reflexion.ipynb에서는 Reflexion: Language Agents with Verbal Reinforcement Learning을 기반한 Reflexion을 구현하고 있습니다.
Reflexion의 Diagram은 아래와 같습니다.
feedback과 self-reflection을 이용해 더 높은 성능의 결과를 얻습니다.
Graph의 구현 코드는 아래와 같습니다.
MAX_ITERATIONS = 5
builder = MessageGraph()
builder.add_node("draft", first_responder.respond)
builder.add_node("execute_tools", tool_node)
builder.add_node("revise", revisor.respond)
builder.add_edge("draft", "execute_tools") # draft -> execute_tools
builder.add_edge("execute_tools", "revise") # execute_tools -> revise
def _get_num_iterations(state: list):
i = 0
for m in state[::-1]:
if m.type not in {"tool", "ai"}:
break
i += 1
return i
def event_loop(state: list) -> Literal["execute_tools", "__end__"]:
# in our case, we'll just stop after N plans
num_iterations = _get_num_iterations(state)
if num_iterations > MAX_ITERATIONS:
return END
return "execute_tools"
builder.add_conditional_edges("revise", event_loop) # revise -> execute_tools OR end
builder.set_entry_point("draft")
graph = builder.compile()이것의 구현된 결과는 아래와 같습니다.
Reflexion에서는 AnswerQuestion/Reflectin 클래스를 이용하여 문장에서 Reflection에 필요한 정보를 추출합니다.
class Reflection(BaseModel):
missing: str = Field(description="Critique of what is missing.")
superfluous: str = Field(description="Critique of what is superfluous")
class AnswerQuestion(BaseModel):
"""Answer the question. Provide an answer, reflection, and then follow up with search queries to improve the answer."""
answer: str = Field(description="~250 word detailed answer to the question.")
reflection: Reflection = Field(description="Your reflection on the initial answer.")
search_queries: list[str] = Field(
description="1-3 search queries for researching improvements to address the critique of your current answer."
)