NLPCloud client integration (#81)

lots of kwargs! generation docs here:
https://docs.nlpcloud.com/#generation

This somewhat breaks the paradigm introduced in LLM base class as the
stop sequence isn't a list, and should rightfully be introduced at the
time of initialization of the class, along with the other kwargs that
depend on its presence (e.g. remove_end_sequence, etc.) curious if you'd
want to refactor LLM base class to take out stop as a specific named
kwarg?
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Samantha Whitmore 2022-11-08 06:24:23 -08:00 committed by GitHub
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"""Wrappers on top of large language models APIs."""
from langchain.llms.cohere import Cohere
from langchain.llms.huggingface_hub import HuggingFaceHub
from langchain.llms.nlpcloud import NLPCloud
from langchain.llms.openai import OpenAI
__all__ = ["Cohere", "OpenAI", "HuggingFaceHub"]
__all__ = ["Cohere", "NLPCloud", "OpenAI", "HuggingFaceHub"]

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

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cohere
openai
google-search-results
nlpcloud
playwright
wikipedia
huggingface_hub

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"""Test NLPCloud API wrapper."""
from langchain.llms.nlpcloud import NLPCloud
def test_nlpcloud_call() -> None:
"""Test valid call to nlpcloud."""
llm = NLPCloud(max_length=10)
output = llm("Say foo:")
assert isinstance(output, str)