from __future__ import annotations
import asyncio
import logging
import time
from collections.abc import AsyncIterator, Iterator
from typing import (
TYPE_CHECKING,
Any,
Optional,
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.
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.
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(
"Agent Iterations: %s (%.2fs elapsed)",
self.iterations,
self.time_elapsed,
)
[docs]
def make_final_outputs(
self,
outputs: dict[str, Any],
run_manager: Union[CallbackManagerForChainRun, AsyncCallbackManagerForChainRun],
) -> AddableDict:
"""Make final outputs for the iterator.
Args:
outputs: The outputs from the agent executor.
run_manager: The run manager to use for callbacks.
"""
# 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]:
"""Create an async iterator for the AgentExecutor."""
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( # noqa: SLF001
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( # noqa: SLF001
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) # noqa: SLF001
# 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]:
"""Create an async iterator for the AgentExecutor.
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( # noqa: SLF001
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( # noqa: SLF001
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) # noqa: SLF001
# 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.
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) # noqa: SLF001
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.
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) # noqa: SLF001
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.
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( # noqa: SLF001
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.
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( # noqa: SLF001
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( # noqa: SLF001
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( # noqa: SLF001
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)
