Source code for langchain_experimental.generative_agents.generative_agent

import re
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple

from langchain.chains import LLMChain
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import PromptTemplate

from langchain_experimental.generative_agents.memory import GenerativeAgentMemory
from langchain_experimental.pydantic_v1 import BaseModel, Field


[docs]class GenerativeAgent(BaseModel): """Agent as a character with memory and innate characteristics.""" name: str """The character's name.""" age: Optional[int] = None """The optional age of the character.""" traits: str = "N/A" """Permanent traits to ascribe to the character.""" status: str """The traits of the character you wish not to change.""" memory: GenerativeAgentMemory """The memory object that combines relevance, recency, and 'importance'.""" llm: BaseLanguageModel """The underlying language model.""" verbose: bool = False summary: str = "" #: :meta private: """Stateful self-summary generated via reflection on the character's memory.""" summary_refresh_seconds: int = 3600 #: :meta private: """How frequently to re-generate the summary.""" last_refreshed: datetime = Field(default_factory=datetime.now) # : :meta private: """The last time the character's summary was regenerated.""" daily_summaries: List[str] = Field(default_factory=list) # : :meta private: """Summary of the events in the plan that the agent took.""" class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True # LLM-related methods @staticmethod def _parse_list(text: str) -> List[str]: """Parse a newline-separated string into a list of strings.""" lines = re.split(r"\n", text.strip()) return [re.sub(r"^\s*\d+\.\s*", "", line).strip() for line in lines]
[docs] def chain(self, prompt: PromptTemplate) -> LLMChain: """Create a chain with the same settings as the agent.""" return LLMChain( llm=self.llm, prompt=prompt, verbose=self.verbose, memory=self.memory )
def _get_entity_from_observation(self, observation: str) -> str: prompt = PromptTemplate.from_template( "What is the observed entity in the following observation? {observation}" + "\nEntity=" ) return self.chain(prompt).run(observation=observation).strip() def _get_entity_action(self, observation: str, entity_name: str) -> str: prompt = PromptTemplate.from_template( "What is the {entity} doing in the following observation? {observation}" + "\nThe {entity} is" ) return ( self.chain(prompt).run(entity=entity_name, observation=observation).strip() ) def _generate_reaction( self, observation: str, suffix: str, now: Optional[datetime] = None ) -> str: """React to a given observation or dialogue act.""" prompt = PromptTemplate.from_template( "{agent_summary_description}" + "\nIt is {current_time}." + "\n{agent_name}'s status: {agent_status}" + "\nSummary of relevant context from {agent_name}'s memory:" + "\n{relevant_memories}" + "\nMost recent observations: {most_recent_memories}" + "\nObservation: {observation}" + "\n\n" + suffix ) agent_summary_description = self.get_summary(now=now) relevant_memories_str = self.summarize_related_memories(observation) current_time_str = ( datetime.now().strftime("%B %d, %Y, %I:%M %p") if now is None else now.strftime("%B %d, %Y, %I:%M %p") ) kwargs: Dict[str, Any] = dict( agent_summary_description=agent_summary_description, current_time=current_time_str, relevant_memories=relevant_memories_str, agent_name=self.name, observation=observation, agent_status=self.status, ) consumed_tokens = self.llm.get_num_tokens( prompt.format(most_recent_memories="", **kwargs) ) kwargs[self.memory.most_recent_memories_token_key] = consumed_tokens return self.chain(prompt=prompt).run(**kwargs).strip() def _clean_response(self, text: str) -> str: return re.sub(f"^{self.name} ", "", text.strip()).strip()
[docs] def generate_reaction( self, observation: str, now: Optional[datetime] = None ) -> Tuple[bool, str]: """React to a given observation.""" 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" ) full_result = self._generate_reaction( observation, call_to_action_template, now=now ) result = full_result.strip().split("\n")[0] # AAA self.memory.save_context( {}, { self.memory.add_memory_key: f"{self.name} observed " f"{observation} and reacted by {result}", self.memory.now_key: now, }, ) if "REACT:" in result: reaction = self._clean_response(result.split("REACT:")[-1]) return False, f"{self.name} {reaction}" if "SAY:" in result: said_value = self._clean_response(result.split("SAY:")[-1]) return True, f"{self.name} said {said_value}" else: return False, result
[docs] def generate_dialogue_response( self, observation: str, now: Optional[datetime] = None ) -> Tuple[bool, str]: """React to a given observation.""" 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' ) full_result = self._generate_reaction( observation, call_to_action_template, now=now ) result = full_result.strip().split("\n")[0] if "GOODBYE:" in result: farewell = self._clean_response(result.split("GOODBYE:")[-1]) self.memory.save_context( {}, { self.memory.add_memory_key: f"{self.name} observed " f"{observation} and said {farewell}", self.memory.now_key: now, }, ) return False, f"{self.name} said {farewell}" if "SAY:" in result: response_text = self._clean_response(result.split("SAY:")[-1]) self.memory.save_context( {}, { self.memory.add_memory_key: f"{self.name} observed " f"{observation} and said {response_text}", self.memory.now_key: now, }, ) return True, f"{self.name} said {response_text}" else: return False, result
###################################################### # Agent stateful' summary methods. # # Each dialog or response prompt includes a header # # summarizing the agent's self-description. This is # # updated periodically through probing its memories # ###################################################### 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() )
[docs] def get_summary( self, force_refresh: bool = False, now: Optional[datetime] = None ) -> str: """Return a descriptive summary of the agent.""" current_time = datetime.now() if now is None else now since_refresh = (current_time - self.last_refreshed).seconds if ( not self.summary or since_refresh >= self.summary_refresh_seconds or force_refresh ): self.summary = self._compute_agent_summary() self.last_refreshed = current_time age = self.age if self.age is not None else "N/A" return ( f"Name: {self.name} (age: {age})" + f"\nInnate traits: {self.traits}" + f"\n{self.summary}" )
[docs] def get_full_header( self, force_refresh: bool = False, now: Optional[datetime] = None ) -> str: """Return a full header of the agent's status, summary, and current time.""" now = datetime.now() if now is None else now summary = self.get_summary(force_refresh=force_refresh, now=now) current_time_str = now.strftime("%B %d, %Y, %I:%M %p") return ( f"{summary}\nIt is {current_time_str}.\n{self.name}'s status: {self.status}" )