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142 lines
4.9 KiB
Python
142 lines
4.9 KiB
Python
"""Wrapper around NLPCloud APIs."""
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from typing import Any, Dict, List, Mapping, Optional
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from pydantic import Extra, root_validator
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from langchain.llms.base import LLM
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from langchain.utils import get_from_dict_or_env
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class NLPCloud(LLM):
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"""Wrapper around NLPCloud large language models.
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To use, you should have the ``nlpcloud`` python package installed, and the
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environment variable ``NLPCLOUD_API_KEY`` set with your API key.
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Example:
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.. code-block:: python
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from langchain.llms import NLPCloud
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nlpcloud = NLPCloud(model="gpt-neox-20b")
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"""
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client: Any #: :meta private:
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model_name: str = "finetuned-gpt-neox-20b"
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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min_length: int = 1
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"""The minimum number of tokens to generate in the completion."""
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max_length: int = 256
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"""The maximum number of tokens to generate in the completion."""
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length_no_input: bool = True
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"""Whether min_length and max_length should include the length of the input."""
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remove_input: bool = True
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"""Remove input text from API response"""
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remove_end_sequence: bool = True
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"""Whether or not to remove the end sequence token."""
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bad_words: List[str] = []
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"""List of tokens not allowed to be generated."""
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top_p: int = 1
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"""Total probability mass of tokens to consider at each step."""
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top_k: int = 50
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"""The number of highest probability tokens to keep for top-k filtering."""
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repetition_penalty: float = 1.0
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"""Penalizes repeated tokens. 1.0 means no penalty."""
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length_penalty: float = 1.0
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"""Exponential penalty to the length."""
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do_sample: bool = True
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"""Whether to use sampling (True) or greedy decoding."""
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num_beams: int = 1
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"""Number of beams for beam search."""
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early_stopping: bool = False
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"""Whether to stop beam search at num_beams sentences."""
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num_return_sequences: int = 1
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"""How many completions to generate for each prompt."""
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nlpcloud_api_key: Optional[str] = None
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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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nlpcloud_api_key = get_from_dict_or_env(
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values, "nlpcloud_api_key", "NLPCLOUD_API_KEY"
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)
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try:
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import nlpcloud
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values["client"] = nlpcloud.Client(
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values["model_name"], nlpcloud_api_key, gpu=True, lang="en"
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)
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except ImportError:
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raise ValueError(
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"Could not import nlpcloud python package. "
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"Please install it with `pip install nlpcloud`."
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)
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return values
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@property
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def _default_params(self) -> Mapping[str, Any]:
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"""Get the default parameters for calling NLPCloud API."""
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return {
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"temperature": self.temperature,
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"min_length": self.min_length,
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"max_length": self.max_length,
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"length_no_input": self.length_no_input,
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"remove_input": self.remove_input,
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"remove_end_sequence": self.remove_end_sequence,
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"bad_words": self.bad_words,
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"top_p": self.top_p,
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"top_k": self.top_k,
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"repetition_penalty": self.repetition_penalty,
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"length_penalty": self.length_penalty,
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"do_sample": self.do_sample,
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"num_beams": self.num_beams,
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"early_stopping": self.early_stopping,
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"num_return_sequences": self.num_return_sequences,
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}
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model_name": self.model_name}, **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 "nlpcloud"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Call out to NLPCloud's create endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Not supported by this interface (pass in init method)
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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 = nlpcloud("Tell me a joke.")
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"""
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if stop and len(stop) > 1:
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raise ValueError(
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"NLPCloud only supports a single stop sequence per generation."
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"Pass in a list of length 1."
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)
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elif stop and len(stop) == 1:
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end_sequence = stop[0]
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else:
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end_sequence = None
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response = self.client.generation(
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prompt, end_sequence=end_sequence, **self._default_params
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)
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return response["generated_text"]
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