mirror of
https://github.com/hwchase17/langchain
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288 lines
11 KiB
Python
288 lines
11 KiB
Python
from typing import Any, Dict, List, Optional, Sequence
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import LLM
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from langchain_core.pydantic_v1 import Extra, root_validator
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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from langchain_community.llms.utils import enforce_stop_tokens
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class AlephAlpha(LLM):
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"""Aleph Alpha large language models.
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To use, you should have the ``aleph_alpha_client`` python package installed, and the
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environment variable ``ALEPH_ALPHA_API_KEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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Parameters are explained more in depth here:
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https://github.com/Aleph-Alpha/aleph-alpha-client/blob/c14b7dd2b4325c7da0d6a119f6e76385800e097b/aleph_alpha_client/completion.py#L10
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Example:
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.. code-block:: python
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from langchain_community.llms import AlephAlpha
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aleph_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key")
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"""
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client: Any #: :meta private:
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model: Optional[str] = "luminous-base"
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"""Model name to use."""
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maximum_tokens: int = 64
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"""The maximum number of tokens to be generated."""
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temperature: float = 0.0
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"""A non-negative float that tunes the degree of randomness in generation."""
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top_k: int = 0
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"""Number of most likely tokens to consider at each step."""
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top_p: float = 0.0
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"""Total probability mass of tokens to consider at each step."""
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presence_penalty: float = 0.0
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"""Penalizes repeated tokens."""
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frequency_penalty: float = 0.0
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"""Penalizes repeated tokens according to frequency."""
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repetition_penalties_include_prompt: Optional[bool] = False
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"""Flag deciding whether presence penalty or frequency penalty are
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updated from the prompt."""
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use_multiplicative_presence_penalty: Optional[bool] = False
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"""Flag deciding whether presence penalty is applied
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multiplicatively (True) or additively (False)."""
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penalty_bias: Optional[str] = None
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"""Penalty bias for the completion."""
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penalty_exceptions: Optional[List[str]] = None
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"""List of strings that may be generated without penalty,
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regardless of other penalty settings"""
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penalty_exceptions_include_stop_sequences: Optional[bool] = None
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"""Should stop_sequences be included in penalty_exceptions."""
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best_of: Optional[int] = None
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"""returns the one with the "best of" results
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(highest log probability per token)
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"""
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n: int = 1
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"""How many completions to generate for each prompt."""
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logit_bias: Optional[Dict[int, float]] = None
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"""The logit bias allows to influence the likelihood of generating tokens."""
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log_probs: Optional[int] = None
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"""Number of top log probabilities to be returned for each generated token."""
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tokens: Optional[bool] = False
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"""return tokens of completion."""
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disable_optimizations: Optional[bool] = False
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minimum_tokens: Optional[int] = 0
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"""Generate at least this number of tokens."""
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echo: bool = False
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"""Echo the prompt in the completion."""
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use_multiplicative_frequency_penalty: bool = False
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sequence_penalty: float = 0.0
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sequence_penalty_min_length: int = 2
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use_multiplicative_sequence_penalty: bool = False
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completion_bias_inclusion: Optional[Sequence[str]] = None
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completion_bias_inclusion_first_token_only: bool = False
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completion_bias_exclusion: Optional[Sequence[str]] = None
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completion_bias_exclusion_first_token_only: bool = False
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"""Only consider the first token for the completion_bias_exclusion."""
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contextual_control_threshold: Optional[float] = None
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"""If set to None, attention control parameters only apply to those tokens that have
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explicitly been set in the request.
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If set to a non-None value, control parameters are also applied to similar tokens.
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"""
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control_log_additive: Optional[bool] = True
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"""True: apply control by adding the log(control_factor) to attention scores.
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False: (attention_scores - - attention_scores.min(-1)) * control_factor
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"""
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repetition_penalties_include_completion: bool = True
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"""Flag deciding whether presence penalty or frequency penalty
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are updated from the completion."""
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raw_completion: bool = False
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"""Force the raw completion of the model to be returned."""
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stop_sequences: Optional[List[str]] = None
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"""Stop sequences to use."""
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# Client params
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aleph_alpha_api_key: Optional[str] = None
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"""API key for Aleph Alpha API."""
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host: str = "https://api.aleph-alpha.com"
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"""The hostname of the API host.
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The default one is "https://api.aleph-alpha.com")"""
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hosting: Optional[str] = None
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"""Determines in which datacenters the request may be processed.
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You can either set the parameter to "aleph-alpha" or omit it (defaulting to None).
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Not setting this value, or setting it to None, gives us maximal
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flexibility in processing your request in our
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own datacenters and on servers hosted with other providers.
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Choose this option for maximal availability.
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Setting it to "aleph-alpha" allows us to only process the
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request in our own datacenters.
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Choose this option for maximal data privacy."""
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request_timeout_seconds: int = 305
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"""Client timeout that will be set for HTTP requests in the
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`requests` library's API calls.
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Server will close all requests after 300 seconds with an internal server error."""
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total_retries: int = 8
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"""The number of retries made in case requests fail with certain retryable
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status codes. If the last
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retry fails a corresponding exception is raised. Note, that between retries
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an exponential backoff
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is applied, starting with 0.5 s after the first retry and doubling for
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each retry made. So with the
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default setting of 8 retries a total wait time of 63.5 s is added
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between the retries."""
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nice: bool = False
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"""Setting this to True, will signal to the API that you intend to be
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nice to other users
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by de-prioritizing your request below concurrent ones."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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values["aleph_alpha_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY")
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)
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try:
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from aleph_alpha_client import Client
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values["client"] = Client(
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token=values["aleph_alpha_api_key"].get_secret_value(),
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host=values["host"],
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hosting=values["hosting"],
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request_timeout_seconds=values["request_timeout_seconds"],
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total_retries=values["total_retries"],
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nice=values["nice"],
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)
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except ImportError:
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raise ImportError(
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"Could not import aleph_alpha_client python package. "
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"Please install it with `pip install aleph_alpha_client`."
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)
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling the Aleph Alpha API."""
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return {
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"maximum_tokens": self.maximum_tokens,
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"temperature": self.temperature,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"presence_penalty": self.presence_penalty,
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"frequency_penalty": self.frequency_penalty,
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"n": self.n,
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"repetition_penalties_include_prompt": self.repetition_penalties_include_prompt, # noqa: E501
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"use_multiplicative_presence_penalty": self.use_multiplicative_presence_penalty, # noqa: E501
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"penalty_bias": self.penalty_bias,
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"penalty_exceptions": self.penalty_exceptions,
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"penalty_exceptions_include_stop_sequences": self.penalty_exceptions_include_stop_sequences, # noqa: E501
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"best_of": self.best_of,
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"logit_bias": self.logit_bias,
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"log_probs": self.log_probs,
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"tokens": self.tokens,
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"disable_optimizations": self.disable_optimizations,
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"minimum_tokens": self.minimum_tokens,
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"echo": self.echo,
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"use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501
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"sequence_penalty": self.sequence_penalty,
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"sequence_penalty_min_length": self.sequence_penalty_min_length,
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"use_multiplicative_sequence_penalty": self.use_multiplicative_sequence_penalty, # noqa: E501
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"completion_bias_inclusion": self.completion_bias_inclusion,
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"completion_bias_inclusion_first_token_only": self.completion_bias_inclusion_first_token_only, # noqa: E501
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"completion_bias_exclusion": self.completion_bias_exclusion,
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"completion_bias_exclusion_first_token_only": self.completion_bias_exclusion_first_token_only, # noqa: E501
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"contextual_control_threshold": self.contextual_control_threshold,
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"control_log_additive": self.control_log_additive,
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"repetition_penalties_include_completion": self.repetition_penalties_include_completion, # noqa: E501
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"raw_completion": self.raw_completion,
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}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model": self.model}, **self._default_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "aleph_alpha"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Call out to Aleph Alpha's completion endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = aleph_alpha("Tell me a joke.")
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"""
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from aleph_alpha_client import CompletionRequest, Prompt
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params = self._default_params
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if self.stop_sequences is not None and stop is not None:
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raise ValueError(
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"stop sequences found in both the input and default params."
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)
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elif self.stop_sequences is not None:
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params["stop_sequences"] = self.stop_sequences
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else:
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params["stop_sequences"] = stop
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params = {**params, **kwargs}
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request = CompletionRequest(prompt=Prompt.from_text(prompt), **params)
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response = self.client.complete(model=self.model, request=request)
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text = response.completions[0].completion
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# If stop tokens are provided, Aleph Alpha's endpoint returns them.
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# In order to make this consistent with other endpoints, we strip them.
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if stop is not None or self.stop_sequences is not None:
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text = enforce_stop_tokens(text, params["stop_sequences"])
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return text
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if __name__ == "__main__":
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aa = AlephAlpha()
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print(aa("How are you?")) # noqa: T201
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