| | |
- langchain_google_vertexai.chat_models.ChatVertexAI(langchain_google_vertexai._base._VertexAICommon, langchain_core.language_models.chat_models.BaseChatModel)
-
- ChatVertexAI
class ChatVertexAI(langchain_google_vertexai.chat_models.ChatVertexAI) |
| |
ChatVertexAI(*args, model: str = '', proxy_model_name: str = '', model_id: str = '', deployment_id: str = '', config_id: str = '', config_name: str = '', proxy_client: Optional[gen_ai_hub.proxy.core.base.BaseProxyClient] = None, 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, async_client: Any = None, project: Optional[str] = None, location: str = 'us-central1', request_parallelism: int = 5, max_retries: int = 6, stop_sequences: Optional[List[str]] = None, full_model_name: Optional[str] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = None, base_url: Optional[str] = None, api_transport: Optional[str] = None, default_metadata: Sequence[Tuple[str, str]] = <factory>, additional_headers: Optional[Dict[str, str]] = None, client_cert_source: Optional[Callable[[], Tuple[bytes, bytes]]] = None, credentials: Any = None, endpoint_version: Literal['v1', 'v1beta1'] = 'v1beta1', client_preview: Any = None, temperature: Optional[float] = None, frequency_penalty: Optional[float] = None, presence_penalty: Optional[float] = None, max_tokens: Optional[int] = None, top_p: Optional[float] = None, top_k: Optional[int] = None, n: int = 1, seed: Optional[int] = None, streaming: bool = False, safety_settings: Optional[SafetySettingsType] = None, tuned_model_name: Optional[str] = None, response_modalities: Optional[List[google.cloud.aiplatform_v1beta1.types.content.Modality]] = None, thinking_budget: Optional[int] = None, include_thoughts: Optional[bool] = None, audio_timestamp: Optional[bool] = None, examples: Optional[List[langchain_core.messages.base.BaseMessage]] = None, response_mime_type: Optional[str] = None, response_schema: Optional[Dict[str, Any]] = None, cached_content: Optional[str] = None, logprobs: Union[bool, int] = False, labels: Optional[Dict[str, str]] = None, perform_literal_eval_on_string_raw_content: bool = False, wait_exponential_kwargs: Optional[dict[str, float]] = None, model_kwargs: dict[str, typing.Any] = <factory>, **kwargs) -> None
Drop-in replacement for langchain_google_vertexai.ChatVertexAI.
|
| |
- Method resolution order:
- ChatVertexAI
- langchain_google_vertexai.chat_models.ChatVertexAI
- langchain_google_vertexai._base._VertexAICommon
- langchain_google_vertexai._base._VertexAIBase
- 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
- abc.ABC
- typing.Generic
- builtins.object
Methods defined here:
- __init__(self, *args, model: str = '', proxy_model_name: str = '', 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)
- Needed for mypy typing to recognize model_name as a valid arg
and for arg validation.
Class methods defined here:
- validate_params_base(values: dict) -> dict from pydantic._internal._model_construction.ModelMetaclass
- Overrides langchain_google_vertexai._base._VertexAIBase -> validate_params_base.
Original method handles some endpoint specific initialization details not required
for Generative AI Hub endpoints.
Readonly properties defined here:
- async_prediction_client
- Overrides langchain_google_vertexai._base._VertexAIBase -> async_prediction_client.
Returns PredictionServiceAsyncClient of Generative AI Hub VertexAI integration.
- prediction_client
- Overrides langchain_google_vertexai._base._VertexAIBase -> prediction_client.
Returns PredictionServiceClient of Generative AI Hub VertexAI integration.
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __class_vars__ = {'task_executor'}
- __parameters__ = ()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'function': {'function': <function ChatVertexAI.validate_environment>, 'type': 'no-info'}, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...b.proxy.langchain.google_vertexai.ChatVertexAI'>>]}, 'ref': 'gen_ai_hub.proxy.langchain.google_vertexai.ChatVertexAI:140090682786400', 'schema': {'function': {'function': <function ChatVertexAI.validate_labels>, 'type': 'no-info'}, 'schema': {'function': {'function': <function _VertexAIBase.validate_project>, 'type': 'no-info'}, 'schema': {'cls': <class 'gen_ai_hub.proxy.langchain.google_vertexai.ChatVertexAI'>, 'config': {'extra_fields_behavior': 'allow', 'title': 'ChatVertexAI', 'validate_by_alias': True, 'validate_by_name': True}, 'custom_init': True, 'root_model': False, 'schema': {'function': {'function': <bound method ChatVertexAI.build_extra of <class...b.proxy.langchain.google_vertexai.ChatVertexAI'>>, 'type': 'no-info'}, 'schema': {'function': {...}, 'schema': {...}, 'type': 'function-before'}, 'type': 'function-before'}, 'type': 'model'}, 'type': 'function-after'}, 'type': 'function-after'}, 'type': 'function-after'}
- __pydantic_custom_init__ = True
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...ecoratorInfo(mode='after'))}, computed_fields={})
- __pydantic_fields__ = {'additional_headers': FieldInfo(annotation=Union[Dict[str, str], NoneType], required=False, default=None), 'api_endpoint': FieldInfo(annotation=Union[str, NoneType], requi...default=None, alias='base_url', alias_priority=2), 'api_transport': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'async_client': FieldInfo(annotation=Any, required=False, default=None, exclude=True), 'audio_timestamp': FieldInfo(annotation=Union[bool, NoneType], requ...ble timestamp understanding of audio-only files'), 'cache': FieldInfo(annotation=Union[BaseCache, bool, NoneType], required=False, default=None, exclude=True), 'cached_content': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), '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), ...}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=Model(
ModelSeri... name: "ChatVertexAI",
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="ChatVertexAI", validator=...se,
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*args, model: str = '', proxy_model_...[str, typing.Any] = <factory>, **kwargs) -> None>
- model_config = {'arbitrary_types_allowed': True, 'extra': 'allow', 'populate_by_name': True, 'protected_namespaces': (), 'validate_by_alias': True, 'validate_by_name': True}
Methods inherited from langchain_google_vertexai.chat_models.ChatVertexAI:
- bind_tools(self, tools: '_ToolsType', tool_config: 'Optional[_ToolConfigDict]' = None, *, tool_choice: 'Optional[Union[_ToolChoiceType, bool]]' = None, **kwargs: 'Any') -> 'Runnable[LanguageModelInput, BaseMessage]'
- Bind tool-like objects to this chat model.
Assumes model is compatible with Vertex tool-calling API.
Args:
tools: A list of tool definitions to bind to this chat model.
Can be a pydantic model, callable, or BaseTool. Pydantic
models, callables, and BaseTools will be automatically converted to
their schema dictionary representation. Tools with Union types in
their arguments are now supported and converted to `anyOf` schemas.
**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.
`More info <https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#counttokensrequest>`__
- validate_environment(self) -> 'Self'
- Validate that the python package exists in environment.
- validate_labels(self) -> 'Self'
- with_structured_output(self, schema: 'Union[Dict, Type[BaseModel], Type]', *, include_raw: 'bool' = False, method: "Optional[Literal['json_mode']]" = None, **kwargs: 'Any') -> 'Runnable[LanguageModelInput, Union[Dict, BaseModel]]'
- Model wrapper that returns outputs formatted to match the given schema.
.. versionchanged:: 1.1.0
Return type corrected in version 1.1.0. Previously if a dict schema
was provided then the output had the form
``[{"args": {}, "name": "schema_name"}]`` where the output was a list with
a single dict and the "args" of the one dict corresponded to the schema.
As of ``1.1.0`` this has been fixed so that the schema (the value
corresponding to the old "args" key) is returned directly.
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. If
``method`` is ``'function_calling'`` and ``schema`` is a dict, then the dict
must match the OpenAI function-calling spec.
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'``.
method: If set to ``'json_schema'`` it will use controlled genetration to
generate the response rather than function calling. Does not work with
schemas with references or Pydantic models with self-references.
Returns:
A Runnable that takes any ChatModel input. If ``'include_raw'`` is True then a
dict with keys — raw: BaseMessage, parsed: Optional[_DictOrPydantic],
parsing_error: Optional[BaseException]. If ``'include_raw'`` is False then just
``_DictOrPydantic`` is returned, where ``_DictOrPydantic`` depends on the schema.
If schema is a Pydantic class then ``_DictOrPydantic`` is the Pydantic class.
If schema is a dict then ``_DictOrPydantic`` is a dict.
Example: Pydantic schema, exclude raw:
.. code-block:: python
from pydantic import BaseModel
from langchain_google_vertexai import ChatVertexAI
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatVertexAI(model_name="gemini-2.0-flash-001", 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='A pound is a pound.'
# )
Example: Pydantic schema, include raw:
.. code-block:: python
from pydantic import BaseModel
from langchain_google_vertexai import ChatVertexAI
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatVertexAI(model_name="gemini-2.0-flash-001", 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, exclude raw:
.. code-block:: python
from pydantic import BaseModel
from langchain_core.utils.function_calling import convert_to_openai_function
from langchain_google_vertexai import ChatVertexAI
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
dict_schema = convert_to_openai_function(AnswerWithJustification)
llm = ChatVertexAI(model_name="gemini-2.0-flash-001", 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.'
# }
Example: Pydantic schema, streaming:
.. code-block:: python
from pydantic import BaseModel, Field
from langchain_google_vertexai import ChatVertexAI
class Explanation(BaseModel):
'''A topic explanation with examples.'''
description: str = Field(description="A brief description of the topic.")
examples: str = Field(description="Two examples related to the topic.")
llm = ChatVertexAI(model_name="gemini-2.0-flash", temperature=0)
structured_llm = llm.with_structured_output(Explanation, method="json_mode")
for chunk in structured_llm.stream("Tell me about transformer models"):
print(chunk)
print('-------------------------')
# -> description='Transformer models are a type of neural network architecture that have revolutionized the field of natural language processing (NLP) and are also increasingly used in computer vision and other domains. They rely on the self-attention mechanism to weigh the importance of different parts of the input data, allowing them to effectively capture long-range dependencies. Unlike recurrent neural networks (RNNs), transformers can process the entire input sequence in parallel, leading to significantly faster training times. Key components of transformer models include: the self-attention mechanism (calculates attention weights between different parts of the input), multi-head attention (performs self-attention multiple times with different learned parameters), positional encoding (adds information about the position of tokens in the input sequence), feedforward networks (applies a non-linear transformation to each position), and encoder-decoder structure (used for sequence-to-sequence tasks).' examples='1. BERT (Bidirectional Encoder Representations from Transformers): A pre-trained transformer'
# -------------------------
# description='Transformer models are a type of neural network architecture that have revolutionized the field of natural language processing (NLP) and are also increasingly used in computer vision and other domains. They rely on the self-attention mechanism to weigh the importance of different parts of the input data, allowing them to effectively capture long-range dependencies. Unlike recurrent neural networks (RNNs), transformers can process the entire input sequence in parallel, leading to significantly faster training times. Key components of transformer models include: the self-attention mechanism (calculates attention weights between different parts of the input), multi-head attention (performs self-attention multiple times with different learned parameters), positional encoding (adds information about the position of tokens in the input sequence), feedforward networks (applies a non-linear transformation to each position), and encoder-decoder structure (used for sequence-to-sequence tasks).' examples='1. BERT (Bidirectional Encoder Representations from Transformers): A pre-trained transformer model that can be fine-tuned for various NLP tasks like text classification, question answering, and named entity recognition. 2. GPT (Generative Pre-trained Transformer): A language model that uses transformers to generate coherent and contextually relevant text. GPT models are used in chatbots, content creation, and code generation.'
# -------------------------
Class methods inherited from langchain_google_vertexai.chat_models.ChatVertexAI:
- build_extra(values: 'dict[str, Any]') -> 'Any' from pydantic._internal._model_construction.ModelMetaclass
- Build extra kwargs from additional params that were passed in.
- 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
- 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.
Data and other attributes inherited from langchain_google_vertexai.chat_models.ChatVertexAI:
- __annotations__ = {'cached_content': 'Optional[str]', 'examples': 'Optional[List[BaseMessage]]', 'labels': 'Optional[Dict[str, str]]', 'logprobs': 'Union[bool, int]', 'model_kwargs': 'dict[str, Any]', 'model_name': 'str', 'perform_literal_eval_on_string_raw_content': 'bool', 'response_mime_type': 'Optional[str]', 'response_schema': 'Optional[Dict[str, Any]]', 'wait_exponential_kwargs': 'Optional[dict[str, float]]'}
Readonly properties inherited from langchain_google_vertexai._base._VertexAICommon:
- max_tokens
Methods inherited from langchain_google_vertexai._base._VertexAIBase:
- validate_project(self) -> 'Any'
Data descriptors inherited from langchain_google_vertexai._base._VertexAIBase:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes inherited from langchain_google_vertexai._base._VertexAIBase:
- task_executor = FieldInfo(annotation=NoneType, required=False, default=None, exclude=True)
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.
Raises:
ValueError: If the generation is not a chat generation.
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'
- 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. Defaults to None.
Returns:
The output of the ``Runnable``.
- 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. Defaults to None.
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.
Raises:
ValueError: If the output is not a string.
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
- Emit deprecation warning if ``callback_manager`` is used.
Args:
values (Dict): Values to validate.
Returns:
Dict: Validated values.
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_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-3-7-sonnet-20250219"
).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.
Raises:
ValueError: If a configuration key is not found in the ``Runnable``.
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.
Returns:
SerializedNotImplemented.
Class methods inherited from langchain_core.load.serializable.Serializable:
- lc_id() -> list[str] from pydantic._internal._model_construction.ModelMetaclass
- Return 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.
- lc_secrets
- A map of constructor argument names to secret ids.
For example,
{"openai_api_key": "OPENAI_API_KEY"}
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: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- 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/models.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, exclude_computed_fields: '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#python-mode)
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`.
exclude_computed_fields: Whether to exclude computed fields.
While this can be useful for round-tripping, it is usually recommended to use the dedicated
`round_trip` parameter instead.
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, ensure_ascii: 'bool' = False, 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, exclude_computed_fields: '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#json-mode)
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.
ensure_ascii: If `True`, the output is guaranteed to have all incoming non-ASCII characters escaped.
If `False` (the default), these characters will be output as-is.
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`.
exclude_computed_fields: Whether to exclude computed fields.
While this can be useful for round-tripping, it is usually recommended to use the dedicated
`round_trip` parameter instead.
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 basic class initialization is complete. In particular, attributes like `model_fields` will
be present when this is called, but forward annotations are not guaranteed to be resolved yet,
meaning that creating an instance of the class may fail.
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.
Note:
You may want to override [`__pydantic_on_complete__()`][pydantic.main.BaseModel.__pydantic_on_complete__]
instead, which is called once the class and its fields are fully initialized and ready for validation.
- __pydantic_on_complete__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- This is called once the class and its fields are fully initialized and ready to be used.
This typically happens when the class is created (just before
[`__pydantic_init_subclass__()`][pydantic.main.BaseModel.__pydantic_init_subclass__] is called on the superclass),
except when forward annotations are used that could not immediately be resolved.
In that case, it will be called later, when the model is rebuilt automatically or explicitly using
[`model_rebuild()`][pydantic.main.BaseModel.model_rebuild].
- 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', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> '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.
union_format: The format to use when combining schemas from unions together. Can be one of:
- `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
keyword to combine schemas (the default).
- `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type)
keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive
type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to
`any_of`.
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, extra: 'ExtraValues | 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.
extra: Whether to ignore, allow, or forbid extra data during model validation.
See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
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, extra: 'ExtraValues | 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.
extra: Whether to ignore, allow, or forbid extra data during model validation.
See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
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, extra: 'ExtraValues | 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.
extra: Whether to ignore, allow, or forbid extra data during model validation.
See the [`extra` configuration value][pydantic.ConfigDict.extra] for details.
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_headers': FieldInfo(annotation=Union[Dict[str, str], NoneType], required=False, default=None), 'api_endpoint': FieldInfo(annotation=Union[str, NoneType], requi...default=None, alias='base_url', alias_priority=2), 'api_transport': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'async_client': FieldInfo(annotation=Any, required=False, default=None, exclude=True), 'audio_timestamp': FieldInfo(annotation=Union[bool, NoneType], requ...ble timestamp understanding of audio-only files'), 'cache': FieldInfo(annotation=Union[BaseCache, bool, NoneType], required=False, default=None, exclude=True), 'cached_content': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), '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), ...}
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]'
- Runnable "or" operator.
Compose this ``Runnable`` with another object to create a
``RunnableSequence``.
Args:
other: Another ``Runnable`` or a ``Runnable``-like object.
Returns:
A new ``Runnable``.
- __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]'
- Runnable "reverse-or" operator.
Compose this ``Runnable`` with another object to create a
``RunnableSequence``.
Args:
other: Another ``Runnable`` or a ``Runnable``-like object.
Returns:
A new ``Runnable``.
- 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.
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``.
.. 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'}}}
Args:
**kwargs: A mapping of keys to ``Runnable`` or ``Runnable``-like objects
that will be invoked with the entire output dict of this ``Runnable``.
Returns:
A new ``Runnable``.
- 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.
.. note::
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]'
- Transform inputs to outputs.
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.
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``.
- 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.
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``.
Yields:
Tuples of the index of the input and the output from the ``Runnable``.
- 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.
``Runnable``s 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``.
Args:
suffix: An optional suffix to append to the name.
name: An optional name to use instead of the ``Runnable``'s name.
Returns:
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``.
``Runnable``s 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]"}
Args:
keys: A key or list of keys to pick from the output dict.
Returns:
a new ``Runnable``.
- pipe(self, *others: 'Union[Runnable[Any, Other], Callable[[Any], Other]]', name: 'Optional[str]' = None) -> 'RunnableSerializable[Input, Other]'
- Pipe runnables.
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]
Args:
*others: Other ``Runnable`` or ``Runnable``-like objects to compose
name: An optional name for the resulting ``RunnableSequence``.
Returns:
A new ``Runnable``.
- transform(self, input: 'Iterator[Input]', config: 'Optional[RunnableConfig]' = None, **kwargs: 'Optional[Any]') -> 'Iterator[Output]'
- Transform inputs to outputs.
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``.
Returns a new ``Runnable``.
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 asynchronously before the ``Runnable`` starts running,
with the ``Run`` object. Defaults to None.
on_end: Called asynchronously after the ``Runnable`` finishes running,
with the ``Run`` object. Defaults to None.
on_error: Called asynchronously if the ``Runnable`` throws an error,
with the ``Run`` object. 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``.
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, with the ``Run``
object. Defaults to None.
on_end: Called after the ``Runnable`` finishes running, with the ``Run``
object. Defaults to None.
on_error: Called if the ``Runnable`` throws an error, with the ``Run``
object. 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
- 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.
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