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 0x7f2a4dcff880>
- 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 0x7f2a4dcff880>
- 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#