mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-07-19 21:33:31 -04:00
Jan Pawellek
3e3ed8c5c9
LLM configurations can be loaded from a Python dict (or JSON file deserialized as dict) using the [load_llm_from_config](https://github.com/hwchase17/langchain/blob/8e1a7a8646dd2e64400c2011fbdcc148127d8ffd/langchain/llms/loading.py#L12) function. However, the type string in the `type_to_cls_dict` lookup dict differs from the type string defined in some LLM classes. This means that the LLM object can be saved, but not loaded again, because the type strings differ.
245 lines
9.2 KiB
Python
245 lines
9.2 KiB
Python
"""Wrapper around Aleph Alpha APIs."""
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from typing import Any, Dict, List, Optional, Sequence
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from pydantic import Extra, root_validator
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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from langchain.utils import get_from_dict_or_env
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class AlephAlpha(LLM):
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"""Wrapper around 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.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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aleph_alpha_api_key: Optional[str] = None
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"""API key for Aleph Alpha API."""
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stop_sequences: Optional[List[str]] = None
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"""Stop sequences to use."""
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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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aleph_alpha_api_key = get_from_dict_or_env(
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values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
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)
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try:
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import aleph_alpha_client
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values["client"] = aleph_alpha_client.Client(token=aleph_alpha_api_key)
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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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