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311 | class LATSAgentWorker(CustomSimpleAgentWorker):
"""Agent worker that performs a step of Language Agent Tree Search.
Source paper: https://arxiv.org/pdf/2310.04406v2.pdf.
Continues iterating until there's no errors / task is done.
"""
num_expansions: int = Field(default=2, description="Number of expansions to do.")
reflection_prompt: PromptTemplate = Field(..., description="Reflection prompt.")
candiate_expansion_prompt: PromptTemplate = Field(
..., description="Candidate expansion prompt."
)
max_rollouts: int = Field(
default=5,
description=(
"Max rollouts. By default, -1 means that we keep going until the first solution is found."
),
)
chat_formatter: ReActChatFormatter = Field(
default_factory=ReActChatFormatter, description="Chat formatter."
)
def __init__(
self,
tools: List[BaseTool],
llm: Optional[LLM] = None,
num_expansions: int = 2,
max_rollouts: int = 5,
reflection_prompt: Optional[PromptTemplate] = None,
candiate_expansion_prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
) -> None:
"""Init params."""
# validate that all tools are query engine tools
llm = llm or Settings.llm
super().__init__(
tools=tools,
llm=llm,
num_expansions=num_expansions,
max_rollouts=max_rollouts,
reflection_prompt=reflection_prompt or DEFAULT_REFLECTION_PROMPT,
candiate_expansion_prompt=candiate_expansion_prompt
or DEFAULT_CANDIDATES_PROMPT,
**kwargs,
)
def _initialize_state(self, task: Task, **kwargs: Any) -> Dict[str, Any]:
"""Initialize state."""
# initialize root node
root_node = SearchNode(
current_reasoning=[ObservationReasoningStep(observation=task.input)],
evaluation=Evaluation(score=1), # evaluation for root node is blank
)
return {"count": 0, "solution_queue": [], "root_node": root_node}
async def _arun_candidate(
self,
node: SearchNode,
task: Task,
) -> List[BaseReasoningStep]:
"""Generate candidate for a given node.
Generically we sample the action space to generate new candidate nodes.
Practically since we're using a ReAct powered agent, this means
using generating a ReAct trajectory, running a tool.
"""
output_parser = ReActOutputParser()
# format react prompt
formatted_prompt = self.chat_formatter.format(
self.tools,
chat_history=task.memory.get(),
current_reasoning=node.current_reasoning,
)
# run LLM
response = await self.llm.achat(formatted_prompt)
# parse output into reasoning step
try:
reasoning_step = output_parser.parse(response.message.content)
except ValueError as e:
reasoning_step = ResponseReasoningStep(
thought=response.message.content,
response=f"Encountered an error parsing: {e!s}",
)
# get response or run tool
if reasoning_step.is_done:
reasoning_step = cast(ResponseReasoningStep, reasoning_step)
current_reasoning = [reasoning_step]
else:
reasoning_step = cast(ActionReasoningStep, reasoning_step)
tool_selection = ToolSelection(
tool_id=reasoning_step.action,
tool_name=reasoning_step.action,
tool_kwargs=reasoning_step.action_input,
)
try:
tool_output = await acall_tool_with_selection(
tool_selection, self.tools, verbose=self.verbose
)
except Exception as e:
tool_output = f"Encountered error: {e!s}"
observation_step = ObservationReasoningStep(observation=str(tool_output))
current_reasoning = [reasoning_step, observation_step]
return current_reasoning
async def _aevaluate(
self,
cur_node: SearchNode,
current_reasoning: List[BaseReasoningStep],
input: str,
) -> float:
"""Evaluate."""
all_reasoning = cur_node.current_reasoning + current_reasoning
history_str = "\n".join([s.get_content() for s in all_reasoning])
evaluation = await self.llm.astructured_predict(
Evaluation,
prompt=self.reflection_prompt,
query=input,
conversation_history=history_str,
)
if self.verbose:
print_text(
f"> Evaluation for input {input}\n: {evaluation}\n\n", color="pink"
)
return evaluation
async def _get_next_candidates(
self,
cur_node: SearchNode,
input: str,
) -> List[str]:
"""Get next candidates."""
# get candidates
history_str = "\n".join([s.get_content() for s in cur_node.current_reasoning])
candidates = await self.llm.astructured_predict(
Candidates,
prompt=self.candiate_expansion_prompt,
query=input,
conversation_history=history_str,
num_candidates=self.num_expansions,
)
candidate_strs = candidates.candidates[: self.num_expansions]
if self.verbose:
print_text(f"> Got candidates: {candidate_strs}\n", color="yellow")
# ensure we have the right number of candidates
if len(candidate_strs) < self.num_expansions:
return (candidate_strs * self.num_expansions)[: self.num_expansions]
else:
return candidate_strs[: self.num_expansions]
def _update_state(
self,
node: SearchNode,
current_reasoning: List[BaseReasoningStep],
evaluation: Evaluation,
) -> SearchNode:
"""Update state."""
# create child node
new_node = SearchNode(
current_reasoning=node.current_reasoning + current_reasoning,
parent=node,
children=[],
evaluation=evaluation,
)
node.children.append(new_node)
# backpropagate the reward
new_node.backpropagate(evaluation.score)
return new_node
def _run_step(
self, state: Dict[str, Any], task: Task, input: Optional[str] = None
) -> Tuple[AgentChatResponse, bool]:
"""Run step.
Returns:
Tuple of (agent_response, is_done)
"""
return asyncio.run(self._arun_step(state, task, input))
async def _arun_step(
self, state: Dict[str, Any], task: Task, input: Optional[str] = None
) -> Tuple[AgentChatResponse, bool]:
"""Run step.
Returns:
Tuple of (agent_response, is_done)
"""
root_node = state["root_node"]
cur_node = root_node.get_best_leaf()
if self.verbose:
print_text(
f"> Selecting node to expand: {cur_node.answer}\n", color="green"
)
# expand the given node, generate n candidate nodes
new_candidates = await self._get_next_candidates(
cur_node,
task.input,
)
new_nodes = []
for candidate in new_candidates:
new_nodes.append(
self._update_state(
cur_node,
[ObservationReasoningStep(observation=candidate)],
Evaluation(score=1), # evaluation for candidate node is blank
)
)
# expand the given node, generate n candidates
# for each candidate, run tool, get response
solution_queue: List[SearchNode] = state["solution_queue"]
# first, generate the candidates
candidate_jobs = [
self._arun_candidate(new_node, task) for new_node in new_nodes
]
all_new_reasoning_steps = await asyncio.gather(*candidate_jobs)
if self.verbose:
for new_reasoning_steps in all_new_reasoning_steps:
out_txt = "\n".join([s.get_content() for s in new_reasoning_steps])
print_text(f"> Generated new reasoning step: {out_txt}\n", color="blue")
# then, evaluate the candidates
eval_jobs = [
self._aevaluate(new_node, new_reasoning_steps, task.input)
for new_node, new_reasoning_steps in zip(new_nodes, all_new_reasoning_steps)
]
evaluations = await asyncio.gather(*eval_jobs)
# then, update the state
for new_reasoning_steps, cur_new_node, evaluation in zip(
all_new_reasoning_steps, new_nodes, evaluations
):
new_node = self._update_state(cur_new_node, new_reasoning_steps, evaluation)
if new_node.is_done:
if self.verbose:
print_text(
f"> Found solution node: {new_node.answer}\n", color="cyan"
)
solution_queue.append(new_node)
# check if done
state["count"] += 1
if self.max_rollouts == -1 and solution_queue:
is_done = True
elif self.max_rollouts > 0 and state["count"] >= self.max_rollouts:
is_done = True
else:
is_done = False
# determine response
if solution_queue:
best_solution_node = max(solution_queue, key=lambda x: x.score)
response = best_solution_node.answer
else:
response = "I am still thinking."
if self.verbose:
print_text(f"> Got final response: {response!s}\n", color="green")
# return response
return AgentChatResponse(response=str(response)), is_done
def _finalize_task(self, state: Dict[str, Any], **kwargs) -> None:
"""Finalize task."""
|
