Tony20100/langchain_full huggingface dataset:
langchain_community.llms.yandex.YandexGPT¶
class langchain_community.llms.yandex.YandexGPT[source]¶
Bases: _BaseYandexGPT, LLM
Yandex large language models.
To use, you should have the yandexcloud python package installed.
There are two authentication options for the service account
with the ai.languageModels.user role:
You can specify the token in a constructor parameter iam_token
or in an environment variable YC_IAM_TOKEN.
- You can specify the key in a constructor parameter api_key
or in an environment variable YC_API_KEY.
To use the default model specify the folder ID in a parameter folder_id
or in an environment variable YC_FOLDER_ID.
Or specify the model URI in a constructor parameter model_uri
Example
from langchain_community.llms import YandexGPT
yandex_gpt = YandexGPT(iam_token="t1.9eu...", folder_id="b1g...")
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 api_key: SecretStr = ''¶
Yandex Cloud Api Key for service account
with the ai.languageModels.user role
Constraints
type = string
writeOnly = True
format = password
param cache: Union[BaseCache, bool, None] = None¶
Whether to cache the response.
If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if it’s set, otherwise no cache.
If instance of BaseCache, will use the provided cache.
Caching is not currently supported for streaming methods of models.
param callback_manager: Optional[BaseCallbackManager] = None¶
[DEPRECATED]
param callbacks: Callbacks = None¶
[DEPRECATED]
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param disable_request_logging: bool = False¶
YandexGPT API logs all request data by default.
If you provide personal data, confidential information, disable logging.
param folder_id: str = ''¶
Yandex Cloud folder ID
param iam_token: SecretStr = ''¶
Yandex Cloud IAM token for service or user account
with the ai.languageModels.user role
Constraints
type = string
writeOnly = True
format = password
param max_retries: int = 6¶
Maximum number of retries to make when generating.
param max_tokens: int = 7400¶
Sets the maximum limit on the total number of tokens
used for both the input prompt and the generated response.
Must be greater than zero and not exceed 7400 tokens.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_name: str = 'yandexgpt-lite'¶
Model name to use.
param model_uri: str = ''¶
Model uri to use.
param model_version: str = 'latest'¶
Model version to use.
param sleep_interval: float = 1.0¶
Delay between API requests
param stop: Optional[List[str]] = None¶
Sequences when completion generation will stop.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.6¶
What sampling temperature to use.
Should be a double number between 0 (inclusive) and 1 (inclusive).
Should be a double number between 0 (inclusive) and 1 (inclusive).
param url: str = 'llm.api.cloud.yandex.net:443'¶
The url of the API.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶
[Deprecated] Check Cache and run the LLM on the given prompt and input.
Notes
Deprecated since version langchain-core==0.1.7: Use invoke instead.
Parameters
prompt (str) – 
stop (Optional[List[str]]) – 
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
str
async abatch(inputs: List[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
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.
Parameters
Parameters
input_type (Optional[Type[Input]]) – 
output_type (Optional[Type[Output]]) – 
Return type
Runnable[Input, Output]
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property config_specs: List[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[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[BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using YandexGPT¶
Yandex
YandexGPT
langchain_community.llms.tongyi.generate_with_last_element_mark¶
langchain_community.llms.tongyi.generate_with_last_element_mark(iterable: Iterable[T]) → Iterator[Tuple[T, bool]][source]¶
Generate elements from an iterable,
and a boolean indicating if it is the last element.
Parameters
iterable (Iterable[T]) – 
Return type
Iterator[Tuple[T, bool]]
langchain_community.llms.fireworks.conditional_decorator¶
langchain_community.llms.fireworks.conditional_decorator(condition: bool, decorator: Callable[[Any], Any]) → Callable[[Any], Any][source]¶
Conditionally apply a decorator.
Parameters
condition (bool) – A boolean indicating whether to apply the decorator.
decorator (Callable[[Any], Any]) – A decorator function.
Returns
A decorator function.
Return type
Callable[[Any], Any]
Construct the pipeline remotely using an auxiliary function.
The load function needs to be importable to be imported
and run on the server, i.e. in a module and not a REPL or closure.
Then, initialize the remote inference function.
param allow_dangerous_deserialization: bool = False¶
Allow deserialization using pickle which can be dangerous if
loading compromised data.
param cache: Union[BaseCache, bool, None] = None¶
Whether to cache the response.
If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if it’s set, otherwise no cache.
If instance of BaseCache, will use the provided cache.
Caching is not currently supported for streaming methods of models.
param callback_manager: Optional[BaseCallbackManager] = None¶
[DEPRECATED]
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param device: int = 0¶
Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc.
param hardware: Any = None¶
Remote hardware to send the inference function to.
param inference_fn: Callable = <function _generate_text>¶
Inference function to send to the remote hardware.
param load_fn_kwargs: Optional[dict] = None¶
Keyword arguments to pass to the model load function.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_id: str = 'gpt2'¶
Hugging Face model_id to load the model.
param model_kwargs: Optional[dict] = None¶
param model_kwargs: Optional[dict] = None¶
Keyword arguments to pass to the model.
param model_load_fn: Callable = <function _load_transformer>¶
Function to load the model remotely on the server.
param model_reqs: List[str] = ['./', 'transformers', 'torch']¶
Requirements to install on hardware to inference the model.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param task: str = 'text-generation'¶
Hugging Face task (“text-generation”, “text2text-generation” or
“summarization”).
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶
[Deprecated] Check Cache and run the LLM on the given prompt and input.
Notes
Deprecated since version langchain-core==0.1.7: Use invoke instead.
Parameters
prompt (str) – 
stop (Optional[List[str]]) – 
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
str
async abatch(inputs: List[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
values (Any) – 
Return type
Model
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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
dict(**kwargs: Any) → Dict¶
Return a dictionary of the LLM.
Parameters
kwargs (Any) – 
Return type
Dict
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) → LLM¶
Init the SelfHostedPipeline from a pipeline object or string.
Parameters
pipeline (Any) – 
hardware (Any) – 
model_reqs (Optional[List[str]]) – 
device (int) – 
kwargs (Any) – 
Return type
LLM
Parameters
input_type (Optional[Type[Input]]) – 
output_type (Optional[Type[Output]]) – 
Return type
Runnable[Input, Output]
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property config_specs: List[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[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[BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using SelfHostedHuggingFaceLLM¶
Runhouse
langchain_aws.llms.bedrock.BedrockBase¶
class langchain_aws.llms.bedrock.BedrockBase[source]¶
Bases: BaseLanguageModel, ABC
Base class for Bedrock models.
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: Union[BaseCache, bool, None] = None¶
Whether to cache the response.
If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if it’s set, otherwise no cache.
If instance of BaseCache, will use the provided cache.
Caching is not currently supported for streaming methods of models.
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param config: Any = None¶
An optional botocore.config.Config instance to pass to the client.
param credentials_profile_name: Optional[str] = None¶
The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None¶
Optional encoder to use for counting tokens.
param endpoint_url: Optional[str] = None¶
Needed if you don’t want to default to us-east-1 endpoint
param guardrails: Optional[Mapping[str, Any]] = {'guardrailIdentifier': None, 'guardrailVersion': None, 'trace': None}¶
An optional dictionary to configure guardrails for Bedrock.
An optional dictionary to configure guardrails for Bedrock.
This field ‘guardrails’ consists of two keys: ‘id’ and ‘version’,
which should be strings, but are initialized to None. It’s used to
determine if specific guardrails are enabled and properly set.
Type:Optional[Mapping[str, str]]: A mapping with ‘id’ and ‘version’ keys.
Example:
llm = Bedrock(model_id=”<model_id>”, client=<bedrock_client>,
model_kwargs={},
guardrails={
“id”: “<guardrail_id>”,
“version”: “<guardrail_version>”})
To enable tracing for guardrails, set the ‘trace’ key to True and pass a callback handler to the
‘run_manager’ parameter of the ‘generate’, ‘_call’ methods.
Example:
llm = Bedrock(model_id=”<model_id>”, client=<bedrock_client>,
model_kwargs={},
guardrails={
“id”: “<guardrail_id>”,
“version”: “<guardrail_version>”,
“trace”: True},
callbacks=[BedrockAsyncCallbackHandler()])
[https://python.langchain.com/docs/modules/callbacks/] for more information on callback handlers.
class BedrockAsyncCallbackHandler(AsyncCallbackHandler):
async def on_llm_error(self,
error: BaseException,
**kwargs: Any,
) -> Any:reason = kwargs.get(“reason”)
if reason == “GUARDRAIL_INTERVENED”:
…Logic to handle guardrail intervention…
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_id: str [Required]¶
Id of the model to call, e.g., amazon.titan-text-express-v1, this is
equivalent to the modelId property in the list-foundation-models api. For custom and
provisioned models, an ARN value is expected.
param model_kwargs: Optional[Dict[str, Any]] = None¶
Keyword arguments to pass to the model.
param provider: Optional[str] = None¶
The model provider, e.g., amazon, cohere, ai21, etc. When not supplied, provider
is extracted from the first part of the model_id e.g. ‘amazon’ in
‘amazon.titan-text-express-v1’. This value should be provided for model ids that do
not have the provider in them, e.g., custom and provisioned models that have an ARN
associated with them.
param provider_stop_reason_key_map: Mapping[str, str] = {'ai21': 'finishReason', 'amazon': 'completionReason', 'anthropic': 'stop_reason', 'cohere': 'finish_reason', 'mistral': 'stop_reason'}¶
param provider_stop_sequence_key_name_map: Mapping[str, str] = {'ai21': 'stop_sequences', 'amazon': 'stopSequences', 'anthropic': 'stop_sequences', 'cohere': 'stop_sequences', 'mistral': 'stop_sequences'}¶
param region_name: Optional[str] = None¶
The aws region e.g., us-west-2. Fallsback to AWS_DEFAULT_REGION env variable
or region specified in ~/.aws/config in case it is not provided here.
param streaming: bool = False¶
Whether to stream the results.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param verbose: bool [Optional]¶
Whether to print out response text.
param verbose: bool [Optional]¶
Whether to print out response text.
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.
Parameters
inputs (List[Input]) – 
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
List[Output]
async abatch_as_completed(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,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
AsyncIterator[Tuple[int, Union[Output, Exception]]]
abstract async agenerate_prompt(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:
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 modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts (List[PromptValue]) – 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 (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks (Callbacks) – Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs (Any) – 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 inputprompt and additional model provider-specific output.
Return type
LLMResult
async ainvoke(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any) → Output¶
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.
Parameters
input (Input) – 
config (Optional[RunnableConfig]) – 
kwargs (Any) – 
Return type
Output
abstract async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
[Deprecated] Asynchronously pass a string to the model and return a string.
[Deprecated] Asynchronously pass a string to the model and return a string.
Use this method when calling pure text generation models and only the topcandidate generation is needed.
Parameters
text (str) – String input to pass to the model.
stop (Optional[Sequence[str]]) – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a string.
Return type
str
Notes
Deprecated since version langchain-core==0.1.7: Use ainvoke instead.
abstract async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶
[Deprecated] Asynchronously pass messages to the model and return a message.
Use this method when calling chat models and only the topcandidate generation is needed.
Parameters
messages (List[BaseMessage]) – A sequence of chat messages corresponding to a single model input.
stop (Optional[Sequence[str]]) – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a message.
Return type
BaseMessage
Notes
Deprecated since version langchain-core==0.1.7: Use ainvoke instead.
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.
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
dict(*, 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) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
abstract generate_prompt(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:
take advantage of batched calls,
need more output from the model than just the top generated value,
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts (List[PromptValue]) – 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 (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks (Callbacks) – Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation.
**kwargs (Any) – 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 inputprompt and additional model provider-specific output.
Return type
LLMResult
get_graph(config: Optional[RunnableConfig] = None) → Graph¶
Return a graph representation of this runnable.
Parameters
config (Optional[RunnableConfig]) – 
Return type
Graph
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 (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
Return type
Type[BaseModel]
classmethod get_lc_namespace() → List[str]¶