forked from Archives/langchain
fd69cc7e42
Removed duplicate BaseModel dependencies in class inheritances. Also, sorted imports by `isort`.
156 lines
4.9 KiB
Python
156 lines
4.9 KiB
Python
"""Wrapper around Writer APIs."""
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from typing import Any, Dict, List, Mapping, Optional
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import requests
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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.llms.utils import enforce_stop_tokens
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from langchain.utils import get_from_dict_or_env
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class Writer(LLM):
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"""Wrapper around Writer large language models.
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To use, you should have the environment variable ``WRITER_API_KEY``
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set with your API key.
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Example:
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.. code-block:: python
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from langchain import Writer
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writer = Writer(model_id="palmyra-base")
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"""
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model_id: str = "palmyra-base"
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"""Model name to use."""
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tokens_to_generate: int = 24
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"""Max number of tokens to generate."""
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logprobs: bool = False
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"""Whether to return log probabilities."""
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temperature: float = 1.0
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"""What sampling temperature to use."""
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length: int = 256
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"""The maximum number of tokens to generate in the completion."""
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top_p: float = 1.0
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"""Total probability mass of tokens to consider at each step."""
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top_k: int = 1
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"""The number of highest probability vocabulary tokens to
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keep for top-k-filtering."""
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repetition_penalty: float = 1.0
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"""Penalizes repeated tokens according to frequency."""
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random_seed: int = 0
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"""The model generates random results.
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Changing the random seed alone will produce a different response
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with similar characteristics. It is possible to reproduce results
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by fixing the random seed (assuming all other hyperparameters
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are also fixed)"""
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beam_search_diversity_rate: float = 1.0
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"""Only applies to beam search, i.e. when the beam width is >1.
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A higher value encourages beam search to return a more diverse
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set of candidates"""
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beam_width: Optional[int] = None
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"""The number of concurrent candidates to keep track of during
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beam search"""
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length_pentaly: float = 1.0
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"""Only applies to beam search, i.e. when the beam width is >1.
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Larger values penalize long candidates more heavily, thus preferring
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shorter candidates"""
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writer_api_key: Optional[str] = None
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stop: Optional[List[str]] = None
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"""Sequences when completion generation will stop"""
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base_url: Optional[str] = None
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"""Base url to use, if None decides based on model name."""
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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 exists in environment."""
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writer_api_key = get_from_dict_or_env(
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values, "writer_api_key", "WRITER_API_KEY"
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)
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values["writer_api_key"] = writer_api_key
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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 Writer API."""
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return {
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"tokens_to_generate": self.tokens_to_generate,
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"stop": self.stop,
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"logprobs": self.logprobs,
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"temperature": self.temperature,
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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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"random_seed": self.random_seed,
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"beam_search_diversity_rate": self.beam_search_diversity_rate,
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"beam_width": self.beam_width,
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"length_pentaly": self.length_pentaly,
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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_id": self.model_id}, **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 "writer"
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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"""Call out to Writer's complete 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 = Writer("Tell me a joke.")
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"""
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if self.base_url is not None:
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base_url = self.base_url
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else:
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base_url = (
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"https://api.llm.writer.com/v1/models/{self.model_id}/completions"
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)
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response = requests.post(
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url=base_url,
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headers={
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"Authorization": f"Bearer {self.writer_api_key}",
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"Content-Type": "application/json",
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"Accept": "application/json",
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},
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json={"prompt": prompt, **self._default_params},
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)
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text = response.text
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if stop is not None:
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# I believe this is required since the stop tokens
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# are not enforced by the model parameters
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text = enforce_stop_tokens(text, stop)
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return text
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