returned_output, run_manager)
