Memory#

pydantic model llama_index.core.memory.BaseMemory#

Base class for all memory types.

NOTE: The interface for memory is not yet finalized and is subject to change.

Show JSON schema
{
   "title": "BaseMemory",
   "description": "Base class for all memory types.\n\nNOTE: The interface for memory is not yet finalized and is subject to change.",
   "type": "object",
   "properties": {
      "class_name": {
         "title": "Class Name",
         "type": "string",
         "default": "BaseMemory"
      }
   }
}

Config
  • schema_extra: function = <function BaseComponent.Config.schema_extra at 0x7fb2d0fa3600>

classmethod class_name() str#

Get class name.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model#

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters
  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) Dict[str, Any]#

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

abstract classmethod from_defaults(chat_history: Optional[List[ChatMessage]] = None, llm: Optional[LLM] = None) BaseMemory#

Create a chat memory from defaults.

classmethod from_dict(data: Dict[str, Any], **kwargs: Any) Self#
classmethod from_json(data_str: str, **kwargs: Any) Self#
classmethod from_orm(obj: Any) Model#
abstract get(**kwargs: Any) List[ChatMessage]#

Get chat history.

abstract get_all() List[ChatMessage]#

Get all chat history.

json(**kwargs: Any) str#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model#
classmethod parse_obj(obj: Any) Model#
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model#
abstract put(message: ChatMessage) None#

Put chat history.

abstract reset() None#

Reset chat history.

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny#
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode#
abstract set(messages: List[ChatMessage]) None#

Set chat history.

to_dict(**kwargs: Any) Dict[str, Any]#
to_json(**kwargs: Any) str#
classmethod update_forward_refs(**localns: Any) None#

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model#
pydantic model llama_index.core.memory.ChatMemoryBuffer#

Simple buffer for storing chat history.

Show JSON schema
{
   "title": "ChatMemoryBuffer",
   "description": "Simple buffer for storing chat history.",
   "type": "object",
   "properties": {
      "token_limit": {
         "title": "Token Limit",
         "type": "integer"
      },
      "chat_store": {
         "$ref": "#/definitions/BaseChatStore"
      },
      "chat_store_key": {
         "title": "Chat Store Key",
         "default": "chat_history",
         "type": "string"
      },
      "class_name": {
         "title": "Class Name",
         "type": "string",
         "default": "ChatMemoryBuffer"
      }
   },
   "required": [
      "token_limit"
   ],
   "definitions": {
      "BaseChatStore": {
         "title": "BaseChatStore",
         "description": "Base component object to capture class names.",
         "type": "object",
         "properties": {
            "class_name": {
               "title": "Class Name",
               "type": "string",
               "default": "BaseChatStore"
            }
         }
      }
   }
}

Config
  • schema_extra: function = <function BaseComponent.Config.schema_extra at 0x7fb2d0fa3600>

Fields
  • chat_store (llama_index.core.storage.chat_store.base.BaseChatStore)

  • chat_store_key (str)

  • token_limit (int)

  • tokenizer_fn (Callable[[str], List])

field chat_store: BaseChatStore [Optional]#
Validated by
  • validate_memory

field chat_store_key: str = 'chat_history'#
Validated by
  • validate_memory

field token_limit: int [Required]#
Validated by
  • validate_memory

field tokenizer_fn: Callable[[str], List] [Optional]#
Validated by
  • validate_memory

classmethod class_name() str#

Get class name.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model#

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters
  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) Dict[str, Any]#

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_defaults(chat_history: Optional[List[ChatMessage]] = None, llm: Optional[LLM] = None, chat_store: Optional[BaseChatStore] = None, chat_store_key: str = 'chat_history', token_limit: Optional[int] = None, tokenizer_fn: Optional[Callable[[str], List]] = None) ChatMemoryBuffer#

Create a chat memory buffer from an LLM.

classmethod from_dict(data: Dict[str, Any], **kwargs: Any) ChatMemoryBuffer#
classmethod from_json(data_str: str, **kwargs: Any) Self#
classmethod from_orm(obj: Any) Model#
classmethod from_string(json_str: str) ChatMemoryBuffer#

Create a chat memory buffer from a string.

get(initial_token_count: int = 0, **kwargs: Any) List[ChatMessage]#

Get chat history.

get_all() List[ChatMessage]#

Get all chat history.

json(**kwargs: Any) str#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model#
classmethod parse_obj(obj: Any) Model#
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model#
put(message: ChatMessage) None#

Put chat history.

reset() None#

Reset chat history.

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny#
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode#
set(messages: List[ChatMessage]) None#

Set chat history.

to_dict(**kwargs: Any) dict#

Convert memory to dict.

to_json(**kwargs: Any) str#
to_string() str#

Convert memory to string.

classmethod update_forward_refs(**localns: Any) None#

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model#
validator validate_memory  »  all fields#