LangChain의 생성 에이전트 깊이 파헤치기

# Main attributes.
llm: BaseLanguageModel = the LLM model
memory_retriever: TimeWeightedVectorStoreRetriever = the retriever with a vector store
verbose: bool = T/F flag if you want logging
reflection_threshold: float = threshold of importance sum scores before 
	starting reflection
current_plan: List[str] = <NOT IMPLEMENTED/UNUSED>
importance_weight: float = the alpha of the importance in the combined 
	score calculation
aggregate_importance: float = a count for the sum of importance scores of 
	recently added memories; if exceeds reflection_threshold, 
	then the agent reflects

# For loading the memory variables (LangChain's BaseMemory). 
max_tokens_limit: int = a counter for max token limit for _get_memories_until_limit
queries_key: str = key string for the input to load_memory_variables; for
	general-purpose loading relevant memories w.r.t. a query
most_recent_memories_token_key: str = key string for the input to 
	load_memory_variables; for general-purpose loading most recent memories
	from the memory stream 
add_memory_key: str = key string for the output variable in save_context
relevant_memories_key: str = key string for load_memory_variables; for
	loading relevant memories w.r.t. a query in load_memory_variables
relevant_memories_simple_key: str = key string for load_memory_variables; for
	loading relevant memories w.r.t. a query in load_memory_variables; simple
	basically means a different formatting
most_recent_memories_key: str = key string for load_memory_variables; for
	loading most recent memories in load_memory_variables
now_key: str = key string for the output variable in save_context

# Flag for whether or not the agent is currently reflecting.
reflecting: bool = True if the agent is reflecting and False otherwise

name: str = name of the agent
age: int = age of agent
traits: str = permanent traits of the agent
status: str = traits you wish not to change (still unclear to me)
memory: GenerateAgentMemory = the agent's memory class
llm: BaseLanguageModel = the LLM
verbose: bool = T/F if you want verbose logging
summary: str = stateful summary for self-reflection; internal variable
summary_refresh_seconds: int = how frequently to regenerate the summary (in sec); internal variable
last_refreshed: datetime = last time the agent's summary was generated; internal variable
daily_summaries: List[str] = summary of agent's daily events undertaken so far; internal variable

 kwargs = {
  "agent_summary_description": <str>,
  "current_time": <str>,
  "relevant_memories": <str>,
  "agent_name": <str>,
  "observation": <str>,
  "agent_status": <str>,
  "recent_memories_token": int,  # number of tokens used in the prompt (w/o including recent memories)
  "most_recent_memories": <str>  # most recent memories (up till a specified token limit)
}

call_to_action_template = (
	"Should {agent_name} react to the observation, and if so,"
        + " what would be an appropriate reaction? Respond in one line."
        + ' If the action is to engage in dialogue, write:\nSAY: "what to say"'
        + "\notherwise, write:\nREACT: {agent_name}'s reaction (if anything)."
        + "\nEither do nothing, react, or say something but not both.\n\n"
)

call_to_action_template = (
	"What would {agent_name} say? To end the conversation, write:"
        ' GOODBYE: "what to say". Otherwise to continue the conversation,'
        ' write: SAY: "what to say next"\n\n'
)

def _compute_agent_summary(self) -> str:
        """"""
        prompt = PromptTemplate.from_template(
            "How would you summarize {name}'s core characteristics given the"
            + " following statements:\n"
            + "{relevant_memories}"
            + "Do not embellish."
            + "\n\nSummary: "
        )
        # The agent seeks to think about their core characteristics.
        return (
            self.chain(prompt)
            .run(name=self.name, queries=[f"{self.name}'s core characteristics"])
            .strip()
        )

inputs = {
	"name": <str>,
	"queries": [<queries>],
	"relevant_memories": <relevant_memories>,
	"relevant_memories_simple": <relevant_memories in simple format>,
}