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from tinytroupe.agent.mental_faculty import TinyMentalFaculty
from tinytroupe.agent.grounding import BaseSemanticGroundingConnector
import tinytroupe.utils as utils
from llama_index.core import Document
from typing import Any
import copy
#######################################################################################################################
# Memory mechanisms
#######################################################################################################################
class TinyMemory(TinyMentalFaculty):
"""
Base class for different types of memory.
"""
def _preprocess_value_for_storage(self, value: Any) -> Any:
"""
Preprocesses a value before storing it in memory.
"""
# by default, we don't preprocess the value
return value
def _store(self, value: Any) -> None:
"""
Stores a value in memory.
"""
raise NotImplementedError("Subclasses must implement this method.")
def store(self, value: dict) -> None:
"""
Stores a value in memory.
"""
self._store(self._preprocess_value_for_storage(value))
def store_all(self, values: list) -> None:
"""
Stores a list of values in memory.
"""
for value in values:
self.store(value)
def retrieve(self, first_n: int, last_n: int, include_omission_info:bool=True) -> list:
"""
Retrieves the first n and/or last n values from memory. If n is None, all values are retrieved.
Args:
first_n (int): The number of first values to retrieve.
last_n (int): The number of last values to retrieve.
include_omission_info (bool): Whether to include an information message when some values are omitted.
Returns:
list: The retrieved values.
"""
raise NotImplementedError("Subclasses must implement this method.")
def retrieve_recent(self) -> list:
"""
Retrieves the n most recent values from memory.
"""
raise NotImplementedError("Subclasses must implement this method.")
def retrieve_all(self) -> list:
"""
Retrieves all values from memory.
"""
raise NotImplementedError("Subclasses must implement this method.")
def retrieve_relevant(self, relevance_target:str, top_k=20) -> list:
"""
Retrieves all values from memory that are relevant to a given target.
"""
raise NotImplementedError("Subclasses must implement this method.")
class EpisodicMemory(TinyMemory):
"""
Provides episodic memory capabilities to an agent. Cognitively, episodic memory is the ability to remember specific events,
or episodes, in the past. This class provides a simple implementation of episodic memory, where the agent can store and retrieve
messages from memory.
Subclasses of this class can be used to provide different memory implementations.
"""
MEMORY_BLOCK_OMISSION_INFO = {'role': 'assistant', 'content': "Info: there were other messages here, but they were omitted for brevity.", 'simulation_timestamp': None}
def __init__(
self, fixed_prefix_length: int = 100, lookback_length: int = 100
) -> None:
"""
Initializes the memory.
Args:
fixed_prefix_length (int): The fixed prefix length. Defaults to 20.
lookback_length (int): The lookback length. Defaults to 20.
"""
self.fixed_prefix_length = fixed_prefix_length
self.lookback_length = lookback_length
self.memory = []
def _store(self, value: Any) -> None:
"""
Stores a value in memory.
"""
self.memory.append(value)
def count(self) -> int:
"""
Returns the number of values in memory.
"""
return len(self.memory)
def retrieve(self, first_n: int, last_n: int, include_omission_info:bool=True) -> list:
"""
Retrieves the first n and/or last n values from memory. If n is None, all values are retrieved.
Args:
first_n (int): The number of first values to retrieve.
last_n (int): The number of last values to retrieve.
include_omission_info (bool): Whether to include an information message when some values are omitted.
Returns:
list: The retrieved values.
"""
omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else []
# use the other methods in the class to implement
if first_n is not None and last_n is not None:
return self.retrieve_first(first_n) + omisssion_info + self.retrieve_last(last_n)
elif first_n is not None:
return self.retrieve_first(first_n)
elif last_n is not None:
return self.retrieve_last(last_n)
else:
return self.retrieve_all()
def retrieve_recent(self, include_omission_info:bool=True) -> list:
"""
Retrieves the n most recent values from memory.
"""
omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else []
# compute fixed prefix
fixed_prefix = self.memory[: self.fixed_prefix_length] + omisssion_info
# how many lookback values remain?
remaining_lookback = min(
len(self.memory) - len(fixed_prefix), self.lookback_length
)
# compute the remaining lookback values and return the concatenation
if remaining_lookback <= 0:
return fixed_prefix
else:
return fixed_prefix + self.memory[-remaining_lookback:]
def retrieve_all(self) -> list:
"""
Retrieves all values from memory.
"""
return copy.copy(self.memory)
def retrieve_relevant(self, relevance_target: str, top_k:int) -> list:
"""
Retrieves top-k values from memory that are most relevant to a given target.
"""
raise NotImplementedError("Subclasses must implement this method.")
def retrieve_first(self, n: int, include_omission_info:bool=True) -> list:
"""
Retrieves the first n values from memory.
"""
omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else []
return self.memory[:n] + omisssion_info
def retrieve_last(self, n: int, include_omission_info:bool=True) -> list:
"""
Retrieves the last n values from memory.
"""
omisssion_info = [EpisodicMemory.MEMORY_BLOCK_OMISSION_INFO] if include_omission_info else []
return omisssion_info + self.memory[-n:]
@utils.post_init
class SemanticMemory(TinyMemory):
"""
In Cognitive Psychology, semantic memory is the memory of meanings, understandings, and other concept-based knowledge unrelated to specific
experiences. It is not ordered temporally, and it is not about remembering specific events or episodes. This class provides a simple implementation
of semantic memory, where the agent can store and retrieve semantic information.
"""
serializable_attrs = ["memories"]
def __init__(self, memories: list=None) -> None:
self.memories = memories
# @post_init ensures that _post_init is called after the __init__ method
def _post_init(self):
"""
This will run after __init__, since the class has the @post_init decorator.
It is convenient to separate some of the initialization processes to make deserialize easier.
"""
if not hasattr(self, 'memories') or self.memories is None:
self.memories = []
self.semantic_grounding_connector = BaseSemanticGroundingConnector("Semantic Memory Storage")
self.semantic_grounding_connector.add_documents(self._build_documents_from(self.memories))
def _preprocess_value_for_storage(self, value: dict) -> Any:
engram = None
if value['type'] == 'action':
engram = f"# Fact\n" +\
f"I have performed the following action at date and time {value['simulation_timestamp']}:\n\n"+\
f" {value['content']}"
elif value['type'] == 'stimulus':
engram = f"# Stimulus\n" +\
f"I have received the following stimulus at date and time {value['simulation_timestamp']}:\n\n"+\
f" {value['content']}"
# else: # Anything else here?
return engram
def _store(self, value: Any) -> None:
engram_doc = self._build_document_from(self._preprocess_value_for_storage(value))
self.semantic_grounding_connector.add_document(engram_doc)
def retrieve_relevant(self, relevance_target:str, top_k=20) -> list:
"""
Retrieves all values from memory that are relevant to a given target.
"""
return self.semantic_grounding_connector.retrieve_relevant(relevance_target, top_k)
#####################################
# Auxiliary compatibility methods
#####################################
def _build_document_from(memory) -> Document:
# TODO: add any metadata as well?
return Document(text=str(memory))
def _build_documents_from(self, memories: list) -> list:
return [self._build_document_from(memory) for memory in memories]
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