| |
- pydantic.main.BaseModel(builtins.object)
-
- AICoreBedrockBaseModel
-
- BedrockEmbeddings(AICoreBedrockBaseModel, langchain_community.embeddings.bedrock.BedrockEmbeddings)
- ChatBedrock(AICoreBedrockBaseModel, langchain_aws.chat_models.bedrock.ChatBedrock)
- ChatBedrockConverse(AICoreBedrockBaseModel, langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse)
class AICoreBedrockBaseModel(pydantic.main.BaseModel) |
|
AICoreBedrockBaseModel(*args, model_id: str = '', deployment_id: str = '', model_name: str = '', config_id: str = '', config_name: str = '', proxy_client: Optional[gen_ai_hub.proxy.core.base.BaseProxyClient] = None, **kwargs) -> None
AICoreBedrockBaseModel provides all adjustments
to boto3 based LangChain classes to enable communication
with SAP AI Core. |
|
- Method resolution order:
- AICoreBedrockBaseModel
- pydantic.main.BaseModel
- builtins.object
Methods defined here:
- __init__(self, *args, model_id: str = '', deployment_id: str = '', model_name: str = '', config_id: str = '', config_name: str = '', proxy_client: Optional[gen_ai_hub.proxy.core.base.BaseProxyClient] = None, **kwargs)
- Extends the constructor of the base class with aicore specific parameters.
Class methods defined here:
- get_corresponding_model_id(model_name) from pydantic._internal._model_construction.ModelMetaclass
- validate_environment(values: Dict) -> Dict from pydantic._internal._model_construction.ModelMetaclass
- # pylint: disable=no-self-argument
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.proxy.langchain.amazon.AICoreBedrockBaseModel'>, 'config': {'extra_fields_behavior': 'allow', 'title': 'AICoreBedrockBaseModel'}, 'custom_init': True, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche....proxy.langchain.amazon.AICoreBedrockBaseModel'>>]}, 'ref': 'gen_ai_hub.proxy.langchain.amazon.AICoreBedrockBaseModel:139976652156736', 'root_model': False, 'schema': {'function': {'function': <bound method AICoreBedrockBaseModel.validate_en....proxy.langchain.amazon.AICoreBedrockBaseModel'>>, 'type': 'no-info'}, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'AICoreBedrockBaseModel', 'type': 'model-fields'}, 'type': 'function-before'}, 'type': 'model'}
- __pydantic_custom_init__ = True
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...coratorInfo(mode='before'))}, computed_fields={})
- __pydantic_fields__ = {}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri...ICoreBedrockBaseModel",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="AICoreBedrockBaseModel", ...l",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*args, model_id: str = '', deploymen....base.BaseProxyClient] = None, **kwargs) -> None>
- model_config = {'extra': 'allow'}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/serialization.md#model_copy)
Returns a copy of the model.
!!! note
The underlying instance's [`__dict__`][object.__dict__] attribute is copied. This
might have unexpected side effects if you store anything in it, on top of the model
fields (e.g. the value of [cached properties][functools.cached_property]).
Args:
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.
- model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
fallback: A function to call when an unknown value is encountered. If not provided,
a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A dictionary representation of the model.
- model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
fallback: A function to call when an unknown value is encountered. If not provided,
a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
schema_generator: To override the logic used to generate the JSON schema, as a subclass of
`GenerateJsonSchema` with your desired modifications
mode: The mode in which to generate the schema.
Returns:
The JSON schema for the given model class.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Validate a pydantic model instance.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
from_attributes: Whether to extract data from object attributes.
context: Additional context to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Raises:
ValidationError: If the object could not be validated.
Returns:
The validated model instance.
- model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- !!! abstract "Usage Documentation"
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Returns:
The validated Pydantic model.
Raises:
ValidationError: If `json_data` is not a JSON string or the object could not be validated.
- model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Validate the given object with string data against the Pydantic model.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Returns:
The validated Pydantic model.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __annotations__ = {}
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {}
|
class BedrockEmbeddings(AICoreBedrockBaseModel, langchain_community.embeddings.bedrock.BedrockEmbeddings) |
|
BedrockEmbeddings(*args, client: Any = None, region_name: Optional[str] = None, credentials_profile_name: Optional[str] = None, model_id: str = 'amazon.titan-embed-text-v1', model_kwargs: Optional[Dict] = None, endpoint_url: Optional[str] = None, normalize: bool = False, **kwargs) -> None
Drop-in replacement for LangChain BedrockEmbeddings. |
|
- Method resolution order:
- BedrockEmbeddings
- AICoreBedrockBaseModel
- langchain_community.embeddings.bedrock.BedrockEmbeddings
- pydantic.main.BaseModel
- langchain_core.embeddings.embeddings.Embeddings
- abc.ABC
- builtins.object
Methods defined here:
- __init__(self, *args, **kwargs)
- Extends the constructor of the base class with aicore specific parameters.
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.proxy.langchain.amazon.BedrockEmbeddings'>, 'config': {'extra_fields_behavior': 'allow', 'title': 'BedrockEmbeddings'}, 'custom_init': True, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...i_hub.proxy.langchain.amazon.BedrockEmbeddings'>>]}, 'ref': 'gen_ai_hub.proxy.langchain.amazon.BedrockEmbeddings:139976652169840', 'root_model': False, 'schema': {'function': {'function': <bound method AICoreBedrockBaseModel.validate_en...i_hub.proxy.langchain.amazon.BedrockEmbeddings'>>, 'type': 'no-info'}, 'schema': {'computed_fields': [], 'fields': {'client': {'metadata': {}, 'schema': {'default': None, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'credentials_profile_name': {'metadata': {}, 'schema': {'default': None, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'endpoint_url': {'metadata': {}, 'schema': {'default': None, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'model_id': {'metadata': {}, 'schema': {'default': 'amazon.titan-embed-text-v1', 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'model_kwargs': {'metadata': {}, 'schema': {'default': None, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'normalize': {'metadata': {}, 'schema': {'default': False, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'region_name': {'metadata': {}, 'schema': {'default': None, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'BedrockEmbeddings', 'type': 'model-fields'}, 'type': 'function-before'}, 'type': 'model'}
- __pydantic_custom_init__ = True
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...coratorInfo(mode='before'))}, computed_fields={})
- __pydantic_fields__ = {'client': FieldInfo(annotation=Any, required=False, default=None), 'credentials_profile_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'endpoint_url': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'model_id': FieldInfo(annotation=str, required=False, default='amazon.titan-embed-text-v1'), 'model_kwargs': FieldInfo(annotation=Union[Dict, NoneType], required=False, default=None), 'normalize': FieldInfo(annotation=bool, required=False, default=False), 'region_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri...e: "BedrockEmbeddings",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="BedrockEmbeddings", valid...s",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*args, client: Any = None, region_na...None, normalize: bool = False, **kwargs) -> None>
- model_config = {'extra': 'allow', 'protected_namespaces': ()}
Class methods inherited from AICoreBedrockBaseModel:
- get_corresponding_model_id(model_name) from pydantic._internal._model_construction.ModelMetaclass
- validate_environment(values: Dict) -> Dict from pydantic._internal._model_construction.ModelMetaclass
- Validate that AWS credentials to and python package exists in environment.
Data descriptors inherited from AICoreBedrockBaseModel:
- __weakref__
- list of weak references to the object (if defined)
Methods inherited from langchain_community.embeddings.bedrock.BedrockEmbeddings:
- async aembed_documents(self, texts: List[str]) -> List[List[float]]
- Asynchronous compute doc embeddings using a Bedrock model.
Args:
texts: The list of texts to embed
Returns:
List of embeddings, one for each text.
- async aembed_query(self, text: str) -> List[float]
- Asynchronous compute query embeddings using a Bedrock model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
- embed_documents(self, texts: List[str]) -> List[List[float]]
- Compute doc embeddings using a Bedrock model.
Args:
texts: The list of texts to embed
Returns:
List of embeddings, one for each text.
- embed_query(self, text: str) -> List[float]
- Compute query embeddings using a Bedrock model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
Data and other attributes inherited from langchain_community.embeddings.bedrock.BedrockEmbeddings:
- __annotations__ = {'client': typing.Any, 'credentials_profile_name': typing.Optional[str], 'endpoint_url': typing.Optional[str], 'model_id': <class 'str'>, 'model_kwargs': typing.Optional[typing.Dict], 'normalize': <class 'bool'>, 'region_name': typing.Optional[str]}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/serialization.md#model_copy)
Returns a copy of the model.
!!! note
The underlying instance's [`__dict__`][object.__dict__] attribute is copied. This
might have unexpected side effects if you store anything in it, on top of the model
fields (e.g. the value of [cached properties][functools.cached_property]).
Args:
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.
- model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
fallback: A function to call when an unknown value is encountered. If not provided,
a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A dictionary representation of the model.
- model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
fallback: A function to call when an unknown value is encountered. If not provided,
a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
schema_generator: To override the logic used to generate the JSON schema, as a subclass of
`GenerateJsonSchema` with your desired modifications
mode: The mode in which to generate the schema.
Returns:
The JSON schema for the given model class.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Validate a pydantic model instance.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
from_attributes: Whether to extract data from object attributes.
context: Additional context to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Raises:
ValidationError: If the object could not be validated.
Returns:
The validated model instance.
- model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- !!! abstract "Usage Documentation"
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Returns:
The validated Pydantic model.
Raises:
ValidationError: If `json_data` is not a JSON string or the object could not be validated.
- model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Validate the given object with string data against the Pydantic model.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Returns:
The validated Pydantic model.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'client': FieldInfo(annotation=Any, required=False, default=None), 'credentials_profile_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'endpoint_url': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'model_id': FieldInfo(annotation=str, required=False, default='amazon.titan-embed-text-v1'), 'model_kwargs': FieldInfo(annotation=Union[Dict, NoneType], required=False, default=None), 'normalize': FieldInfo(annotation=bool, required=False, default=False), 'region_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
|
class ChatBedrock(AICoreBedrockBaseModel, langchain_aws.chat_models.bedrock.ChatBedrock) |
|
ChatBedrock(*args, name: Optional[str] = None, cache: Union[langchain_core.caches.BaseCache, bool, NoneType] = None, verbose: bool = <factory>, callbacks: Union[list[langchain_core.callbacks.base.BaseCallbackHandler], langchain_core.callbacks.base.BaseCallbackManager, NoneType] = None, tags: Optional[list[str]] = None, metadata: Optional[dict[str, Any]] = None, custom_get_token_ids: Optional[Callable[[str], list[int]]] = None, client: Any = None, region: Optional[str] = None, credentials_profile_name: Optional[str] = None, aws_access_key_id: Optional[pydantic.types.SecretStr] = <factory>, aws_secret_access_key: Optional[pydantic.types.SecretStr] = <factory>, aws_session_token: Optional[pydantic.types.SecretStr] = <factory>, config: Any = None, provider: Optional[str] = None, model: str, model_kwargs: Optional[Dict[str, Any]] = None, endpoint_url: Optional[str] = None, streaming: bool = False, provider_stop_sequence_key_name_map: Mapping[str, str] = {'anthropic': 'stop_sequences', 'amazon': 'stopSequences', 'ai21': 'stop_sequences', 'cohere': 'stop_sequences', 'mistral': 'stop_sequences'}, provider_stop_reason_key_map: Mapping[str, str] = {'anthropic': 'stop_reason', 'amazon': 'completionReason', 'ai21': 'finishReason', 'cohere': 'finish_reason', 'mistral': 'stop_reason'}, guardrails: Optional[Mapping[str, Any]] = {'trace': None, 'guardrailIdentifier': None, 'guardrailVersion': None}, temperature: Optional[float] = None, max_tokens: Optional[int] = None, callback_manager: Optional[langchain_core.callbacks.base.BaseCallbackManager] = None, rate_limiter: Optional[langchain_core.rate_limiters.BaseRateLimiter] = None, disable_streaming: Union[bool, Literal['tool_calling']] = False, system_prompt_with_tools: str = '', beta_use_converse_api: bool = False, stop: Optional[List[str]] = None, **kwargs) -> None
Drop-in replacement for LangChain ChatBedrock. |
|
- Method resolution order:
- ChatBedrock
- AICoreBedrockBaseModel
- langchain_aws.chat_models.bedrock.ChatBedrock
- langchain_core.language_models.chat_models.BaseChatModel
- langchain_core.language_models.base.BaseLanguageModel[BaseMessage]
- langchain_aws.llms.bedrock.BedrockBase
- langchain_core.language_models.base.BaseLanguageModel
- langchain_core.runnables.base.RunnableSerializable[Union[PromptValue, str, Sequence[Union[BaseMessage, list[str], tuple[str, str], str, dict[str, Any]]]], ~LanguageModelOutputVar]
- langchain_core.runnables.base.RunnableSerializable
- langchain_core.load.serializable.Serializable
- pydantic.main.BaseModel
- langchain_core.runnables.base.Runnable
- typing.Generic
- abc.ABC
- builtins.object
Methods defined here:
- __init__(self, *args, **kwargs)
- Extends the constructor of the base class with aicore specific parameters.
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __class_vars__ = set()
- __parameters__ = ()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.proxy.langchain.amazon.ChatBedrock'>, 'config': {'extra_fields_behavior': 'allow', 'title': 'ChatBedrock', 'validate_by_alias': True, 'validate_by_name': True}, 'custom_init': True, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...'gen_ai_hub.proxy.langchain.amazon.ChatBedrock'>>]}, 'ref': 'gen_ai_hub.proxy.langchain.amazon.ChatBedrock:139976652157744', 'root_model': False, 'schema': {'function': {'function': <bound method ChatBedrock.set_beta_use_converse_...'gen_ai_hub.proxy.langchain.amazon.ChatBedrock'>>, 'type': 'no-info'}, 'schema': {'function': {'function': <bound method BaseChatModel.raise_deprecation of...'gen_ai_hub.proxy.langchain.amazon.ChatBedrock'>>, 'type': 'no-info'}, 'schema': {'function': {'function': <bound method AICoreBedrockBaseModel.validate_en...'gen_ai_hub.proxy.langchain.amazon.ChatBedrock'>>, 'type': 'no-info'}, 'schema': {'computed_fields': [], 'fields': {'aws_access_key_id': {...}, 'aws_secret_access_key': {...}, 'aws_session_token': {...}, 'beta_use_converse_api': {...}, 'cache': {...}, 'callback_manager': {...}, 'callbacks': {...}, 'client': {...}, 'config': {...}, 'credentials_profile_name': {...}, ...}, 'model_name': 'ChatBedrock', 'type': 'model-fields'}, 'type': 'function-before'}, 'type': 'function-before'}, 'type': 'function-before'}, 'type': 'model'}
- __pydantic_custom_init__ = True
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...coratorInfo(mode='before'))}, computed_fields={})
- __pydantic_fields__ = {'aws_access_key_id': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'aws_secret_access_key': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'aws_session_token': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'beta_use_converse_api': FieldInfo(annotation=bool, required=False, default=False), 'cache': FieldInfo(annotation=Union[BaseCache, bool, NoneType], required=False, default=None, exclude=True), 'callback_manager': FieldInfo(annotation=Union[BaseCallbackManager, ... manager to add to the run trace.', exclude=True), 'callbacks': FieldInfo(annotation=Union[list[BaseCallbackHand...ype], required=False, default=None, exclude=True), 'client': FieldInfo(annotation=Any, required=False, default=None, exclude=True), 'config': FieldInfo(annotation=Any, required=False, default=None), 'credentials_profile_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, exclude=True), ...}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri... name: "ChatBedrock",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="ChatBedrock", validator=M...k",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*args, name: Optional[str] = None, c...p: Optional[List[str]] = None, **kwargs) -> None>
- model_config = {'arbitrary_types_allowed': True, 'extra': 'allow', 'populate_by_name': True, 'protected_namespaces': (), 'validate_by_alias': True, 'validate_by_name': True}
Class methods inherited from AICoreBedrockBaseModel:
- get_corresponding_model_id(model_name) from pydantic._internal._model_construction.ModelMetaclass
- validate_environment(values: Dict) -> Dict from pydantic._internal._model_construction.ModelMetaclass
- Validate that AWS credentials to and python package exists in environment.
Data descriptors inherited from AICoreBedrockBaseModel:
- __weakref__
- list of weak references to the object (if defined)
Methods inherited from langchain_aws.chat_models.bedrock.ChatBedrock:
- bind_tools(self, tools: Sequence[Union[Dict[str, Any], type[pydantic.main.BaseModel], Callable, langchain_core.tools.base.BaseTool]], *, tool_choice: Union[dict, str, Literal['auto', 'none'], bool, NoneType] = None, **kwargs: Any) -> langchain_core.runnables.base.Runnable[typing.Union[langchain_core.prompt_values.PromptValue, str, collections.abc.Sequence[typing.Union[langchain_core.messages.base.BaseMessage, list[str], tuple[str, str], str, dict[str, typing.Any]]]], langchain_core.messages.base.BaseMessage]
- Bind tool-like objects to this chat model.
Assumes model has a tool calling API.
Args:
tools: A list of tool definitions to bind to this chat model.
Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic
models, callables, and BaseTools will be automatically converted to
their schema dictionary representation.
tool_choice: Which tool to require the model to call.
Must be the name of the single provided function or
"auto" to automatically determine which function to call
(if any), or a dict of the form:
{"type": "function", "function": {"name": <<tool_name>>}}.
**kwargs: Any additional parameters to pass to the
:class:`~langchain.runnable.Runnable` constructor.
- get_num_tokens(self, text: str) -> int
- Get the number of tokens present in the text.
Useful for checking if an input fits in a model's context window.
Args:
text: The string input to tokenize.
Returns:
The integer number of tokens in the text.
- get_token_ids(self, text: str) -> List[int]
- Return the ordered ids of the tokens in a text.
Args:
text: The string input to tokenize.
Returns:
A list of ids corresponding to the tokens in the text, in order they occur
in the text.
- set_system_prompt_with_tools(self, xml_tools_system_prompt: str) -> None
- Workaround to bind. Sets the system prompt with tools
- with_structured_output(self, schema: Union[Dict, type[pydantic.main.BaseModel]], *, include_raw: bool = False, **kwargs: Any) -> langchain_core.runnables.base.Runnable[typing.Union[langchain_core.prompt_values.PromptValue, str, collections.abc.Sequence[typing.Union[langchain_core.messages.base.BaseMessage, list[str], tuple[str, str], str, dict[str, typing.Any]]]], typing.Union[typing.Dict, pydantic.main.BaseModel]]
- Model wrapper that returns outputs formatted to match the given schema.
Args:
schema: The output schema as a dict or a Pydantic class. If a Pydantic class
then the model output will be an object of that class. If a dict then
the model output will be a dict. With a Pydantic class the returned
attributes will be validated, whereas with a dict they will not be.
include_raw: If False then only the parsed structured output is returned. If
an error occurs during model output parsing it will be raised. If True
then both the raw model response (a BaseMessage) and the parsed model
response will be returned. If an error occurs during output parsing it
will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error".
Returns:
A Runnable that takes any ChatModel input. The output type depends on
include_raw and schema.
If include_raw is True then output is a dict with keys:
raw: BaseMessage,
parsed: Optional[_DictOrPydantic],
parsing_error: Optional[BaseException],
If include_raw is False and schema is a Dict then the runnable outputs a Dict.
If include_raw is False and schema is a Type[BaseModel] then the runnable
outputs a BaseModel.
Example: Pydantic schema (include_raw=False):
.. code-block:: python
from langchain_aws.chat_models.bedrock import ChatBedrock
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm =ChatBedrock(
model_id="anthropic.claude-3-sonnet-20240229-v1:0",
model_kwargs={"temperature": 0.001},
) # type: ignore[call-arg]
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> AnswerWithJustification(
# answer='They weigh the same',
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
Example: Pydantic schema (include_raw=True):
.. code-block:: python
from langchain_aws.chat_models.bedrock import ChatBedrock
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm =ChatBedrock(
model_id="anthropic.claude-3-sonnet-20240229-v1:0",
model_kwargs={"temperature": 0.001},
) # type: ignore[call-arg]
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
# 'parsing_error': None
# }
Example: Dict schema (include_raw=False):
.. code-block:: python
from langchain_aws.chat_models.bedrock import ChatBedrock
schema = {
"name": "AnswerWithJustification",
"description": "An answer to the user question along with justification for the answer.",
"input_schema": {
"type": "object",
"properties": {
"answer": {"type": "string"},
"justification": {"type": "string"},
},
"required": ["answer", "justification"]
}
}
llm =ChatBedrock(
model_id="anthropic.claude-3-sonnet-20240229-v1:0",
model_kwargs={"temperature": 0.001},
) # type: ignore[call-arg]
structured_llm = llm.with_structured_output(schema)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
Class methods inherited from langchain_aws.chat_models.bedrock.ChatBedrock:
- get_lc_namespace() -> List[str] from pydantic._internal._model_construction.ModelMetaclass
- Get the namespace of the langchain object.
- is_lc_serializable() -> bool from pydantic._internal._model_construction.ModelMetaclass
- Return whether this model can be serialized by Langchain.
- set_beta_use_converse_api(values: Dict) -> Any from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from langchain_aws.chat_models.bedrock.ChatBedrock:
- lc_attributes
- List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
Default is an empty dictionary.
Data and other attributes inherited from langchain_aws.chat_models.bedrock.ChatBedrock:
- __annotations__ = {'beta_use_converse_api': <class 'bool'>, 'stop_sequences': typing.Optional[typing.List[str]], 'system_prompt_with_tools': <class 'str'>}
Methods inherited from langchain_core.language_models.chat_models.BaseChatModel:
- __call__(self, messages: 'list[BaseMessage]', stop: 'Optional[list[str]]' = None, callbacks: 'Callbacks' = None, **kwargs: 'Any') -> 'BaseMessage'
- .. deprecated:: 0.1.7 Use :meth:`~invoke` instead. It will not be removed until langchain-core==1.0.
Call the model.
Args:
messages: List of messages.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
The model output message.
- async agenerate(self, messages: 'list[list[BaseMessage]]', stop: 'Optional[list[str]]' = None, callbacks: 'Callbacks' = None, *, tags: 'Optional[list[str]]' = None, metadata: 'Optional[dict[str, Any]]' = None, run_name: 'Optional[str]' = None, run_id: 'Optional[uuid.UUID]' = None, **kwargs: 'Any') -> 'LLMResult'
- Asynchronously pass a sequence of prompts to a model and return generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
messages: List of list of messages.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
tags: The tags to apply.
metadata: The metadata to apply.
run_name: The name of the run.
run_id: The ID of the run.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- async agenerate_prompt(self, prompts: 'list[PromptValue]', stop: 'Optional[list[str]]' = None, callbacks: 'Callbacks' = None, **kwargs: 'Any') -> 'LLMResult'
- Asynchronously pass a sequence of prompts and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
prompts: List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- async ainvoke(self, input: 'LanguageModelInput', config: 'Optional[RunnableConfig]' = None, *, stop: 'Optional[list[str]]' = None, **kwargs: 'Any') -> 'BaseMessage'
- Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the Runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
- async apredict(self, text: 'str', *, stop: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'str'
- .. deprecated:: 0.1.7 Use :meth:`~ainvoke` instead. It will not be removed until langchain-core==1.0.
- async apredict_messages(self, messages: 'list[BaseMessage]', *, stop: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'BaseMessage'
- .. deprecated:: 0.1.7 Use :meth:`~ainvoke` instead. It will not be removed until langchain-core==1.0.
- async astream(self, input: 'LanguageModelInput', config: 'Optional[RunnableConfig]' = None, *, stop: 'Optional[list[str]]' = None, **kwargs: 'Any') -> 'AsyncIterator[BaseMessageChunk]'
- Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
Args:
input: The input to the Runnable.
config: The config to use for the Runnable. Defaults to None.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
The output of the Runnable.
- call_as_llm(self, message: 'str', stop: 'Optional[list[str]]' = None, **kwargs: 'Any') -> 'str'
- .. deprecated:: 0.1.7 Use :meth:`~invoke` instead. It will not be removed until langchain-core==1.0.
Call the model.
Args:
message: The input message.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
The model output string.
- dict(self, **kwargs: 'Any') -> 'dict'
- Return a dictionary of the LLM.
- generate(self, messages: 'list[list[BaseMessage]]', stop: 'Optional[list[str]]' = None, callbacks: 'Callbacks' = None, *, tags: 'Optional[list[str]]' = None, metadata: 'Optional[dict[str, Any]]' = None, run_name: 'Optional[str]' = None, run_id: 'Optional[uuid.UUID]' = None, **kwargs: 'Any') -> 'LLMResult'
- Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
messages: List of list of messages.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
tags: The tags to apply.
metadata: The metadata to apply.
run_name: The name of the run.
run_id: The ID of the run.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- generate_prompt(self, prompts: 'list[PromptValue]', stop: 'Optional[list[str]]' = None, callbacks: 'Callbacks' = None, **kwargs: 'Any') -> 'LLMResult'
- Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
prompts: List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- invoke(self, input: 'LanguageModelInput', config: 'Optional[RunnableConfig]' = None, *, stop: 'Optional[list[str]]' = None, **kwargs: 'Any') -> 'BaseMessage'
- Transform a single input into an output.
Args:
input: The input to the Runnable.
config: A config to use when invoking the Runnable.
The config supports standard keys like 'tags', 'metadata' for tracing
purposes, 'max_concurrency' for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns:
The output of the Runnable.
- predict(self, text: 'str', *, stop: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'str'
- .. deprecated:: 0.1.7 Use :meth:`~invoke` instead. It will not be removed until langchain-core==1.0.
Predict the next message.
Args:
text: The input message.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
The predicted output string.
- predict_messages(self, messages: 'list[BaseMessage]', *, stop: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'BaseMessage'
- .. deprecated:: 0.1.7 Use :meth:`~invoke` instead. It will not be removed until langchain-core==1.0.
- stream(self, input: 'LanguageModelInput', config: 'Optional[RunnableConfig]' = None, *, stop: 'Optional[list[str]]' = None, **kwargs: 'Any') -> 'Iterator[BaseMessageChunk]'
- Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
Args:
input: The input to the Runnable.
config: The config to use for the Runnable. Defaults to None.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
The output of the Runnable.
Class methods inherited from langchain_core.language_models.chat_models.BaseChatModel:
- raise_deprecation(values: 'dict') -> 'Any' from pydantic._internal._model_construction.ModelMetaclass
- Raise deprecation warning if callback_manager is used.
Args:
values (Dict): Values to validate.
Returns:
Dict: Validated values.
Raises:
DeprecationWarning: If callback_manager is used.
Readonly properties inherited from langchain_core.language_models.chat_models.BaseChatModel:
- OutputType
- Get the output type for this runnable.
Readonly properties inherited from langchain_aws.llms.bedrock.BedrockBase:
- lc_secrets
- A map of constructor argument names to secret ids.
For example,
{"openai_api_key": "OPENAI_API_KEY"}
Methods inherited from langchain_core.language_models.base.BaseLanguageModel:
- get_num_tokens_from_messages(self, messages: 'list[BaseMessage]', tools: 'Optional[Sequence]' = None) -> 'int'
- Get the number of tokens in the messages.
Useful for checking if an input fits in a model's context window.
**Note**: the base implementation of get_num_tokens_from_messages ignores
tool schemas.
Args:
messages: The message inputs to tokenize.
tools: If provided, sequence of dict, BaseModel, function, or BaseTools
to be converted to tool schemas.
Returns:
The sum of the number of tokens across the messages.
Class methods inherited from langchain_core.language_models.base.BaseLanguageModel:
- set_verbose(verbose: 'Optional[bool]') -> 'bool' from pydantic._internal._model_construction.ModelMetaclass
- If verbose is None, set it.
This allows users to pass in None as verbose to access the global setting.
Args:
verbose: The verbosity setting to use.
Returns:
The verbosity setting to use.
Readonly properties inherited from langchain_core.language_models.base.BaseLanguageModel:
- InputType
- Get the input type for this runnable.
Methods inherited from langchain_core.runnables.base.RunnableSerializable:
- configurable_alternatives(self, which: 'ConfigurableField', *, default_key: 'str' = 'default', prefix_keys: 'bool' = False, **kwargs: 'Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]') -> 'RunnableSerializable[Input, Output]'
- Configure alternatives for Runnables that can be set at runtime.
Args:
which: The ConfigurableField instance that will be used to select the
alternative.
default_key: The default key to use if no alternative is selected.
Defaults to "default".
prefix_keys: Whether to prefix the keys with the ConfigurableField id.
Defaults to False.
**kwargs: A dictionary of keys to Runnable instances or callables that
return Runnable instances.
Returns:
A new Runnable with the alternatives configured.
.. code-block:: python
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatAnthropic(
model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
ConfigurableField(id="llm"),
default_key="anthropic",
openai=ChatOpenAI()
)
# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)
# uses ChatOpenAI
print(
model.with_config(
configurable={"llm": "openai"}
).invoke("which organization created you?").content
)
- configurable_fields(self, **kwargs: 'AnyConfigurableField') -> 'RunnableSerializable[Input, Output]'
- Configure particular Runnable fields at runtime.
Args:
**kwargs: A dictionary of ConfigurableField instances to configure.
Returns:
A new Runnable with the fields configured.
.. code-block:: python
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(max_tokens=20).configurable_fields(
max_tokens=ConfigurableField(
id="output_token_number",
name="Max tokens in the output",
description="The maximum number of tokens in the output",
)
)
# max_tokens = 20
print(
"max_tokens_20: ",
model.invoke("tell me something about chess").content
)
# max_tokens = 200
print("max_tokens_200: ", model.with_config(
configurable={"output_token_number": 200}
).invoke("tell me something about chess").content
)
- to_json(self) -> 'Union[SerializedConstructor, SerializedNotImplemented]'
- Serialize the Runnable to JSON.
Returns:
A JSON-serializable representation of the Runnable.
Data and other attributes inherited from langchain_core.runnables.base.RunnableSerializable:
- __orig_bases__ = (<class 'langchain_core.load.serializable.Serializable'>, langchain_core.runnables.base.Runnable[-Input, +Output])
Methods inherited from langchain_core.load.serializable.Serializable:
- __repr_args__(self) -> Any
- to_json_not_implemented(self) -> langchain_core.load.serializable.SerializedNotImplemented
- Serialize a "not implemented" object.
Class methods inherited from langchain_core.load.serializable.Serializable:
- lc_id() -> list[str] from pydantic._internal._model_construction.ModelMetaclass
- A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
For example, for the class `langchain.llms.openai.OpenAI`, the id is
["langchain", "llms", "openai", "OpenAI"].
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/serialization.md#model_copy)
Returns a copy of the model.
!!! note
The underlying instance's [`__dict__`][object.__dict__] attribute is copied. This
might have unexpected side effects if you store anything in it, on top of the model
fields (e.g. the value of [cached properties][functools.cached_property]).
Args:
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.
- model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
fallback: A function to call when an unknown value is encountered. If not provided,
a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A dictionary representation of the model.
- model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
fallback: A function to call when an unknown value is encountered. If not provided,
a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
schema_generator: To override the logic used to generate the JSON schema, as a subclass of
`GenerateJsonSchema` with your desired modifications
mode: The mode in which to generate the schema.
Returns:
The JSON schema for the given model class.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Validate a pydantic model instance.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
from_attributes: Whether to extract data from object attributes.
context: Additional context to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Raises:
ValidationError: If the object could not be validated.
Returns:
The validated model instance.
- model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- !!! abstract "Usage Documentation"
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Returns:
The validated Pydantic model.
Raises:
ValidationError: If `json_data` is not a JSON string or the object could not be validated.
- model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Validate the given object with string data against the Pydantic model.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Returns:
The validated Pydantic model.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'aws_access_key_id': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'aws_secret_access_key': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'aws_session_token': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'beta_use_converse_api': FieldInfo(annotation=bool, required=False, default=False), 'cache': FieldInfo(annotation=Union[BaseCache, bool, NoneType], required=False, default=None, exclude=True), 'callback_manager': FieldInfo(annotation=Union[BaseCallbackManager, ... manager to add to the run trace.', exclude=True), 'callbacks': FieldInfo(annotation=Union[list[BaseCallbackHand...ype], required=False, default=None, exclude=True), 'client': FieldInfo(annotation=Any, required=False, default=None, exclude=True), 'config': FieldInfo(annotation=Any, required=False, default=None), 'credentials_profile_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, exclude=True), ...}
Methods inherited from langchain_core.runnables.base.Runnable:
- __or__(self, other: 'Union[Runnable[Any, Other], Callable[[Iterator[Any]], Iterator[Other]], Callable[[AsyncIterator[Any]], AsyncIterator[Other]], Callable[[Any], Other], Mapping[str, Union[Runnable[Any, Other], Callable[[Any], Other], Any]]]') -> 'RunnableSerializable[Input, Other]'
- Compose this Runnable with another object to create a RunnableSequence.
- __ror__(self, other: 'Union[Runnable[Other, Any], Callable[[Iterator[Other]], Iterator[Any]], Callable[[AsyncIterator[Other]], AsyncIterator[Any]], Callable[[Other], Any], Mapping[str, Union[Runnable[Other, Any], Callable[[Other], Any], Any]]]') -> 'RunnableSerializable[Other, Output]'
- Compose this Runnable with another object to create a RunnableSequence.
- async abatch(self, inputs: 'list[Input]', config: 'Optional[Union[RunnableConfig, list[RunnableConfig]]]' = None, *, return_exceptions: 'bool' = False, **kwargs: 'Optional[Any]') -> 'list[Output]'
- Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying Runnable uses an API which supports a batch mode.
Args:
inputs: A list of inputs to the Runnable.
config: A config to use when invoking the Runnable.
The config supports standard keys like 'tags', 'metadata' for tracing
purposes, 'max_concurrency' for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details. Defaults to None.
return_exceptions: Whether to return exceptions instead of raising them.
Defaults to False.
kwargs: Additional keyword arguments to pass to the Runnable.
Returns:
A list of outputs from the Runnable.
- async abatch_as_completed(self, inputs: 'Sequence[Input]', config: 'Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]' = None, *, return_exceptions: 'bool' = False, **kwargs: 'Optional[Any]') -> 'AsyncIterator[tuple[int, Union[Output, Exception]]]'
- Run ainvoke in parallel on a list of inputs.
Yields results as they complete.
Args:
inputs: A list of inputs to the Runnable.
config: A config to use when invoking the Runnable.
The config supports standard keys like 'tags', 'metadata' for tracing
purposes, 'max_concurrency' for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details. Defaults to None. Defaults to None.
return_exceptions: Whether to return exceptions instead of raising them.
Defaults to False.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
A tuple of the index of the input and the output from the Runnable.
- as_tool(self, args_schema: 'Optional[type[BaseModel]]' = None, *, name: 'Optional[str]' = None, description: 'Optional[str]' = None, arg_types: 'Optional[dict[str, type]]' = None) -> 'BaseTool'
- .. beta::
This API is in beta and may change in the future.
Create a BaseTool from a Runnable.
``as_tool`` will instantiate a BaseTool with a name, description, and
``args_schema`` from a Runnable. Where possible, schemas are inferred
from ``runnable.get_input_schema``. Alternatively (e.g., if the
Runnable takes a dict as input and the specific dict keys are not typed),
the schema can be specified directly with ``args_schema``. You can also
pass ``arg_types`` to just specify the required arguments and their types.
Args:
args_schema: The schema for the tool. Defaults to None.
name: The name of the tool. Defaults to None.
description: The description of the tool. Defaults to None.
arg_types: A dictionary of argument names to types. Defaults to None.
Returns:
A BaseTool instance.
Typed dict input:
.. code-block:: python
from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda
class Args(TypedDict):
a: int
b: list[int]
def f(x: Args) -> str:
return str(x["a"] * max(x["b"]))
runnable = RunnableLambda(f)
as_tool = runnable.as_tool()
as_tool.invoke({"a": 3, "b": [1, 2]})
``dict`` input, specifying schema via ``args_schema``:
.. code-block:: python
from typing import Any
from pydantic import BaseModel, Field
from langchain_core.runnables import RunnableLambda
def f(x: dict[str, Any]) -> str:
return str(x["a"] * max(x["b"]))
class FSchema(BaseModel):
"""Apply a function to an integer and list of integers."""
a: int = Field(..., description="Integer")
b: list[int] = Field(..., description="List of ints")
runnable = RunnableLambda(f)
as_tool = runnable.as_tool(FSchema)
as_tool.invoke({"a": 3, "b": [1, 2]})
``dict`` input, specifying schema via ``arg_types``:
.. code-block:: python
from typing import Any
from langchain_core.runnables import RunnableLambda
def f(x: dict[str, Any]) -> str:
return str(x["a"] * max(x["b"]))
runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
as_tool.invoke({"a": 3, "b": [1, 2]})
String input:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
def f(x: str) -> str:
return x + "a"
def g(x: str) -> str:
return x + "z"
runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()
as_tool.invoke("b")
.. versionadded:: 0.2.14
- assign(self, **kwargs: 'Union[Runnable[dict[str, Any], Any], Callable[[dict[str, Any]], Any], Mapping[str, Union[Runnable[dict[str, Any], Any], Callable[[dict[str, Any]], Any]]]]') -> 'RunnableSerializable[Any, Any]'
- Assigns new fields to the dict output of this Runnable.
Returns a new Runnable.
.. code-block:: python
from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter
prompt = (
SystemMessagePromptTemplate.from_template("You are a nice assistant.")
+ "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])
chain: Runnable = prompt | llm | {"str": StrOutputParser()}
chain_with_assign = chain.assign(hello=itemgetter("str") | llm)
print(chain_with_assign.input_schema.model_json_schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.model_json_schema())
# {'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}
- async astream_events(self, input: 'Any', config: 'Optional[RunnableConfig]' = None, *, version: "Literal['v1', 'v2']" = 'v2', include_names: 'Optional[Sequence[str]]' = None, include_types: 'Optional[Sequence[str]]' = None, include_tags: 'Optional[Sequence[str]]' = None, exclude_names: 'Optional[Sequence[str]]' = None, exclude_types: 'Optional[Sequence[str]]' = None, exclude_tags: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'AsyncIterator[StreamEvent]'
- Generate a stream of events.
Use to create an iterator over StreamEvents that provide real-time information
about the progress of the Runnable, including StreamEvents from intermediate
results.
A StreamEvent is a dictionary with the following schema:
- ``event``: **str** - Event names are of the
format: on_[runnable_type]_(start|stream|end).
- ``name``: **str** - The name of the Runnable that generated the event.
- ``run_id``: **str** - randomly generated ID associated with the given execution of
the Runnable that emitted the event.
A child Runnable that gets invoked as part of the execution of a
parent Runnable is assigned its own unique ID.
- ``parent_ids``: **list[str]** - The IDs of the parent runnables that
generated the event. The root Runnable will have an empty list.
The order of the parent IDs is from the root to the immediate parent.
Only available for v2 version of the API. The v1 version of the API
will return an empty list.
- ``tags``: **Optional[list[str]]** - The tags of the Runnable that generated
the event.
- ``metadata``: **Optional[dict[str, Any]]** - The metadata of the Runnable
that generated the event.
- ``data``: **dict[str, Any]**
Below is a table that illustrates some events that might be emitted by various
chains. Metadata fields have been omitted from the table for brevity.
Chain definitions have been included after the table.
**ATTENTION** This reference table is for the V2 version of the schema.
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| event | name | chunk | input | output |
+======================+==================+=================================+===============================================+=================================================+
| on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content="hello world") |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_start | [model name] | | {'input': 'hello'} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_stream | [model name] | 'Hello' | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_end | [model name] | | 'Hello human!' | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_start | format_docs | | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_stream | format_docs | "hello world!, goodbye world!" | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_end | format_docs | | [Document(...)] | "hello world!, goodbye world!" |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_start | some_tool | | {"x": 1, "y": "2"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_end | some_tool | | | {"x": 1, "y": "2"} |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_start | [retriever name] | | {"query": "hello"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_end | [retriever name] | | {"query": "hello"} | [Document(...), ..] |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_prompt_start | [template_name] | | {"question": "hello"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
In addition to the standard events, users can also dispatch custom events (see example below).
Custom events will be only be surfaced with in the `v2` version of the API!
A custom event has following format:
+-----------+------+-----------------------------------------------------------------------------------------------------------+
| Attribute | Type | Description |
+===========+======+===========================================================================================================+
| name | str | A user defined name for the event. |
+-----------+------+-----------------------------------------------------------------------------------------------------------+
| data | Any | The data associated with the event. This can be anything, though we suggest making it JSON serializable. |
+-----------+------+-----------------------------------------------------------------------------------------------------------+
Here are declarations associated with the standard events shown above:
`format_docs`:
.. code-block:: python
def format_docs(docs: list[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
`some_tool`:
.. code-block:: python
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
`prompt`:
.. code-block:: python
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
async def reverse(s: str) -> str:
return s[::-1]
chain = RunnableLambda(func=reverse)
events = [
event async for event in chain.astream_events("hello", version="v2")
]
# will produce the following events (run_id, and parent_ids
# has been omitted for brevity):
[
{
"data": {"input": "hello"},
"event": "on_chain_start",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"chunk": "olleh"},
"event": "on_chain_stream",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"output": "olleh"},
"event": "on_chain_end",
"metadata": {},
"name": "reverse",
"tags": [],
},
]
Example: Dispatch Custom Event
.. code-block:: python
from langchain_core.callbacks.manager import (
adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio
async def slow_thing(some_input: str, config: RunnableConfig) -> str:
"""Do something that takes a long time."""
await asyncio.sleep(1) # Placeholder for some slow operation
await adispatch_custom_event(
"progress_event",
{"message": "Finished step 1 of 3"},
config=config # Must be included for python < 3.10
)
await asyncio.sleep(1) # Placeholder for some slow operation
await adispatch_custom_event(
"progress_event",
{"message": "Finished step 2 of 3"},
config=config # Must be included for python < 3.10
)
await asyncio.sleep(1) # Placeholder for some slow operation
return "Done"
slow_thing = RunnableLambda(slow_thing)
async for event in slow_thing.astream_events("some_input", version="v2"):
print(event)
Args:
input: The input to the Runnable.
config: The config to use for the Runnable.
version: The version of the schema to use either `v2` or `v1`.
Users should use `v2`.
`v1` is for backwards compatibility and will be deprecated
in 0.4.0.
No default will be assigned until the API is stabilized.
custom events will only be surfaced in `v2`.
include_names: Only include events from runnables with matching names.
include_types: Only include events from runnables with matching types.
include_tags: Only include events from runnables with matching tags.
exclude_names: Exclude events from runnables with matching names.
exclude_types: Exclude events from runnables with matching types.
exclude_tags: Exclude events from runnables with matching tags.
kwargs: Additional keyword arguments to pass to the Runnable.
These will be passed to astream_log as this implementation
of astream_events is built on top of astream_log.
Yields:
An async stream of StreamEvents.
Raises:
NotImplementedError: If the version is not `v1` or `v2`.
- async astream_log(self, input: 'Any', config: 'Optional[RunnableConfig]' = None, *, diff: 'bool' = True, with_streamed_output_list: 'bool' = True, include_names: 'Optional[Sequence[str]]' = None, include_types: 'Optional[Sequence[str]]' = None, include_tags: 'Optional[Sequence[str]]' = None, exclude_names: 'Optional[Sequence[str]]' = None, exclude_types: 'Optional[Sequence[str]]' = None, exclude_tags: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]'
- Stream all output from a Runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
Jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The Jsonpatch ops can be applied in order to construct state.
Args:
input: The input to the Runnable.
config: The config to use for the Runnable.
diff: Whether to yield diffs between each step or the current state.
with_streamed_output_list: Whether to yield the streamed_output list.
include_names: Only include logs with these names.
include_types: Only include logs with these types.
include_tags: Only include logs with these tags.
exclude_names: Exclude logs with these names.
exclude_types: Exclude logs with these types.
exclude_tags: Exclude logs with these tags.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
A RunLogPatch or RunLog object.
- async atransform(self, input: 'AsyncIterator[Input]', config: 'Optional[RunnableConfig]' = None, **kwargs: 'Optional[Any]') -> 'AsyncIterator[Output]'
- Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
Args:
input: An async iterator of inputs to the Runnable.
config: The config to use for the Runnable. Defaults to None.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
The output of the Runnable.
- batch(self, inputs: 'list[Input]', config: 'Optional[Union[RunnableConfig, list[RunnableConfig]]]' = None, *, return_exceptions: 'bool' = False, **kwargs: 'Optional[Any]') -> 'list[Output]'
- Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying Runnable uses an API which supports a batch mode.
- batch_as_completed(self, inputs: 'Sequence[Input]', config: 'Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]' = None, *, return_exceptions: 'bool' = False, **kwargs: 'Optional[Any]') -> 'Iterator[tuple[int, Union[Output, Exception]]]'
- Run invoke in parallel on a list of inputs.
Yields results as they complete.
- bind(self, **kwargs: 'Any') -> 'Runnable[Input, Output]'
- Bind arguments to a Runnable, returning a new Runnable.
Useful when a Runnable in a chain requires an argument that is not
in the output of the previous Runnable or included in the user input.
Args:
kwargs: The arguments to bind to the Runnable.
Returns:
A new Runnable with the arguments bound.
Example:
.. code-block:: python
from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser
llm = ChatOllama(model='llama2')
# Without bind.
chain = (
llm
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'
# With bind.
chain = (
llm.bind(stop=["three"])
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'
- config_schema(self, *, include: 'Optional[Sequence[str]]' = None) -> 'type[BaseModel]'
- The type of config this Runnable accepts specified as a pydantic model.
To mark a field as configurable, see the `configurable_fields`
and `configurable_alternatives` methods.
Args:
include: A list of fields to include in the config schema.
Returns:
A pydantic model that can be used to validate config.
- get_config_jsonschema(self, *, include: 'Optional[Sequence[str]]' = None) -> 'dict[str, Any]'
- Get a JSON schema that represents the config of the Runnable.
Args:
include: A list of fields to include in the config schema.
Returns:
A JSON schema that represents the config of the Runnable.
.. versionadded:: 0.3.0
- get_graph(self, config: 'Optional[RunnableConfig]' = None) -> 'Graph'
- Return a graph representation of this Runnable.
- get_input_jsonschema(self, config: 'Optional[RunnableConfig]' = None) -> 'dict[str, Any]'
- Get a JSON schema that represents the input to the Runnable.
Args:
config: A config to use when generating the schema.
Returns:
A JSON schema that represents the input to the Runnable.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
def add_one(x: int) -> int:
return x + 1
runnable = RunnableLambda(add_one)
print(runnable.get_input_jsonschema())
.. versionadded:: 0.3.0
- get_input_schema(self, config: 'Optional[RunnableConfig]' = None) -> 'type[BaseModel]'
- Get a pydantic model that can be used to validate input to the Runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the Runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Args:
config: A config to use when generating the schema.
Returns:
A pydantic model that can be used to validate input.
- get_name(self, suffix: 'Optional[str]' = None, *, name: 'Optional[str]' = None) -> 'str'
- Get the name of the Runnable.
- get_output_jsonschema(self, config: 'Optional[RunnableConfig]' = None) -> 'dict[str, Any]'
- Get a JSON schema that represents the output of the Runnable.
Args:
config: A config to use when generating the schema.
Returns:
A JSON schema that represents the output of the Runnable.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
def add_one(x: int) -> int:
return x + 1
runnable = RunnableLambda(add_one)
print(runnable.get_output_jsonschema())
.. versionadded:: 0.3.0
- get_output_schema(self, config: 'Optional[RunnableConfig]' = None) -> 'type[BaseModel]'
- Get a pydantic model that can be used to validate output to the Runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the Runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Args:
config: A config to use when generating the schema.
Returns:
A pydantic model that can be used to validate output.
- get_prompts(self, config: 'Optional[RunnableConfig]' = None) -> 'list[BasePromptTemplate]'
- Return a list of prompts used by this Runnable.
- map(self) -> 'Runnable[list[Input], list[Output]]'
- Return a new Runnable that maps a list of inputs to a list of outputs.
Calls invoke() with each input.
Returns:
A new Runnable that maps a list of inputs to a list of outputs.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
def _lambda(x: int) -> int:
return x + 1
runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]
- pick(self, keys: 'Union[str, list[str]]') -> 'RunnableSerializable[Any, Any]'
- Pick keys from the output dict of this Runnable.
Pick single key:
.. code-block:: python
import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}
json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]
Pick list of keys:
.. code-block:: python
from typing import Any
import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
def as_bytes(x: Any) -> bytes:
return bytes(x, "utf-8")
chain = RunnableMap(
str=as_str,
json=as_json,
bytes=RunnableLambda(as_bytes)
)
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
- pipe(self, *others: 'Union[Runnable[Any, Other], Callable[[Any], Other]]', name: 'Optional[str]' = None) -> 'RunnableSerializable[Input, Other]'
- Compose this Runnable with Runnable-like objects to make a RunnableSequence.
Equivalent to `RunnableSequence(self, *others)` or `self | others[0] | ...`
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
def add_one(x: int) -> int:
return x + 1
def mul_two(x: int) -> int:
return x * 2
runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4
sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]
- transform(self, input: 'Iterator[Input]', config: 'Optional[RunnableConfig]' = None, **kwargs: 'Optional[Any]') -> 'Iterator[Output]'
- Default implementation of transform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
Args:
input: An iterator of inputs to the Runnable.
config: The config to use for the Runnable. Defaults to None.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
The output of the Runnable.
- with_alisteners(self, *, on_start: 'Optional[AsyncListener]' = None, on_end: 'Optional[AsyncListener]' = None, on_error: 'Optional[AsyncListener]' = None) -> 'Runnable[Input, Output]'
- Bind async lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Asynchronously called before the Runnable starts running.
on_end: Asynchronously called after the Runnable finishes running.
on_error: Asynchronously called if the Runnable throws an error.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
Args:
on_start: Asynchronously called before the Runnable starts running.
Defaults to None.
on_end: Asynchronously called after the Runnable finishes running.
Defaults to None.
on_error: Asynchronously called if the Runnable throws an error.
Defaults to None.
Returns:
A new Runnable with the listeners bound.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio
def format_t(timestamp: float) -> str:
return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()
async def test_runnable(time_to_sleep : int):
print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
await asyncio.sleep(time_to_sleep)
print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")
async def fn_start(run_obj : Runnable):
print(f"on start callback starts at {format_t(time.time())}")
await asyncio.sleep(3)
print(f"on start callback ends at {format_t(time.time())}")
async def fn_end(run_obj : Runnable):
print(f"on end callback starts at {format_t(time.time())}")
await asyncio.sleep(2)
print(f"on end callback ends at {format_t(time.time())}")
runnable = RunnableLambda(test_runnable).with_alisteners(
on_start=fn_start,
on_end=fn_end
)
async def concurrent_runs():
await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))
asyncio.run(concurrent_runs())
Result:
on start callback starts at 2025-03-01T07:05:22.875378+00:00
on start callback starts at 2025-03-01T07:05:22.875495+00:00
on start callback ends at 2025-03-01T07:05:25.878862+00:00
on start callback ends at 2025-03-01T07:05:25.878947+00:00
Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
on end callback starts at 2025-03-01T07:05:27.882360+00:00
Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
on end callback starts at 2025-03-01T07:05:28.882428+00:00
on end callback ends at 2025-03-01T07:05:29.883893+00:00
on end callback ends at 2025-03-01T07:05:30.884831+00:00
- with_config(self, config: 'Optional[RunnableConfig]' = None, **kwargs: 'Any') -> 'Runnable[Input, Output]'
- Bind config to a Runnable, returning a new Runnable.
Args:
config: The config to bind to the Runnable.
kwargs: Additional keyword arguments to pass to the Runnable.
Returns:
A new Runnable with the config bound.
- with_fallbacks(self, fallbacks: 'Sequence[Runnable[Input, Output]]', *, exceptions_to_handle: 'tuple[type[BaseException], ...]' = (<class 'Exception'>,), exception_key: 'Optional[str]' = None) -> 'RunnableWithFallbacksT[Input, Output]'
- Add fallbacks to a Runnable, returning a new Runnable.
The new Runnable will try the original Runnable, and then each fallback
in order, upon failures.
Args:
fallbacks: A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle: A tuple of exception types to handle.
Defaults to (Exception,).
exception_key: If string is specified then handled exceptions will be passed
to fallbacks as part of the input under the specified key. If None,
exceptions will not be passed to fallbacks. If used, the base Runnable
and its fallbacks must accept a dictionary as input. Defaults to None.
Returns:
A new Runnable that will try the original Runnable, and then each
fallback in order, upon failures.
Example:
.. code-block:: python
from typing import Iterator
from langchain_core.runnables import RunnableGenerator
def _generate_immediate_error(input: Iterator) -> Iterator[str]:
raise ValueError()
yield ""
def _generate(input: Iterator) -> Iterator[str]:
yield from "foo bar"
runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
[RunnableGenerator(_generate)]
)
print(''.join(runnable.stream({}))) #foo bar
Args:
fallbacks: A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle: A tuple of exception types to handle.
exception_key: If string is specified then handled exceptions will be passed
to fallbacks as part of the input under the specified key. If None,
exceptions will not be passed to fallbacks. If used, the base Runnable
and its fallbacks must accept a dictionary as input.
Returns:
A new Runnable that will try the original Runnable, and then each
fallback in order, upon failures.
- with_listeners(self, *, on_start: 'Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]' = None, on_end: 'Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]' = None, on_error: 'Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]' = None) -> 'Runnable[Input, Output]'
- Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the Runnable starts running, with the Run object.
on_end: Called after the Runnable finishes running, with the Run object.
on_error: Called if the Runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
Args:
on_start: Called before the Runnable starts running. Defaults to None.
on_end: Called after the Runnable finishes running. Defaults to None.
on_error: Called if the Runnable throws an error. Defaults to None.
Returns:
A new Runnable with the listeners bound.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run
import time
def test_runnable(time_to_sleep : int):
time.sleep(time_to_sleep)
def fn_start(run_obj: Run):
print("start_time:", run_obj.start_time)
def fn_end(run_obj: Run):
print("end_time:", run_obj.end_time)
chain = RunnableLambda(test_runnable).with_listeners(
on_start=fn_start,
on_end=fn_end
)
chain.invoke(2)
- with_retry(self, *, retry_if_exception_type: 'tuple[type[BaseException], ...]' = (<class 'Exception'>,), wait_exponential_jitter: 'bool' = True, exponential_jitter_params: 'Optional[ExponentialJitterParams]' = None, stop_after_attempt: 'int' = 3) -> 'Runnable[Input, Output]'
- Create a new Runnable that retries the original Runnable on exceptions.
Args:
retry_if_exception_type: A tuple of exception types to retry on.
Defaults to (Exception,).
wait_exponential_jitter: Whether to add jitter to the wait
time between retries. Defaults to True.
stop_after_attempt: The maximum number of attempts to make before
giving up. Defaults to 3.
exponential_jitter_params: Parameters for
``tenacity.wait_exponential_jitter``. Namely: ``initial``, ``max``,
``exp_base``, and ``jitter`` (all float values).
Returns:
A new Runnable that retries the original Runnable on exceptions.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
count = 0
def _lambda(x: int) -> None:
global count
count = count + 1
if x == 1:
raise ValueError("x is 1")
else:
pass
runnable = RunnableLambda(_lambda)
try:
runnable.with_retry(
stop_after_attempt=2,
retry_if_exception_type=(ValueError,),
).invoke(1)
except ValueError:
pass
assert (count == 2)
- with_types(self, *, input_type: 'Optional[type[Input]]' = None, output_type: 'Optional[type[Output]]' = None) -> 'Runnable[Input, Output]'
- Bind input and output types to a Runnable, returning a new Runnable.
Args:
input_type: The input type to bind to the Runnable. Defaults to None.
output_type: The output type to bind to the Runnable. Defaults to None.
Returns:
A new Runnable with the types bound.
Readonly properties inherited from langchain_core.runnables.base.Runnable:
- config_specs
- List configurable fields for this Runnable.
- input_schema
- The type of input this Runnable accepts specified as a pydantic model.
- output_schema
- The type of output this Runnable produces specified as a pydantic model.
Class methods inherited from typing.Generic:
- __init_subclass__(*args, **kwargs) from pydantic._internal._model_construction.ModelMetaclass
- This method is called when a class is subclassed.
The default implementation does nothing. It may be
overridden to extend subclasses.
|
class ChatBedrockConverse(AICoreBedrockBaseModel, langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse) |
|
ChatBedrockConverse(*args, name: Optional[str] = None, cache: Union[langchain_core.caches.BaseCache, bool, NoneType] = None, verbose: bool = <factory>, callbacks: Union[list[langchain_core.callbacks.base.BaseCallbackHandler], langchain_core.callbacks.base.BaseCallbackManager, NoneType] = None, tags: Optional[list[str]] = None, metadata: Optional[dict[str, Any]] = None, custom_get_token_ids: Optional[Callable[[str], list[int]]] = None, callback_manager: Optional[langchain_core.callbacks.base.BaseCallbackManager] = None, rate_limiter: Optional[langchain_core.rate_limiters.BaseRateLimiter] = None, disable_streaming: Union[bool, Literal['tool_calling']] = False, client: Any = None, model: str, max_tokens: Optional[int] = None, stop: Optional[List[str]] = None, temperature: Optional[float] = None, top_p: Optional[float] = None, region_name: Optional[str] = None, credentials_profile_name: Optional[str] = None, aws_access_key_id: Optional[pydantic.types.SecretStr] = <factory>, aws_secret_access_key: Optional[pydantic.types.SecretStr] = <factory>, aws_session_token: Optional[pydantic.types.SecretStr] = <factory>, provider: str = '', base_url: Optional[str] = None, config: Any = None, guardrails: Optional[Dict[str, Any]] = None, additional_model_request_fields: Optional[Dict[str, Any]] = None, additional_model_response_field_paths: Optional[List[str]] = None, supports_tool_choice_values: Optional[Sequence[Literal['auto', 'any', 'tool']]] = None, performance_config: Optional[Mapping[str, Any]] = None, request_metadata: Optional[Dict[str, str]] = None, **kwargs) -> None
Drop-in replacement for LangChain ChatBedrockConverse. |
|
- Method resolution order:
- ChatBedrockConverse
- AICoreBedrockBaseModel
- langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse
- langchain_core.language_models.chat_models.BaseChatModel
- langchain_core.language_models.base.BaseLanguageModel[BaseMessage]
- langchain_core.language_models.base.BaseLanguageModel
- langchain_core.runnables.base.RunnableSerializable[Union[PromptValue, str, Sequence[Union[BaseMessage, list[str], tuple[str, str], str, dict[str, Any]]]], ~LanguageModelOutputVar]
- langchain_core.runnables.base.RunnableSerializable
- langchain_core.load.serializable.Serializable
- pydantic.main.BaseModel
- langchain_core.runnables.base.Runnable
- typing.Generic
- abc.ABC
- builtins.object
Methods defined here:
- __init__(self, *args, **kwargs)
- Extends the constructor of the base class with aicore specific parameters.
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __class_vars__ = set()
- __parameters__ = ()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.proxy.langchain.amazon.ChatBedrockConverse'>, 'config': {'extra_fields_behavior': 'allow', 'title': 'ChatBedrockConverse', 'validate_by_alias': True, 'validate_by_name': True}, 'custom_init': True, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...hub.proxy.langchain.amazon.ChatBedrockConverse'>>]}, 'ref': 'gen_ai_hub.proxy.langchain.amazon.ChatBedrockConverse:139976652162784', 'root_model': False, 'schema': {'function': {'function': <bound method AICoreBedrockBaseModel.validate_en...hub.proxy.langchain.amazon.ChatBedrockConverse'>>, 'type': 'no-info'}, 'schema': {'function': {'function': <bound method ChatBedrockConverse.set_disable_st...hub.proxy.langchain.amazon.ChatBedrockConverse'>>, 'type': 'no-info'}, 'schema': {'function': {'function': <bound method BaseChatModel.raise_deprecation of...hub.proxy.langchain.amazon.ChatBedrockConverse'>>, 'type': 'no-info'}, 'schema': {'computed_fields': [], 'fields': {'additional_model_request_fields': {...}, 'additional_model_response_field_paths': {...}, 'aws_access_key_id': {...}, 'aws_secret_access_key': {...}, 'aws_session_token': {...}, 'cache': {...}, 'callback_manager': {...}, 'callbacks': {...}, 'client': {...}, 'config': {...}, ...}, 'model_name': 'ChatBedrockConverse', 'type': 'model-fields'}, 'type': 'function-before'}, 'type': 'function-before'}, 'type': 'function-before'}, 'type': 'model'}
- __pydantic_custom_init__ = True
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...coratorInfo(mode='before'))}, computed_fields={})
- __pydantic_fields__ = {'additional_model_request_fields': FieldInfo(annotation=Union[Dict[str, Any], NoneType], required=False, default=None), 'additional_model_response_field_paths': FieldInfo(annotation=Union[List[str], NoneType], required=False, default=None), 'aws_access_key_id': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'aws_secret_access_key': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'aws_session_token': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'cache': FieldInfo(annotation=Union[BaseCache, bool, NoneType], required=False, default=None, exclude=True), 'callback_manager': FieldInfo(annotation=Union[BaseCallbackManager, ... manager to add to the run trace.', exclude=True), 'callbacks': FieldInfo(annotation=Union[list[BaseCallbackHand...ype], required=False, default=None, exclude=True), 'client': FieldInfo(annotation=Any, required=False, default=None, exclude=True), 'config': FieldInfo(annotation=Any, required=False, default=None), ...}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri... "ChatBedrockConverse",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="ChatBedrockConverse", val...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*args, name: Optional[str] = None, c...tional[Dict[str, str]] = None, **kwargs) -> None>
- model_config = {'arbitrary_types_allowed': True, 'extra': 'allow', 'populate_by_name': True, 'protected_namespaces': (), 'validate_by_alias': True, 'validate_by_name': True}
Class methods inherited from AICoreBedrockBaseModel:
- get_corresponding_model_id(model_name) from pydantic._internal._model_construction.ModelMetaclass
- validate_environment(values: Dict) -> Dict from pydantic._internal._model_construction.ModelMetaclass
- Validate that AWS credentials to and python package exists in environment.
Data descriptors inherited from AICoreBedrockBaseModel:
- __weakref__
- list of weak references to the object (if defined)
Methods inherited from langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse:
- bind_tools(self, tools: Sequence[Union[Dict[str, Any], type[pydantic.main.BaseModel], Callable, langchain_core.tools.base.BaseTool]], *, tool_choice: Union[dict, str, Literal['auto', 'any'], NoneType] = None, **kwargs: Any) -> langchain_core.runnables.base.Runnable[typing.Union[langchain_core.prompt_values.PromptValue, str, collections.abc.Sequence[typing.Union[langchain_core.messages.base.BaseMessage, list[str], tuple[str, str], str, dict[str, typing.Any]]]], langchain_core.messages.base.BaseMessage]
- Bind tools to the model.
Args:
tools: Sequence of tools to bind to the model.
tool_choice: The tool to use. If "any" then any tool can be used.
Returns:
A Runnable that returns a message.
- with_structured_output(self, schema: Union[Dict[str, Any], Type[~_BM], Type], *, include_raw: bool = False, **kwargs: Any) -> langchain_core.runnables.base.Runnable[typing.Union[langchain_core.prompt_values.PromptValue, str, collections.abc.Sequence[typing.Union[langchain_core.messages.base.BaseMessage, list[str], tuple[str, str], str, dict[str, typing.Any]]]], typing.Union[typing.Dict, pydantic.main.BaseModel]]
- Model wrapper that returns outputs formatted to match the given schema.
Args:
schema:
The output schema. Can be passed in as:
- an OpenAI function/tool schema,
- a JSON Schema,
- a TypedDict class,
- or a Pydantic class.
If ``schema`` is a Pydantic class then the model output will be a
Pydantic instance of that class, and the model-generated fields will be
validated by the Pydantic class. Otherwise the model output will be a
dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
for more on how to properly specify types and descriptions of
schema fields when specifying a Pydantic or TypedDict class.
include_raw:
If False then only the parsed structured output is returned. If
an error occurs during model output parsing it will be raised. If True
then both the raw model response (a BaseMessage) and the parsed model
response will be returned. If an error occurs during output parsing it
will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error".
Returns:
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
an instance of ``schema`` (i.e., a Pydantic object).
Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
If ``include_raw`` is True, then Runnable outputs a dict with keys:
- ``"raw"``: BaseMessage
- ``"parsed"``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
- ``"parsing_error"``: Optional[BaseException]
Example: Pydantic schema (include_raw=False):
.. code-block:: python
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatModel(model="model-name", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> AnswerWithJustification(
# answer='They weigh the same',
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
Example: Pydantic schema (include_raw=True):
.. code-block:: python
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatModel(model="model-name", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
# 'parsing_error': None
# }
Example: Dict schema (include_raw=False):
.. code-block:: python
from pydantic import BaseModel
from langchain_core.utils.function_calling import convert_to_openai_tool
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
dict_schema = convert_to_openai_tool(AnswerWithJustification)
llm = ChatModel(model="model-name", temperature=0)
structured_llm = llm.with_structured_output(dict_schema)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
.. versionchanged:: 0.2.26
Added support for TypedDict class.
Class methods inherited from langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse:
- get_lc_namespace() -> list[str] from pydantic._internal._model_construction.ModelMetaclass
- Get the namespace of the langchain object.
For example, if the class is `langchain.llms.openai.OpenAI`, then the
namespace is ["langchain", "llms", "openai"]
- is_lc_serializable() -> bool from pydantic._internal._model_construction.ModelMetaclass
- Is this class serializable?
By design, even if a class inherits from Serializable, it is not serializable by
default. This is to prevent accidental serialization of objects that should not
be serialized.
Returns:
Whether the class is serializable. Default is False.
- set_disable_streaming(values: Dict) -> Any from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse:
- lc_secrets
- A map of constructor argument names to secret ids.
For example,
{"openai_api_key": "OPENAI_API_KEY"}
Data and other attributes inherited from langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse:
- __annotations__ = {'additional_model_request_fields': typing.Optional[typing.Dict[str, typing.Any]], 'additional_model_response_field_paths': typing.Optional[typing.List[str]], 'aws_access_key_id': typing.Optional[pydantic.types.SecretStr], 'aws_secret_access_key': typing.Optional[pydantic.types.SecretStr], 'aws_session_token': typing.Optional[pydantic.types.SecretStr], 'client': typing.Any, 'config': typing.Any, 'credentials_profile_name': typing.Optional[str], 'endpoint_url': typing.Optional[str], 'guardrail_config': typing.Optional[typing.Dict[str, typing.Any]], ...}
Methods inherited from langchain_core.language_models.chat_models.BaseChatModel:
- __call__(self, messages: 'list[BaseMessage]', stop: 'Optional[list[str]]' = None, callbacks: 'Callbacks' = None, **kwargs: 'Any') -> 'BaseMessage'
- .. deprecated:: 0.1.7 Use :meth:`~invoke` instead. It will not be removed until langchain-core==1.0.
Call the model.
Args:
messages: List of messages.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
The model output message.
- async agenerate(self, messages: 'list[list[BaseMessage]]', stop: 'Optional[list[str]]' = None, callbacks: 'Callbacks' = None, *, tags: 'Optional[list[str]]' = None, metadata: 'Optional[dict[str, Any]]' = None, run_name: 'Optional[str]' = None, run_id: 'Optional[uuid.UUID]' = None, **kwargs: 'Any') -> 'LLMResult'
- Asynchronously pass a sequence of prompts to a model and return generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
messages: List of list of messages.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
tags: The tags to apply.
metadata: The metadata to apply.
run_name: The name of the run.
run_id: The ID of the run.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- async agenerate_prompt(self, prompts: 'list[PromptValue]', stop: 'Optional[list[str]]' = None, callbacks: 'Callbacks' = None, **kwargs: 'Any') -> 'LLMResult'
- Asynchronously pass a sequence of prompts and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
prompts: List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- async ainvoke(self, input: 'LanguageModelInput', config: 'Optional[RunnableConfig]' = None, *, stop: 'Optional[list[str]]' = None, **kwargs: 'Any') -> 'BaseMessage'
- Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the Runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
- async apredict(self, text: 'str', *, stop: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'str'
- .. deprecated:: 0.1.7 Use :meth:`~ainvoke` instead. It will not be removed until langchain-core==1.0.
- async apredict_messages(self, messages: 'list[BaseMessage]', *, stop: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'BaseMessage'
- .. deprecated:: 0.1.7 Use :meth:`~ainvoke` instead. It will not be removed until langchain-core==1.0.
- async astream(self, input: 'LanguageModelInput', config: 'Optional[RunnableConfig]' = None, *, stop: 'Optional[list[str]]' = None, **kwargs: 'Any') -> 'AsyncIterator[BaseMessageChunk]'
- Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
Args:
input: The input to the Runnable.
config: The config to use for the Runnable. Defaults to None.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
The output of the Runnable.
- call_as_llm(self, message: 'str', stop: 'Optional[list[str]]' = None, **kwargs: 'Any') -> 'str'
- .. deprecated:: 0.1.7 Use :meth:`~invoke` instead. It will not be removed until langchain-core==1.0.
Call the model.
Args:
message: The input message.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
The model output string.
- dict(self, **kwargs: 'Any') -> 'dict'
- Return a dictionary of the LLM.
- generate(self, messages: 'list[list[BaseMessage]]', stop: 'Optional[list[str]]' = None, callbacks: 'Callbacks' = None, *, tags: 'Optional[list[str]]' = None, metadata: 'Optional[dict[str, Any]]' = None, run_name: 'Optional[str]' = None, run_id: 'Optional[uuid.UUID]' = None, **kwargs: 'Any') -> 'LLMResult'
- Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
messages: List of list of messages.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
tags: The tags to apply.
metadata: The metadata to apply.
run_name: The name of the run.
run_id: The ID of the run.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- generate_prompt(self, prompts: 'list[PromptValue]', stop: 'Optional[list[str]]' = None, callbacks: 'Callbacks' = None, **kwargs: 'Any') -> 'LLMResult'
- Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
1. take advantage of batched calls,
2. need more output from the model than just the top generated value,
3. are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
Args:
prompts: List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks: Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- invoke(self, input: 'LanguageModelInput', config: 'Optional[RunnableConfig]' = None, *, stop: 'Optional[list[str]]' = None, **kwargs: 'Any') -> 'BaseMessage'
- Transform a single input into an output.
Args:
input: The input to the Runnable.
config: A config to use when invoking the Runnable.
The config supports standard keys like 'tags', 'metadata' for tracing
purposes, 'max_concurrency' for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns:
The output of the Runnable.
- predict(self, text: 'str', *, stop: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'str'
- .. deprecated:: 0.1.7 Use :meth:`~invoke` instead. It will not be removed until langchain-core==1.0.
Predict the next message.
Args:
text: The input message.
stop: Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs: Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns:
The predicted output string.
- predict_messages(self, messages: 'list[BaseMessage]', *, stop: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'BaseMessage'
- .. deprecated:: 0.1.7 Use :meth:`~invoke` instead. It will not be removed until langchain-core==1.0.
- stream(self, input: 'LanguageModelInput', config: 'Optional[RunnableConfig]' = None, *, stop: 'Optional[list[str]]' = None, **kwargs: 'Any') -> 'Iterator[BaseMessageChunk]'
- Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
Args:
input: The input to the Runnable.
config: The config to use for the Runnable. Defaults to None.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
The output of the Runnable.
Class methods inherited from langchain_core.language_models.chat_models.BaseChatModel:
- raise_deprecation(values: 'dict') -> 'Any' from pydantic._internal._model_construction.ModelMetaclass
- Raise deprecation warning if callback_manager is used.
Args:
values (Dict): Values to validate.
Returns:
Dict: Validated values.
Raises:
DeprecationWarning: If callback_manager is used.
Readonly properties inherited from langchain_core.language_models.chat_models.BaseChatModel:
- OutputType
- Get the output type for this runnable.
Methods inherited from langchain_core.language_models.base.BaseLanguageModel:
- get_num_tokens(self, text: 'str') -> 'int'
- Get the number of tokens present in the text.
Useful for checking if an input fits in a model's context window.
Args:
text: The string input to tokenize.
Returns:
The integer number of tokens in the text.
- get_num_tokens_from_messages(self, messages: 'list[BaseMessage]', tools: 'Optional[Sequence]' = None) -> 'int'
- Get the number of tokens in the messages.
Useful for checking if an input fits in a model's context window.
**Note**: the base implementation of get_num_tokens_from_messages ignores
tool schemas.
Args:
messages: The message inputs to tokenize.
tools: If provided, sequence of dict, BaseModel, function, or BaseTools
to be converted to tool schemas.
Returns:
The sum of the number of tokens across the messages.
- get_token_ids(self, text: 'str') -> 'list[int]'
- Return the ordered ids of the tokens in a text.
Args:
text: The string input to tokenize.
Returns:
A list of ids corresponding to the tokens in the text, in order they occur
in the text.
Class methods inherited from langchain_core.language_models.base.BaseLanguageModel:
- set_verbose(verbose: 'Optional[bool]') -> 'bool' from pydantic._internal._model_construction.ModelMetaclass
- If verbose is None, set it.
This allows users to pass in None as verbose to access the global setting.
Args:
verbose: The verbosity setting to use.
Returns:
The verbosity setting to use.
Readonly properties inherited from langchain_core.language_models.base.BaseLanguageModel:
- InputType
- Get the input type for this runnable.
Methods inherited from langchain_core.runnables.base.RunnableSerializable:
- configurable_alternatives(self, which: 'ConfigurableField', *, default_key: 'str' = 'default', prefix_keys: 'bool' = False, **kwargs: 'Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]') -> 'RunnableSerializable[Input, Output]'
- Configure alternatives for Runnables that can be set at runtime.
Args:
which: The ConfigurableField instance that will be used to select the
alternative.
default_key: The default key to use if no alternative is selected.
Defaults to "default".
prefix_keys: Whether to prefix the keys with the ConfigurableField id.
Defaults to False.
**kwargs: A dictionary of keys to Runnable instances or callables that
return Runnable instances.
Returns:
A new Runnable with the alternatives configured.
.. code-block:: python
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatAnthropic(
model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
ConfigurableField(id="llm"),
default_key="anthropic",
openai=ChatOpenAI()
)
# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)
# uses ChatOpenAI
print(
model.with_config(
configurable={"llm": "openai"}
).invoke("which organization created you?").content
)
- configurable_fields(self, **kwargs: 'AnyConfigurableField') -> 'RunnableSerializable[Input, Output]'
- Configure particular Runnable fields at runtime.
Args:
**kwargs: A dictionary of ConfigurableField instances to configure.
Returns:
A new Runnable with the fields configured.
.. code-block:: python
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(max_tokens=20).configurable_fields(
max_tokens=ConfigurableField(
id="output_token_number",
name="Max tokens in the output",
description="The maximum number of tokens in the output",
)
)
# max_tokens = 20
print(
"max_tokens_20: ",
model.invoke("tell me something about chess").content
)
# max_tokens = 200
print("max_tokens_200: ", model.with_config(
configurable={"output_token_number": 200}
).invoke("tell me something about chess").content
)
- to_json(self) -> 'Union[SerializedConstructor, SerializedNotImplemented]'
- Serialize the Runnable to JSON.
Returns:
A JSON-serializable representation of the Runnable.
Data and other attributes inherited from langchain_core.runnables.base.RunnableSerializable:
- __orig_bases__ = (<class 'langchain_core.load.serializable.Serializable'>, langchain_core.runnables.base.Runnable[-Input, +Output])
Methods inherited from langchain_core.load.serializable.Serializable:
- __repr_args__(self) -> Any
- to_json_not_implemented(self) -> langchain_core.load.serializable.SerializedNotImplemented
- Serialize a "not implemented" object.
Class methods inherited from langchain_core.load.serializable.Serializable:
- lc_id() -> list[str] from pydantic._internal._model_construction.ModelMetaclass
- A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
For example, for the class `langchain.llms.openai.OpenAI`, the id is
["langchain", "llms", "openai", "OpenAI"].
Readonly properties inherited from langchain_core.load.serializable.Serializable:
- lc_attributes
- List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
Default is an empty dictionary.
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'typing.Callable[[Any], Any]', **kwargs: 'Any') -> 'typing.Generator[Any, None, None]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/serialization.md#model_copy)
Returns a copy of the model.
!!! note
The underlying instance's [`__dict__`][object.__dict__] attribute is copied. This
might have unexpected side effects if you store anything in it, on top of the model
fields (e.g. the value of [cached properties][functools.cached_property]).
Args:
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.
- model_dump(self, *, mode: "Literal['json', 'python'] | str" = 'python', include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
fallback: A function to call when an unknown value is encountered. If not provided,
a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A dictionary representation of the model.
- model_dump_json(self, *, indent: 'int | None' = None, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, round_trip: 'bool' = False, warnings: "bool | Literal['none', 'warn', 'error']" = True, fallback: 'Callable[[Any], Any] | None' = None, serialize_as_any: 'bool' = False) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
fallback: A function to call when an unknown value is encountered. If not provided,
a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
only after the class is actually fully initialized. In particular, attributes like `model_fields` will
be present when this is called.
This is necessary because `__init_subclass__` will always be called by `type.__new__`,
and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
`type.__new__` was called in such a manner that the class would already be sufficiently initialized.
This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
any kwargs passed to the class definition that aren't used internally by pydantic.
Args:
**kwargs: Any keyword arguments passed to the class definition that aren't used internally
by pydantic.
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
schema_generator: To override the logic used to generate the JSON schema, as a subclass of
`GenerateJsonSchema` with your desired modifications
mode: The mode in which to generate the schema.
Returns:
The JSON schema for the given model class.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Validate a pydantic model instance.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
from_attributes: Whether to extract data from object attributes.
context: Additional context to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Raises:
ValidationError: If the object could not be validated.
Returns:
The validated model instance.
- model_validate_json(json_data: 'str | bytes | bytearray', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- !!! abstract "Usage Documentation"
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Returns:
The validated Pydantic model.
Raises:
ValidationError: If `json_data` is not a JSON string or the object could not be validated.
- model_validate_strings(obj: 'Any', *, strict: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Validate the given object with string data against the Pydantic model.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
by_alias: Whether to use the field's alias when validating against the provided input data.
by_name: Whether to use the field's name when validating against the provided input data.
Returns:
The validated Pydantic model.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'additional_model_request_fields': FieldInfo(annotation=Union[Dict[str, Any], NoneType], required=False, default=None), 'additional_model_response_field_paths': FieldInfo(annotation=Union[List[str], NoneType], required=False, default=None), 'aws_access_key_id': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'aws_secret_access_key': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'aws_session_token': FieldInfo(annotation=Union[SecretStr, NoneType],...uired=False, default_factory=get_secret_from_env), 'cache': FieldInfo(annotation=Union[BaseCache, bool, NoneType], required=False, default=None, exclude=True), 'callback_manager': FieldInfo(annotation=Union[BaseCallbackManager, ... manager to add to the run trace.', exclude=True), 'callbacks': FieldInfo(annotation=Union[list[BaseCallbackHand...ype], required=False, default=None, exclude=True), 'client': FieldInfo(annotation=Any, required=False, default=None, exclude=True), 'config': FieldInfo(annotation=Any, required=False, default=None), ...}
Methods inherited from langchain_core.runnables.base.Runnable:
- __or__(self, other: 'Union[Runnable[Any, Other], Callable[[Iterator[Any]], Iterator[Other]], Callable[[AsyncIterator[Any]], AsyncIterator[Other]], Callable[[Any], Other], Mapping[str, Union[Runnable[Any, Other], Callable[[Any], Other], Any]]]') -> 'RunnableSerializable[Input, Other]'
- Compose this Runnable with another object to create a RunnableSequence.
- __ror__(self, other: 'Union[Runnable[Other, Any], Callable[[Iterator[Other]], Iterator[Any]], Callable[[AsyncIterator[Other]], AsyncIterator[Any]], Callable[[Other], Any], Mapping[str, Union[Runnable[Other, Any], Callable[[Other], Any], Any]]]') -> 'RunnableSerializable[Other, Output]'
- Compose this Runnable with another object to create a RunnableSequence.
- async abatch(self, inputs: 'list[Input]', config: 'Optional[Union[RunnableConfig, list[RunnableConfig]]]' = None, *, return_exceptions: 'bool' = False, **kwargs: 'Optional[Any]') -> 'list[Output]'
- Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying Runnable uses an API which supports a batch mode.
Args:
inputs: A list of inputs to the Runnable.
config: A config to use when invoking the Runnable.
The config supports standard keys like 'tags', 'metadata' for tracing
purposes, 'max_concurrency' for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details. Defaults to None.
return_exceptions: Whether to return exceptions instead of raising them.
Defaults to False.
kwargs: Additional keyword arguments to pass to the Runnable.
Returns:
A list of outputs from the Runnable.
- async abatch_as_completed(self, inputs: 'Sequence[Input]', config: 'Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]' = None, *, return_exceptions: 'bool' = False, **kwargs: 'Optional[Any]') -> 'AsyncIterator[tuple[int, Union[Output, Exception]]]'
- Run ainvoke in parallel on a list of inputs.
Yields results as they complete.
Args:
inputs: A list of inputs to the Runnable.
config: A config to use when invoking the Runnable.
The config supports standard keys like 'tags', 'metadata' for tracing
purposes, 'max_concurrency' for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details. Defaults to None. Defaults to None.
return_exceptions: Whether to return exceptions instead of raising them.
Defaults to False.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
A tuple of the index of the input and the output from the Runnable.
- as_tool(self, args_schema: 'Optional[type[BaseModel]]' = None, *, name: 'Optional[str]' = None, description: 'Optional[str]' = None, arg_types: 'Optional[dict[str, type]]' = None) -> 'BaseTool'
- .. beta::
This API is in beta and may change in the future.
Create a BaseTool from a Runnable.
``as_tool`` will instantiate a BaseTool with a name, description, and
``args_schema`` from a Runnable. Where possible, schemas are inferred
from ``runnable.get_input_schema``. Alternatively (e.g., if the
Runnable takes a dict as input and the specific dict keys are not typed),
the schema can be specified directly with ``args_schema``. You can also
pass ``arg_types`` to just specify the required arguments and their types.
Args:
args_schema: The schema for the tool. Defaults to None.
name: The name of the tool. Defaults to None.
description: The description of the tool. Defaults to None.
arg_types: A dictionary of argument names to types. Defaults to None.
Returns:
A BaseTool instance.
Typed dict input:
.. code-block:: python
from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda
class Args(TypedDict):
a: int
b: list[int]
def f(x: Args) -> str:
return str(x["a"] * max(x["b"]))
runnable = RunnableLambda(f)
as_tool = runnable.as_tool()
as_tool.invoke({"a": 3, "b": [1, 2]})
``dict`` input, specifying schema via ``args_schema``:
.. code-block:: python
from typing import Any
from pydantic import BaseModel, Field
from langchain_core.runnables import RunnableLambda
def f(x: dict[str, Any]) -> str:
return str(x["a"] * max(x["b"]))
class FSchema(BaseModel):
"""Apply a function to an integer and list of integers."""
a: int = Field(..., description="Integer")
b: list[int] = Field(..., description="List of ints")
runnable = RunnableLambda(f)
as_tool = runnable.as_tool(FSchema)
as_tool.invoke({"a": 3, "b": [1, 2]})
``dict`` input, specifying schema via ``arg_types``:
.. code-block:: python
from typing import Any
from langchain_core.runnables import RunnableLambda
def f(x: dict[str, Any]) -> str:
return str(x["a"] * max(x["b"]))
runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
as_tool.invoke({"a": 3, "b": [1, 2]})
String input:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
def f(x: str) -> str:
return x + "a"
def g(x: str) -> str:
return x + "z"
runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()
as_tool.invoke("b")
.. versionadded:: 0.2.14
- assign(self, **kwargs: 'Union[Runnable[dict[str, Any], Any], Callable[[dict[str, Any]], Any], Mapping[str, Union[Runnable[dict[str, Any], Any], Callable[[dict[str, Any]], Any]]]]') -> 'RunnableSerializable[Any, Any]'
- Assigns new fields to the dict output of this Runnable.
Returns a new Runnable.
.. code-block:: python
from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter
prompt = (
SystemMessagePromptTemplate.from_template("You are a nice assistant.")
+ "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])
chain: Runnable = prompt | llm | {"str": StrOutputParser()}
chain_with_assign = chain.assign(hello=itemgetter("str") | llm)
print(chain_with_assign.input_schema.model_json_schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.model_json_schema())
# {'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}
- async astream_events(self, input: 'Any', config: 'Optional[RunnableConfig]' = None, *, version: "Literal['v1', 'v2']" = 'v2', include_names: 'Optional[Sequence[str]]' = None, include_types: 'Optional[Sequence[str]]' = None, include_tags: 'Optional[Sequence[str]]' = None, exclude_names: 'Optional[Sequence[str]]' = None, exclude_types: 'Optional[Sequence[str]]' = None, exclude_tags: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'AsyncIterator[StreamEvent]'
- Generate a stream of events.
Use to create an iterator over StreamEvents that provide real-time information
about the progress of the Runnable, including StreamEvents from intermediate
results.
A StreamEvent is a dictionary with the following schema:
- ``event``: **str** - Event names are of the
format: on_[runnable_type]_(start|stream|end).
- ``name``: **str** - The name of the Runnable that generated the event.
- ``run_id``: **str** - randomly generated ID associated with the given execution of
the Runnable that emitted the event.
A child Runnable that gets invoked as part of the execution of a
parent Runnable is assigned its own unique ID.
- ``parent_ids``: **list[str]** - The IDs of the parent runnables that
generated the event. The root Runnable will have an empty list.
The order of the parent IDs is from the root to the immediate parent.
Only available for v2 version of the API. The v1 version of the API
will return an empty list.
- ``tags``: **Optional[list[str]]** - The tags of the Runnable that generated
the event.
- ``metadata``: **Optional[dict[str, Any]]** - The metadata of the Runnable
that generated the event.
- ``data``: **dict[str, Any]**
Below is a table that illustrates some events that might be emitted by various
chains. Metadata fields have been omitted from the table for brevity.
Chain definitions have been included after the table.
**ATTENTION** This reference table is for the V2 version of the schema.
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| event | name | chunk | input | output |
+======================+==================+=================================+===============================================+=================================================+
| on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content="hello world") |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_start | [model name] | | {'input': 'hello'} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_stream | [model name] | 'Hello' | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_end | [model name] | | 'Hello human!' | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_start | format_docs | | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_stream | format_docs | "hello world!, goodbye world!" | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_end | format_docs | | [Document(...)] | "hello world!, goodbye world!" |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_start | some_tool | | {"x": 1, "y": "2"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_end | some_tool | | | {"x": 1, "y": "2"} |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_start | [retriever name] | | {"query": "hello"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_end | [retriever name] | | {"query": "hello"} | [Document(...), ..] |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_prompt_start | [template_name] | | {"question": "hello"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
In addition to the standard events, users can also dispatch custom events (see example below).
Custom events will be only be surfaced with in the `v2` version of the API!
A custom event has following format:
+-----------+------+-----------------------------------------------------------------------------------------------------------+
| Attribute | Type | Description |
+===========+======+===========================================================================================================+
| name | str | A user defined name for the event. |
+-----------+------+-----------------------------------------------------------------------------------------------------------+
| data | Any | The data associated with the event. This can be anything, though we suggest making it JSON serializable. |
+-----------+------+-----------------------------------------------------------------------------------------------------------+
Here are declarations associated with the standard events shown above:
`format_docs`:
.. code-block:: python
def format_docs(docs: list[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
`some_tool`:
.. code-block:: python
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
`prompt`:
.. code-block:: python
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
async def reverse(s: str) -> str:
return s[::-1]
chain = RunnableLambda(func=reverse)
events = [
event async for event in chain.astream_events("hello", version="v2")
]
# will produce the following events (run_id, and parent_ids
# has been omitted for brevity):
[
{
"data": {"input": "hello"},
"event": "on_chain_start",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"chunk": "olleh"},
"event": "on_chain_stream",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"output": "olleh"},
"event": "on_chain_end",
"metadata": {},
"name": "reverse",
"tags": [],
},
]
Example: Dispatch Custom Event
.. code-block:: python
from langchain_core.callbacks.manager import (
adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio
async def slow_thing(some_input: str, config: RunnableConfig) -> str:
"""Do something that takes a long time."""
await asyncio.sleep(1) # Placeholder for some slow operation
await adispatch_custom_event(
"progress_event",
{"message": "Finished step 1 of 3"},
config=config # Must be included for python < 3.10
)
await asyncio.sleep(1) # Placeholder for some slow operation
await adispatch_custom_event(
"progress_event",
{"message": "Finished step 2 of 3"},
config=config # Must be included for python < 3.10
)
await asyncio.sleep(1) # Placeholder for some slow operation
return "Done"
slow_thing = RunnableLambda(slow_thing)
async for event in slow_thing.astream_events("some_input", version="v2"):
print(event)
Args:
input: The input to the Runnable.
config: The config to use for the Runnable.
version: The version of the schema to use either `v2` or `v1`.
Users should use `v2`.
`v1` is for backwards compatibility and will be deprecated
in 0.4.0.
No default will be assigned until the API is stabilized.
custom events will only be surfaced in `v2`.
include_names: Only include events from runnables with matching names.
include_types: Only include events from runnables with matching types.
include_tags: Only include events from runnables with matching tags.
exclude_names: Exclude events from runnables with matching names.
exclude_types: Exclude events from runnables with matching types.
exclude_tags: Exclude events from runnables with matching tags.
kwargs: Additional keyword arguments to pass to the Runnable.
These will be passed to astream_log as this implementation
of astream_events is built on top of astream_log.
Yields:
An async stream of StreamEvents.
Raises:
NotImplementedError: If the version is not `v1` or `v2`.
- async astream_log(self, input: 'Any', config: 'Optional[RunnableConfig]' = None, *, diff: 'bool' = True, with_streamed_output_list: 'bool' = True, include_names: 'Optional[Sequence[str]]' = None, include_types: 'Optional[Sequence[str]]' = None, include_tags: 'Optional[Sequence[str]]' = None, exclude_names: 'Optional[Sequence[str]]' = None, exclude_types: 'Optional[Sequence[str]]' = None, exclude_tags: 'Optional[Sequence[str]]' = None, **kwargs: 'Any') -> 'Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]'
- Stream all output from a Runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
Jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The Jsonpatch ops can be applied in order to construct state.
Args:
input: The input to the Runnable.
config: The config to use for the Runnable.
diff: Whether to yield diffs between each step or the current state.
with_streamed_output_list: Whether to yield the streamed_output list.
include_names: Only include logs with these names.
include_types: Only include logs with these types.
include_tags: Only include logs with these tags.
exclude_names: Exclude logs with these names.
exclude_types: Exclude logs with these types.
exclude_tags: Exclude logs with these tags.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
A RunLogPatch or RunLog object.
- async atransform(self, input: 'AsyncIterator[Input]', config: 'Optional[RunnableConfig]' = None, **kwargs: 'Optional[Any]') -> 'AsyncIterator[Output]'
- Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
Args:
input: An async iterator of inputs to the Runnable.
config: The config to use for the Runnable. Defaults to None.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
The output of the Runnable.
- batch(self, inputs: 'list[Input]', config: 'Optional[Union[RunnableConfig, list[RunnableConfig]]]' = None, *, return_exceptions: 'bool' = False, **kwargs: 'Optional[Any]') -> 'list[Output]'
- Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying Runnable uses an API which supports a batch mode.
- batch_as_completed(self, inputs: 'Sequence[Input]', config: 'Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]' = None, *, return_exceptions: 'bool' = False, **kwargs: 'Optional[Any]') -> 'Iterator[tuple[int, Union[Output, Exception]]]'
- Run invoke in parallel on a list of inputs.
Yields results as they complete.
- bind(self, **kwargs: 'Any') -> 'Runnable[Input, Output]'
- Bind arguments to a Runnable, returning a new Runnable.
Useful when a Runnable in a chain requires an argument that is not
in the output of the previous Runnable or included in the user input.
Args:
kwargs: The arguments to bind to the Runnable.
Returns:
A new Runnable with the arguments bound.
Example:
.. code-block:: python
from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser
llm = ChatOllama(model='llama2')
# Without bind.
chain = (
llm
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'
# With bind.
chain = (
llm.bind(stop=["three"])
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'
- config_schema(self, *, include: 'Optional[Sequence[str]]' = None) -> 'type[BaseModel]'
- The type of config this Runnable accepts specified as a pydantic model.
To mark a field as configurable, see the `configurable_fields`
and `configurable_alternatives` methods.
Args:
include: A list of fields to include in the config schema.
Returns:
A pydantic model that can be used to validate config.
- get_config_jsonschema(self, *, include: 'Optional[Sequence[str]]' = None) -> 'dict[str, Any]'
- Get a JSON schema that represents the config of the Runnable.
Args:
include: A list of fields to include in the config schema.
Returns:
A JSON schema that represents the config of the Runnable.
.. versionadded:: 0.3.0
- get_graph(self, config: 'Optional[RunnableConfig]' = None) -> 'Graph'
- Return a graph representation of this Runnable.
- get_input_jsonschema(self, config: 'Optional[RunnableConfig]' = None) -> 'dict[str, Any]'
- Get a JSON schema that represents the input to the Runnable.
Args:
config: A config to use when generating the schema.
Returns:
A JSON schema that represents the input to the Runnable.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
def add_one(x: int) -> int:
return x + 1
runnable = RunnableLambda(add_one)
print(runnable.get_input_jsonschema())
.. versionadded:: 0.3.0
- get_input_schema(self, config: 'Optional[RunnableConfig]' = None) -> 'type[BaseModel]'
- Get a pydantic model that can be used to validate input to the Runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the Runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Args:
config: A config to use when generating the schema.
Returns:
A pydantic model that can be used to validate input.
- get_name(self, suffix: 'Optional[str]' = None, *, name: 'Optional[str]' = None) -> 'str'
- Get the name of the Runnable.
- get_output_jsonschema(self, config: 'Optional[RunnableConfig]' = None) -> 'dict[str, Any]'
- Get a JSON schema that represents the output of the Runnable.
Args:
config: A config to use when generating the schema.
Returns:
A JSON schema that represents the output of the Runnable.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
def add_one(x: int) -> int:
return x + 1
runnable = RunnableLambda(add_one)
print(runnable.get_output_jsonschema())
.. versionadded:: 0.3.0
- get_output_schema(self, config: 'Optional[RunnableConfig]' = None) -> 'type[BaseModel]'
- Get a pydantic model that can be used to validate output to the Runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the Runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Args:
config: A config to use when generating the schema.
Returns:
A pydantic model that can be used to validate output.
- get_prompts(self, config: 'Optional[RunnableConfig]' = None) -> 'list[BasePromptTemplate]'
- Return a list of prompts used by this Runnable.
- map(self) -> 'Runnable[list[Input], list[Output]]'
- Return a new Runnable that maps a list of inputs to a list of outputs.
Calls invoke() with each input.
Returns:
A new Runnable that maps a list of inputs to a list of outputs.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
def _lambda(x: int) -> int:
return x + 1
runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]
- pick(self, keys: 'Union[str, list[str]]') -> 'RunnableSerializable[Any, Any]'
- Pick keys from the output dict of this Runnable.
Pick single key:
.. code-block:: python
import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}
json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]
Pick list of keys:
.. code-block:: python
from typing import Any
import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
def as_bytes(x: Any) -> bytes:
return bytes(x, "utf-8")
chain = RunnableMap(
str=as_str,
json=as_json,
bytes=RunnableLambda(as_bytes)
)
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
- pipe(self, *others: 'Union[Runnable[Any, Other], Callable[[Any], Other]]', name: 'Optional[str]' = None) -> 'RunnableSerializable[Input, Other]'
- Compose this Runnable with Runnable-like objects to make a RunnableSequence.
Equivalent to `RunnableSequence(self, *others)` or `self | others[0] | ...`
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
def add_one(x: int) -> int:
return x + 1
def mul_two(x: int) -> int:
return x * 2
runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4
sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]
- transform(self, input: 'Iterator[Input]', config: 'Optional[RunnableConfig]' = None, **kwargs: 'Optional[Any]') -> 'Iterator[Output]'
- Default implementation of transform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
Args:
input: An iterator of inputs to the Runnable.
config: The config to use for the Runnable. Defaults to None.
kwargs: Additional keyword arguments to pass to the Runnable.
Yields:
The output of the Runnable.
- with_alisteners(self, *, on_start: 'Optional[AsyncListener]' = None, on_end: 'Optional[AsyncListener]' = None, on_error: 'Optional[AsyncListener]' = None) -> 'Runnable[Input, Output]'
- Bind async lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Asynchronously called before the Runnable starts running.
on_end: Asynchronously called after the Runnable finishes running.
on_error: Asynchronously called if the Runnable throws an error.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
Args:
on_start: Asynchronously called before the Runnable starts running.
Defaults to None.
on_end: Asynchronously called after the Runnable finishes running.
Defaults to None.
on_error: Asynchronously called if the Runnable throws an error.
Defaults to None.
Returns:
A new Runnable with the listeners bound.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio
def format_t(timestamp: float) -> str:
return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()
async def test_runnable(time_to_sleep : int):
print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
await asyncio.sleep(time_to_sleep)
print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")
async def fn_start(run_obj : Runnable):
print(f"on start callback starts at {format_t(time.time())}")
await asyncio.sleep(3)
print(f"on start callback ends at {format_t(time.time())}")
async def fn_end(run_obj : Runnable):
print(f"on end callback starts at {format_t(time.time())}")
await asyncio.sleep(2)
print(f"on end callback ends at {format_t(time.time())}")
runnable = RunnableLambda(test_runnable).with_alisteners(
on_start=fn_start,
on_end=fn_end
)
async def concurrent_runs():
await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))
asyncio.run(concurrent_runs())
Result:
on start callback starts at 2025-03-01T07:05:22.875378+00:00
on start callback starts at 2025-03-01T07:05:22.875495+00:00
on start callback ends at 2025-03-01T07:05:25.878862+00:00
on start callback ends at 2025-03-01T07:05:25.878947+00:00
Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
on end callback starts at 2025-03-01T07:05:27.882360+00:00
Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
on end callback starts at 2025-03-01T07:05:28.882428+00:00
on end callback ends at 2025-03-01T07:05:29.883893+00:00
on end callback ends at 2025-03-01T07:05:30.884831+00:00
- with_config(self, config: 'Optional[RunnableConfig]' = None, **kwargs: 'Any') -> 'Runnable[Input, Output]'
- Bind config to a Runnable, returning a new Runnable.
Args:
config: The config to bind to the Runnable.
kwargs: Additional keyword arguments to pass to the Runnable.
Returns:
A new Runnable with the config bound.
- with_fallbacks(self, fallbacks: 'Sequence[Runnable[Input, Output]]', *, exceptions_to_handle: 'tuple[type[BaseException], ...]' = (<class 'Exception'>,), exception_key: 'Optional[str]' = None) -> 'RunnableWithFallbacksT[Input, Output]'
- Add fallbacks to a Runnable, returning a new Runnable.
The new Runnable will try the original Runnable, and then each fallback
in order, upon failures.
Args:
fallbacks: A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle: A tuple of exception types to handle.
Defaults to (Exception,).
exception_key: If string is specified then handled exceptions will be passed
to fallbacks as part of the input under the specified key. If None,
exceptions will not be passed to fallbacks. If used, the base Runnable
and its fallbacks must accept a dictionary as input. Defaults to None.
Returns:
A new Runnable that will try the original Runnable, and then each
fallback in order, upon failures.
Example:
.. code-block:: python
from typing import Iterator
from langchain_core.runnables import RunnableGenerator
def _generate_immediate_error(input: Iterator) -> Iterator[str]:
raise ValueError()
yield ""
def _generate(input: Iterator) -> Iterator[str]:
yield from "foo bar"
runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
[RunnableGenerator(_generate)]
)
print(''.join(runnable.stream({}))) #foo bar
Args:
fallbacks: A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle: A tuple of exception types to handle.
exception_key: If string is specified then handled exceptions will be passed
to fallbacks as part of the input under the specified key. If None,
exceptions will not be passed to fallbacks. If used, the base Runnable
and its fallbacks must accept a dictionary as input.
Returns:
A new Runnable that will try the original Runnable, and then each
fallback in order, upon failures.
- with_listeners(self, *, on_start: 'Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]' = None, on_end: 'Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]' = None, on_error: 'Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]' = None) -> 'Runnable[Input, Output]'
- Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the Runnable starts running, with the Run object.
on_end: Called after the Runnable finishes running, with the Run object.
on_error: Called if the Runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
Args:
on_start: Called before the Runnable starts running. Defaults to None.
on_end: Called after the Runnable finishes running. Defaults to None.
on_error: Called if the Runnable throws an error. Defaults to None.
Returns:
A new Runnable with the listeners bound.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run
import time
def test_runnable(time_to_sleep : int):
time.sleep(time_to_sleep)
def fn_start(run_obj: Run):
print("start_time:", run_obj.start_time)
def fn_end(run_obj: Run):
print("end_time:", run_obj.end_time)
chain = RunnableLambda(test_runnable).with_listeners(
on_start=fn_start,
on_end=fn_end
)
chain.invoke(2)
- with_retry(self, *, retry_if_exception_type: 'tuple[type[BaseException], ...]' = (<class 'Exception'>,), wait_exponential_jitter: 'bool' = True, exponential_jitter_params: 'Optional[ExponentialJitterParams]' = None, stop_after_attempt: 'int' = 3) -> 'Runnable[Input, Output]'
- Create a new Runnable that retries the original Runnable on exceptions.
Args:
retry_if_exception_type: A tuple of exception types to retry on.
Defaults to (Exception,).
wait_exponential_jitter: Whether to add jitter to the wait
time between retries. Defaults to True.
stop_after_attempt: The maximum number of attempts to make before
giving up. Defaults to 3.
exponential_jitter_params: Parameters for
``tenacity.wait_exponential_jitter``. Namely: ``initial``, ``max``,
``exp_base``, and ``jitter`` (all float values).
Returns:
A new Runnable that retries the original Runnable on exceptions.
Example:
.. code-block:: python
from langchain_core.runnables import RunnableLambda
count = 0
def _lambda(x: int) -> None:
global count
count = count + 1
if x == 1:
raise ValueError("x is 1")
else:
pass
runnable = RunnableLambda(_lambda)
try:
runnable.with_retry(
stop_after_attempt=2,
retry_if_exception_type=(ValueError,),
).invoke(1)
except ValueError:
pass
assert (count == 2)
- with_types(self, *, input_type: 'Optional[type[Input]]' = None, output_type: 'Optional[type[Output]]' = None) -> 'Runnable[Input, Output]'
- Bind input and output types to a Runnable, returning a new Runnable.
Args:
input_type: The input type to bind to the Runnable. Defaults to None.
output_type: The output type to bind to the Runnable. Defaults to None.
Returns:
A new Runnable with the types bound.
Readonly properties inherited from langchain_core.runnables.base.Runnable:
- config_specs
- List configurable fields for this Runnable.
- input_schema
- The type of input this Runnable accepts specified as a pydantic model.
- output_schema
- The type of output this Runnable produces specified as a pydantic model.
Class methods inherited from typing.Generic:
- __init_subclass__(*args, **kwargs) from pydantic._internal._model_construction.ModelMetaclass
- This method is called when a class is subclassed.
The default implementation does nothing. It may be
overridden to extend subclasses.
| |