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253 lines
9.9 KiB
Python
253 lines
9.9 KiB
Python
import re
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from datetime import datetime
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from typing import Any, Dict, List, Optional, Tuple
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from pydantic import BaseModel, Field
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from langchain import LLMChain
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from langchain.base_language import BaseLanguageModel
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from langchain.experimental.generative_agents.memory import GenerativeAgentMemory
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from langchain.prompts import PromptTemplate
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class GenerativeAgent(BaseModel):
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"""A character with memory and innate characteristics."""
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name: str
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"""The character's name."""
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age: Optional[int] = None
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"""The optional age of the character."""
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traits: str = "N/A"
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"""Permanent traits to ascribe to the character."""
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status: str
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"""The traits of the character you wish not to change."""
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memory: GenerativeAgentMemory
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"""The memory object that combines relevance, recency, and 'importance'."""
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llm: BaseLanguageModel
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"""The underlying language model."""
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verbose: bool = False
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summary: str = "" #: :meta private:
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"""Stateful self-summary generated via reflection on the character's memory."""
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summary_refresh_seconds: int = 3600 #: :meta private:
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"""How frequently to re-generate the summary."""
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last_refreshed: datetime = Field(default_factory=datetime.now) # : :meta private:
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"""The last time the character's summary was regenerated."""
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daily_summaries: List[str] = Field(default_factory=list) # : :meta private:
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"""Summary of the events in the plan that the agent took."""
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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# LLM-related methods
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@staticmethod
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def _parse_list(text: str) -> List[str]:
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"""Parse a newline-separated string into a list of strings."""
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lines = re.split(r"\n", text.strip())
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return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines]
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def chain(self, prompt: PromptTemplate) -> LLMChain:
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return LLMChain(
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llm=self.llm, prompt=prompt, verbose=self.verbose, memory=self.memory
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)
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def _get_entity_from_observation(self, observation: str) -> str:
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prompt = PromptTemplate.from_template(
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"What is the observed entity in the following observation? {observation}"
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+ "\nEntity="
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)
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return self.chain(prompt).run(observation=observation).strip()
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def _get_entity_action(self, observation: str, entity_name: str) -> str:
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prompt = PromptTemplate.from_template(
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"What is the {entity} doing in the following observation? {observation}"
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+ "\nThe {entity} is"
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)
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return (
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self.chain(prompt).run(entity=entity_name, observation=observation).strip()
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)
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def summarize_related_memories(self, observation: str) -> str:
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"""Summarize memories that are most relevant to an observation."""
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prompt = PromptTemplate.from_template(
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"""
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{q1}?
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Context from memory:
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{relevant_memories}
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Relevant context:
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"""
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)
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entity_name = self._get_entity_from_observation(observation)
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entity_action = self._get_entity_action(observation, entity_name)
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q1 = f"What is the relationship between {self.name} and {entity_name}"
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q2 = f"{entity_name} is {entity_action}"
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return self.chain(prompt=prompt).run(q1=q1, queries=[q1, q2]).strip()
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def _generate_reaction(
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self, observation: str, suffix: str, now: Optional[datetime] = None
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) -> str:
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"""React to a given observation or dialogue act."""
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prompt = PromptTemplate.from_template(
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"{agent_summary_description}"
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+ "\nIt is {current_time}."
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+ "\n{agent_name}'s status: {agent_status}"
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+ "\nSummary of relevant context from {agent_name}'s memory:"
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+ "\n{relevant_memories}"
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+ "\nMost recent observations: {most_recent_memories}"
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+ "\nObservation: {observation}"
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+ "\n\n"
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+ suffix
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)
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agent_summary_description = self.get_summary(now=now)
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relevant_memories_str = self.summarize_related_memories(observation)
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current_time_str = (
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datetime.now().strftime("%B %d, %Y, %I:%M %p")
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if now is None
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else now.strftime("%B %d, %Y, %I:%M %p")
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)
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kwargs: Dict[str, Any] = dict(
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agent_summary_description=agent_summary_description,
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current_time=current_time_str,
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relevant_memories=relevant_memories_str,
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agent_name=self.name,
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observation=observation,
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agent_status=self.status,
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)
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consumed_tokens = self.llm.get_num_tokens(
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prompt.format(most_recent_memories="", **kwargs)
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)
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kwargs[self.memory.most_recent_memories_token_key] = consumed_tokens
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return self.chain(prompt=prompt).run(**kwargs).strip()
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def _clean_response(self, text: str) -> str:
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return re.sub(f"^{self.name} ", "", text.strip()).strip()
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def generate_reaction(
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self, observation: str, now: Optional[datetime] = None
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) -> Tuple[bool, str]:
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"""React to a given observation."""
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call_to_action_template = (
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"Should {agent_name} react to the observation, and if so,"
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+ " what would be an appropriate reaction? Respond in one line."
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+ ' If the action is to engage in dialogue, write:\nSAY: "what to say"'
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+ "\notherwise, write:\nREACT: {agent_name}'s reaction (if anything)."
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+ "\nEither do nothing, react, or say something but not both.\n\n"
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)
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full_result = self._generate_reaction(
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observation, call_to_action_template, now=now
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)
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result = full_result.strip().split("\n")[0]
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# AAA
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self.memory.save_context(
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{},
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{
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self.memory.add_memory_key: f"{self.name} observed "
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f"{observation} and reacted by {result}",
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self.memory.now_key: now,
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},
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)
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if "REACT:" in result:
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reaction = self._clean_response(result.split("REACT:")[-1])
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return False, f"{self.name} {reaction}"
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if "SAY:" in result:
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said_value = self._clean_response(result.split("SAY:")[-1])
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return True, f"{self.name} said {said_value}"
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else:
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return False, result
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def generate_dialogue_response(
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self, observation: str, now: Optional[datetime] = None
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) -> Tuple[bool, str]:
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"""React to a given observation."""
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call_to_action_template = (
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"What would {agent_name} say? To end the conversation, write:"
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' GOODBYE: "what to say". Otherwise to continue the conversation,'
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' write: SAY: "what to say next"\n\n'
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)
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full_result = self._generate_reaction(
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observation, call_to_action_template, now=now
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)
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result = full_result.strip().split("\n")[0]
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if "GOODBYE:" in result:
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farewell = self._clean_response(result.split("GOODBYE:")[-1])
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self.memory.save_context(
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{},
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{
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self.memory.add_memory_key: f"{self.name} observed "
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f"{observation} and said {farewell}",
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self.memory.now_key: now,
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},
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)
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return False, f"{self.name} said {farewell}"
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if "SAY:" in result:
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response_text = self._clean_response(result.split("SAY:")[-1])
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self.memory.save_context(
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{},
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{
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self.memory.add_memory_key: f"{self.name} observed "
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f"{observation} and said {response_text}",
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self.memory.now_key: now,
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},
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)
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return True, f"{self.name} said {response_text}"
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else:
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return False, result
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######################################################
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# Agent stateful' summary methods. #
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# Each dialog or response prompt includes a header #
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# summarizing the agent's self-description. This is #
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# updated periodically through probing its memories #
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######################################################
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def _compute_agent_summary(self) -> str:
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""""""
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prompt = PromptTemplate.from_template(
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"How would you summarize {name}'s core characteristics given the"
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+ " following statements:\n"
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+ "{relevant_memories}"
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+ "Do not embellish."
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+ "\n\nSummary: "
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)
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# The agent seeks to think about their core characteristics.
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return (
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self.chain(prompt)
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.run(name=self.name, queries=[f"{self.name}'s core characteristics"])
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.strip()
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)
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def get_summary(
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self, force_refresh: bool = False, now: Optional[datetime] = None
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) -> str:
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"""Return a descriptive summary of the agent."""
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current_time = datetime.now() if now is None else now
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since_refresh = (current_time - self.last_refreshed).seconds
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if (
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not self.summary
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or since_refresh >= self.summary_refresh_seconds
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or force_refresh
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):
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self.summary = self._compute_agent_summary()
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self.last_refreshed = current_time
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age = self.age if self.age is not None else "N/A"
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return (
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f"Name: {self.name} (age: {age})"
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+ f"\nInnate traits: {self.traits}"
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+ f"\n{self.summary}"
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)
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def get_full_header(
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self, force_refresh: bool = False, now: Optional[datetime] = None
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) -> str:
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"""Return a full header of the agent's status, summary, and current time."""
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now = datetime.now() if now is None else now
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summary = self.get_summary(force_refresh=force_refresh, now=now)
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current_time_str = now.strftime("%B %d, %Y, %I:%M %p")
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return (
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f"{summary}\nIt is {current_time_str}.\n{self.name}'s status: {self.status}"
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)
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