langchain_community.chat_models.cohere
.ChatCohere¶
- class langchain_community.chat_models.cohere.ChatCohere[source]¶
Bases:
BaseChatModel
,BaseCohere
Cohere chat large language models.
To use, you should have the
cohere
python package installed, and the environment variableCOHERE_API_KEY
set with your API key, or pass it as a named parameter to the constructor.Example
from langchain_community.chat_models import ChatCohere from langchain_core.messages import HumanMessage chat = ChatCohere(model="foo") result = chat([HumanMessage(content="Hello")]) print(result.content)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
- param cache: Optional[bool] = None¶
Whether to cache the response.
- param callback_manager: Optional[BaseCallbackManager] = None¶
Callback manager to add to the run trace.
- param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
- param cohere_api_key: Optional[str] = None¶
- param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
- param model: Optional[str] = None¶
Model name to use.
- param stop: Optional[List[str]] = None¶
- param streaming: bool = False¶
Whether to stream the results.
- param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
- param temperature: float = 0.75¶
A non-negative float that tunes the degree of randomness in generation.
- param user_agent: str = 'langchain'¶
Identifier for the application making the request.
- param verbose: bool [Optional]¶
Whether to print out response text.
- __call__(messages: List[BaseMessage], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) BaseMessage ¶
Call self as a function.
- async abatch(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.
- async agenerate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) LLMResult ¶
Top Level call
- async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = 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:
take advantage of batched calls,
need more output from the model than just the top generated value,
- are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
- Parameters
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(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) BaseMessage ¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
- async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str ¶
Asynchronously pass a string to the model and return a string prediction.
- Use this method when calling pure text generation models and only the top
candidate generation is needed.
- Parameters
text – String input to pass to the model.
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
Top model prediction as a string.
- async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage ¶
Asynchronously pass messages to the model and return a message prediction.
- Use this method when calling chat models and only the top
candidate generation is needed.
- Parameters
messages – A sequence of chat messages corresponding to a single model input.
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
Top model prediction as a message.
- assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) RunnableSerializable[Any, Any] ¶
Assigns new fields to the dict output of this runnable. Returns a new runnable.
- async astream(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.
- async astream_log(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: Optional[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.
- Parameters
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.
- async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) AsyncIterator[Output] ¶
Default implementation of atransform, which buffers input and calls astream. Subclasses should override this method if they can start producing output while input is still being generated.
- batch(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.
- bind(**kwargs: Any) Runnable[Input, Output] ¶
Bind arguments to a Runnable, returning a new Runnable.
- call_as_llm(message: str, stop: Optional[List[str]] = None, **kwargs: Any) str ¶
- config_schema(*, 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.
- Parameters
include – A list of fields to include in the config schema.
- Returns
A pydantic model that can be used to validate config.
- configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) RunnableSerializable[Input, Output] ¶
- configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) RunnableSerializable[Input, Output] ¶
- classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model ¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model ¶
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model
- Returns
new model instance
- dict(**kwargs: Any) Dict ¶
Return a dictionary of the LLM.
- classmethod from_orm(obj: Any) Model ¶
- generate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) LLMResult ¶
Top Level call
- generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = 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:
take advantage of batched calls,
need more output from the model than just the top generated value,
- are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
- Parameters
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.
- get_graph(config: Optional[RunnableConfig] = None) Graph ¶
Return a graph representation of this runnable.
- get_input_schema(config: Optional[RunnableConfig] = None) Type[BaseModel] ¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic input schema that depends on which configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
- Parameters
config – A config to use when generating the schema.
- Returns
A pydantic model that can be used to validate input.
- classmethod get_lc_namespace() List[str] ¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the namespace is [“langchain”, “llms”, “openai”]
- get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) str ¶
Get the name of the runnable.
- get_num_tokens_from_messages(messages: List[BaseMessage]) int ¶
Get the number of tokens in the messages.
Useful for checking if an input will fit in a model’s context window.
- Parameters
messages – The message inputs to tokenize.
- Returns
The sum of the number of tokens across the messages.
- get_output_schema(config: Optional[RunnableConfig] = None) Type[BaseModel] ¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
- Parameters
config – A config to use when generating the schema.
- Returns
A pydantic model that can be used to validate output.
- get_prompts(config: Optional[RunnableConfig] = None) List[BasePromptTemplate] ¶
- get_token_ids(text: str) List[int] ¶
Return the ordered ids of the tokens in a text.
- Parameters
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.
- invoke(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) BaseMessage ¶
Transform a single input into an output. Override to implement.
- Parameters
input – The input to the runnable.
config – A config to use when invoking the runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details.
- Returns
The output of the runnable.
- classmethod is_lc_serializable() bool ¶
Is this class serializable?
- json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode ¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
- classmethod lc_id() List[str] ¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path to the object.
- map() Runnable[List[Input], List[Output]] ¶
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
- classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- classmethod parse_obj(obj: Any) Model ¶
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- pick(keys: Union[str, List[str]]) RunnableSerializable[Any, Any] ¶
Pick keys from the dict output of this runnable. Returns a new runnable.
- pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) RunnableSerializable[Input, Other] ¶
Compose this runnable with another object to create a RunnableSequence.
- predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str ¶
Pass a single string input to the model and return a string prediction.
- Use this method when passing in raw text. If you want to pass in specific
types of chat messages, use predict_messages.
- Parameters
text – String input to pass to the model.
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
Top model prediction as a string.
- predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage ¶
Pass a message sequence to the model and return a message prediction.
- Use this method when passing in chat messages. If you want to pass in raw text,
use predict.
- Parameters
messages – A sequence of chat messages corresponding to a single model input.
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
Top model prediction as a message.
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny ¶
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode ¶
- stream(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.
- to_json() Union[SerializedConstructor, SerializedNotImplemented] ¶
- to_json_not_implemented() SerializedNotImplemented ¶
- transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) Iterator[Output] ¶
Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.
- classmethod update_forward_refs(**localns: Any) None ¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- classmethod validate(value: Any) Model ¶
- with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) Runnable[Input, Output] ¶
Bind config to a Runnable, returning a new Runnable.
- with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) RunnableWithFallbacksT[Input, Output] ¶
Add fallbacks to a runnable, returning a new Runnable.
- Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
- Returns
A new Runnable that will try the original runnable, and then each fallback in order, upon failures.
- with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) Runnable[Input, Output] ¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run object. on_end: Called after the runnable finishes running, with the Run object. on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.
- with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) Runnable[Input, Output] ¶
Create a new Runnable that retries the original runnable on exceptions.
- Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time between retries
stop_after_attempt – The maximum number of attempts to make before giving up
- Returns
A new Runnable that retries the original runnable on exceptions.
- with_types(*, 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.
- property InputType: TypeAlias¶
Get the input type for this runnable.
- property OutputType: Any¶
Get the output type for this runnable.
- property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
- property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
- property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
- property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
- For example,
{“openai_api_key”: “OPENAI_API_KEY”}
- name: Optional[str] = None¶
The name of the runnable. Used for debugging and tracing.
- property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.