`langchain_community.llms.aleph_alpha`.AlephAlpha¶

class langchain_community.llms.aleph_alpha.AlephAlpha[source]¶

Bases: LLM

Aleph Alpha large language models.

To use, you should have the aleph_alpha_client python package installed, and the environment variable ALEPH_ALPHA_API_KEY set with your API key, or pass it as a named parameter to the constructor.

Parameters are explained more in depth here: https://github.com/Aleph-Alpha/aleph-alpha-client/blob/c14b7dd2b4325c7da0d6a119f6e76385800e097b/aleph_alpha_client/completion.py#L10

Example

from langchain_community.llms import AlephAlpha
aleph_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key")

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 aleph_alpha_api_key: Optional[str] = None¶: API key for Aleph Alpha API.

param best_of: Optional[int] = None¶: returns the one with the “best of” results (highest log probability per token)

param cache: Optional[bool] = None¶

param callback_manager: Optional[BaseCallbackManager] = None¶

param callbacks: Callbacks = None¶

param completion_bias_exclusion: Optional[Sequence[str]] = None¶

param completion_bias_exclusion_first_token_only: bool = False¶: Only consider the first token for the completion_bias_exclusion.

param completion_bias_inclusion: Optional[Sequence[str]] = None¶

param completion_bias_inclusion_first_token_only: bool = False¶

param contextual_control_threshold: Optional[float] = None¶: If set to None, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-None value, control parameters are also applied to similar tokens.

param control_log_additive: Optional[bool] = True¶: True: apply control by adding the log(control_factor) to attention scores. False: (attention_scores - - attention_scores.min(-1)) * control_factor

param disable_optimizations: Optional[bool] = False¶

param echo: bool = False¶: Echo the prompt in the completion.

param frequency_penalty: float = 0.0¶: Penalizes repeated tokens according to frequency.

param host: str = 'https://api.aleph-alpha.com'¶: The hostname of the API host. The default one is “https://api.aleph-alpha.com”)

param hosting: Optional[str] = None¶: Determines in which datacenters the request may be processed. You can either set the parameter to “aleph-alpha” or omit it (defaulting to None). Not setting this value, or setting it to None, gives us maximal flexibility in processing your request in our own datacenters and on servers hosted with other providers. Choose this option for maximal availability. Setting it to “aleph-alpha” allows us to only process the request in our own datacenters. Choose this option for maximal data privacy.

param log_probs: Optional[int] = None¶: Number of top log probabilities to be returned for each generated token.

param logit_bias: Optional[Dict[int, float]] = None¶: The logit bias allows to influence the likelihood of generating tokens.

param maximum_tokens: int = 64¶: The maximum number of tokens to be generated.

param metadata: Optional[Dict[str, Any]] = None¶: Metadata to add to the run trace.

param minimum_tokens: Optional[int] = 0¶: Generate at least this number of tokens.

param model: Optional[str] = 'luminous-base'¶: Model name to use.

param n: int = 1¶: How many completions to generate for each prompt.

param nice: bool = False¶: Setting this to True, will signal to the API that you intend to be nice to other users by de-prioritizing your request below concurrent ones.

param penalty_bias: Optional[str] = None¶: Penalty bias for the completion.

param penalty_exceptions: Optional[List[str]] = None¶: List of strings that may be generated without penalty, regardless of other penalty settings

param penalty_exceptions_include_stop_sequences: Optional[bool] = None¶: Should stop_sequences be included in penalty_exceptions.

param presence_penalty: float = 0.0¶: Penalizes repeated tokens.

param raw_completion: bool = False¶: Force the raw completion of the model to be returned.

param repetition_penalties_include_completion: bool = True¶: Flag deciding whether presence penalty or frequency penalty are updated from the completion.

param repetition_penalties_include_prompt: Optional[bool] = False¶: Flag deciding whether presence penalty or frequency penalty are updated from the prompt.

param request_timeout_seconds: int = 305¶: Client timeout that will be set for HTTP requests in the requests library’s API calls. Server will close all requests after 300 seconds with an internal server error.

param sequence_penalty: float = 0.0¶

param sequence_penalty_min_length: int = 2¶

param stop_sequences: Optional[List[str]] = None¶: Stop sequences to use.

param tags: Optional[List[str]] = None¶: Tags to add to the run trace.

param temperature: float = 0.0¶: A non-negative float that tunes the degree of randomness in generation.

param tokens: Optional[bool] = False¶: return tokens of completion.

param top_k: int = 0¶: Number of most likely tokens to consider at each step.

param top_p: float = 0.0¶: Total probability mass of tokens to consider at each step.

param total_retries: int = 8¶: The number of retries made in case requests fail with certain retryable status codes. If the last retry fails a corresponding exception is raised. Note, that between retries an exponential backoff is applied, starting with 0.5 s after the first retry and doubling for each retry made. So with the default setting of 8 retries a total wait time of 63.5 s is added between the retries.

param use_multiplicative_frequency_penalty: bool = False¶

param use_multiplicative_presence_penalty: Optional[bool] = False¶: Flag deciding whether presence penalty is applied multiplicatively (True) or additively (False).

param use_multiplicative_sequence_penalty: bool = False¶

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¶: Check Cache and run the LLM on the given prompt and input.

async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], 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.

async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, run_name: Optional[Union[str, List[str]]] = None, **kwargs: Any) → LLMResult¶: Run the LLM on the given prompt and input.

async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[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: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶

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.

async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶: 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[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶

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.

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(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, run_name: Optional[Union[str, List[str]]] = None, **kwargs: Any) → LLMResult¶: Run the LLM on the given prompt and input.

generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[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_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_num_tokens(text: str) → int¶

Get the number of tokens present in the text.

Useful for checking if an input will fit in a model’s context window.

Parameters: text – The string input to tokenize.
Returns: The integer number of tokens in the text.

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_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: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶

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¶

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.

save(file_path: Union[Path, str]) → None¶

Save the LLM.

Parameters: file_path – Path to file to save the LLM to.

Example: .. code-block:: python

llm.save(file_path=”path/llm.yaml”)

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: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶: 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: Type[str]¶: Get the input 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”}

property output_schema: Type[pydantic.main.BaseModel]¶: The type of output this runnable produces specified as a pydantic model.

Examples using AlephAlpha¶

Aleph Alpha

langchain_community.llms.aleph_alpha.AlephAlpha¶

Examples using AlephAlpha¶

`langchain_community.llms.aleph_alpha`.AlephAlpha¶