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Chat memory buffer

ChatMemoryBuffer #

Bases: BaseChatStoreMemory

Deprecated: Please use llama_index.core.memory.Memory instead.

Simple buffer for storing chat history.

Parameters:

Name Type Description Default
token_limit int
required
tokenizer_fn Callable[list, List]
<dynamic>
Source code in llama-index-core/llama_index/core/memory/chat_memory_buffer.py 
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class ChatMemoryBuffer(BaseChatStoreMemory):  """  Deprecated: Please use `llama_index.core.memory.Memory` instead.  Simple buffer for storing chat history.  """ token_limit: int tokenizer_fn: Callable[[str], List] = Field( default_factory=get_tokenizer, exclude=True, ) @classmethod def class_name(cls) -> str:  """Get class name.""" return "ChatMemoryBuffer" @model_validator(mode="before") @classmethod def validate_memory(cls, values: dict) -> dict: # Validate token limit token_limit = values.get("token_limit", -1) if token_limit < 1: raise ValueError("Token limit must be set and greater than 0.") # Validate tokenizer -- this avoids errors when loading from json/dict tokenizer_fn = values.get("tokenizer_fn") if tokenizer_fn is None: values["tokenizer_fn"] = get_tokenizer() return values @classmethod def from_defaults( cls, chat_history: Optional[List[ChatMessage]] = None, llm: Optional[LLM] = None, chat_store: Optional[BaseChatStore] = None, chat_store_key: str = DEFAULT_CHAT_STORE_KEY, token_limit: Optional[int] = None, tokenizer_fn: Optional[Callable[[str], List]] = None, **kwargs: Any, ) -> "ChatMemoryBuffer":  """Create a chat memory buffer from an LLM.""" if kwargs: raise ValueError(f"Unexpected kwargs: {kwargs}") if llm is not None: context_window = llm.metadata.context_window token_limit = token_limit or int(context_window * DEFAULT_TOKEN_LIMIT_RATIO) elif token_limit is None: token_limit = DEFAULT_TOKEN_LIMIT if chat_history is not None: chat_store = chat_store or SimpleChatStore() chat_store.set_messages(chat_store_key, chat_history) return cls( token_limit=token_limit, tokenizer_fn=tokenizer_fn or get_tokenizer(), chat_store=chat_store or SimpleChatStore(), chat_store_key=chat_store_key, ) def to_string(self) -> str:  """Convert memory to string.""" return self.json() @classmethod def from_string(cls, json_str: str) -> "ChatMemoryBuffer":  """Create a chat memory buffer from a string.""" dict_obj = json.loads(json_str) return cls.from_dict(dict_obj) def to_dict(self, **kwargs: Any) -> dict:  """Convert memory to dict.""" return self.dict() @classmethod def from_dict(cls, data: Dict[str, Any], **kwargs: Any) -> "ChatMemoryBuffer": from llama_index.core.storage.chat_store.loading import load_chat_store # NOTE: this handles backwards compatibility with the old chat history if "chat_history" in data: chat_history = data.pop("chat_history") simple_store = SimpleChatStore(store={DEFAULT_CHAT_STORE_KEY: chat_history}) data["chat_store"] = simple_store elif "chat_store" in data: chat_store_dict = data.pop("chat_store") chat_store = load_chat_store(chat_store_dict) data["chat_store"] = chat_store return cls(**data) def get( self, input: Optional[str] = None, initial_token_count: int = 0, **kwargs: Any ) -> List[ChatMessage]:  """Get chat history.""" chat_history = self.get_all() if initial_token_count > self.token_limit: raise ValueError("Initial token count exceeds token limit") message_count = len(chat_history) cur_messages = chat_history[-message_count:] token_count = self._token_count_for_messages(cur_messages) + initial_token_count while token_count > self.token_limit and message_count > 1: message_count -= 1 while chat_history[-message_count].role in ( MessageRole.TOOL, MessageRole.ASSISTANT, ): # we cannot have an assistant message at the start of the chat history # if after removal of the first, we have an assistant message, # we need to remove the assistant message too # # all tool messages should be preceded by an assistant message # if we remove a tool message, we need to remove the assistant message too message_count -= 1 cur_messages = chat_history[-message_count:] token_count = ( self._token_count_for_messages(cur_messages) + initial_token_count ) # catch one message longer than token limit if token_count > self.token_limit or message_count <= 0: return [] return chat_history[-message_count:] async def aget( self, input: Optional[str] = None, initial_token_count: int = 0, **kwargs: Any ) -> List[ChatMessage]:  """Get chat history.""" return await asyncio.to_thread( self.get, input=input, initial_token_count=initial_token_count, **kwargs ) def _token_count_for_messages(self, messages: List[ChatMessage]) -> int: if len(messages) <= 0: return 0 msg_str = " ".join(str(m.content) for m in messages) return len(self.tokenizer_fn(msg_str))

class_name classmethod #

class_name() -> str

Get class name.

Source code in llama-index-core/llama_index/core/memory/chat_memory_buffer.py 
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@classmethod def class_name(cls) -> str:  """Get class name.""" return "ChatMemoryBuffer"

from_defaults classmethod #

from_defaults(chat_history: Optional[List[ChatMessage]] = None, llm: Optional[LLM] = None, chat_store: Optional[BaseChatStore] = None, chat_store_key: str = DEFAULT_CHAT_STORE_KEY, token_limit: Optional[int] = None, tokenizer_fn: Optional[Callable[[str], List]] = None, **kwargs: Any) -> ChatMemoryBuffer

Create a chat memory buffer from an LLM.

Source code in llama-index-core/llama_index/core/memory/chat_memory_buffer.py 
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@classmethod def from_defaults( cls, chat_history: Optional[List[ChatMessage]] = None, llm: Optional[LLM] = None, chat_store: Optional[BaseChatStore] = None, chat_store_key: str = DEFAULT_CHAT_STORE_KEY, token_limit: Optional[int] = None, tokenizer_fn: Optional[Callable[[str], List]] = None, **kwargs: Any, ) -> "ChatMemoryBuffer":  """Create a chat memory buffer from an LLM.""" if kwargs: raise ValueError(f"Unexpected kwargs: {kwargs}") if llm is not None: context_window = llm.metadata.context_window token_limit = token_limit or int(context_window * DEFAULT_TOKEN_LIMIT_RATIO) elif token_limit is None: token_limit = DEFAULT_TOKEN_LIMIT if chat_history is not None: chat_store = chat_store or SimpleChatStore() chat_store.set_messages(chat_store_key, chat_history) return cls( token_limit=token_limit, tokenizer_fn=tokenizer_fn or get_tokenizer(), chat_store=chat_store or SimpleChatStore(), chat_store_key=chat_store_key, )

to_string #

to_string() -> str

Convert memory to string.

Source code in llama-index-core/llama_index/core/memory/chat_memory_buffer.py 
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def to_string(self) -> str:  """Convert memory to string.""" return self.json()

from_string classmethod #

from_string(json_str: str) -> ChatMemoryBuffer

Create a chat memory buffer from a string.

Source code in llama-index-core/llama_index/core/memory/chat_memory_buffer.py 
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@classmethod def from_string(cls, json_str: str) -> "ChatMemoryBuffer":  """Create a chat memory buffer from a string.""" dict_obj = json.loads(json_str) return cls.from_dict(dict_obj)

to_dict #

to_dict(**kwargs: Any) -> dict

Convert memory to dict.

Source code in llama-index-core/llama_index/core/memory/chat_memory_buffer.py 
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def to_dict(self, **kwargs: Any) -> dict:  """Convert memory to dict.""" return self.dict()

get #

get(input: Optional[str] = None, initial_token_count: int = 0, **kwargs: Any) -> List[ChatMessage]

Get chat history.

Source code in llama-index-core/llama_index/core/memory/chat_memory_buffer.py 
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def get( self, input: Optional[str] = None, initial_token_count: int = 0, **kwargs: Any ) -> List[ChatMessage]:  """Get chat history.""" chat_history = self.get_all() if initial_token_count > self.token_limit: raise ValueError("Initial token count exceeds token limit") message_count = len(chat_history) cur_messages = chat_history[-message_count:] token_count = self._token_count_for_messages(cur_messages) + initial_token_count while token_count > self.token_limit and message_count > 1: message_count -= 1 while chat_history[-message_count].role in ( MessageRole.TOOL, MessageRole.ASSISTANT, ): # we cannot have an assistant message at the start of the chat history # if after removal of the first, we have an assistant message, # we need to remove the assistant message too # # all tool messages should be preceded by an assistant message # if we remove a tool message, we need to remove the assistant message too message_count -= 1 cur_messages = chat_history[-message_count:] token_count = ( self._token_count_for_messages(cur_messages) + initial_token_count ) # catch one message longer than token limit if token_count > self.token_limit or message_count <= 0: return [] return chat_history[-message_count:]

aget async #

aget(input: Optional[str] = None, initial_token_count: int = 0, **kwargs: Any) -> List[ChatMessage]

Get chat history.

Source code in llama-index-core/llama_index/core/memory/chat_memory_buffer.py 
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async def aget( self, input: Optional[str] = None, initial_token_count: int = 0, **kwargs: Any ) -> List[ChatMessage]:  """Get chat history.""" return await asyncio.to_thread( self.get, input=input, initial_token_count=initial_token_count, **kwargs )