mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
211 lines
7.5 KiB
Python
211 lines
7.5 KiB
Python
"""Wrapper around Together AI's Completion API."""
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import logging
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from typing import Any, Dict, List, Optional
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from aiohttp import ClientSession
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from langchain_core._api.deprecation import deprecated
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.llms import LLM
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from langchain_core.pydantic_v1 import Extra, SecretStr, 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.utilities.requests import Requests
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logger = logging.getLogger(__name__)
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@deprecated(
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since="0.0.12", removal="0.2", alternative_import="langchain_together.Together"
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)
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class Together(LLM):
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"""LLM models from `Together`.
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To use, you'll need an API key which you can find here:
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https://api.together.xyz/settings/api-keys. This can be passed in as init param
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``together_api_key`` or set as environment variable ``TOGETHER_API_KEY``.
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Together AI API reference: https://docs.together.ai/reference/inference
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"""
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base_url: str = "https://api.together.xyz/inference"
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"""Base inference API URL."""
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together_api_key: SecretStr
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"""Together AI API key. Get it here: https://api.together.xyz/settings/api-keys"""
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model: str
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"""Model name. Available models listed here:
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https://docs.together.ai/docs/inference-models
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"""
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temperature: Optional[float] = None
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"""Model temperature."""
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top_p: Optional[float] = None
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"""Used to dynamically adjust the number of choices for each predicted token based
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on the cumulative probabilities. A value of 1 will always yield the same
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output. A temperature less than 1 favors more correctness and is appropriate
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for question answering or summarization. A value greater than 1 introduces more
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randomness in the output.
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"""
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top_k: Optional[int] = None
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"""Used to limit the number of choices for the next predicted word or token. It
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specifies the maximum number of tokens to consider at each step, based on their
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probability of occurrence. This technique helps to speed up the generation
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process and can improve the quality of the generated text by focusing on the
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most likely options.
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"""
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max_tokens: Optional[int] = None
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"""The maximum number of tokens to generate."""
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repetition_penalty: Optional[float] = None
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"""A number that controls the diversity of generated text by reducing the
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likelihood of repeated sequences. Higher values decrease repetition.
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"""
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logprobs: Optional[int] = None
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"""An integer that specifies how many top token log probabilities are included in
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the response for each token generation step.
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"""
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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(pre=True)
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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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values["together_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "together_api_key", "TOGETHER_API_KEY")
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)
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return values
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@property
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def _llm_type(self) -> str:
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"""Return type of model."""
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return "together"
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def _format_output(self, output: dict) -> str:
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return output["output"]["choices"][0]["text"]
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@staticmethod
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def get_user_agent() -> str:
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from langchain_community import __version__
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return f"langchain/{__version__}"
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@property
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def default_params(self) -> Dict[str, Any]:
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return {
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"model": self.model,
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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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"max_tokens": self.max_tokens,
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"repetition_penalty": self.repetition_penalty,
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}
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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 Together's text generation endpoint.
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Args:
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prompt: The prompt to pass into the model.
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Returns:
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The string generated by the model..
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"""
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headers = {
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"Authorization": f"Bearer {self.together_api_key.get_secret_value()}",
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"Content-Type": "application/json",
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}
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stop_to_use = stop[0] if stop and len(stop) == 1 else stop
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payload: Dict[str, Any] = {
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**self.default_params,
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"prompt": prompt,
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"stop": stop_to_use,
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**kwargs,
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}
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# filter None values to not pass them to the http payload
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payload = {k: v for k, v in payload.items() if v is not None}
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request = Requests(headers=headers)
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response = request.post(url=self.base_url, data=payload)
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if response.status_code >= 500:
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raise Exception(f"Together Server: Error {response.status_code}")
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elif response.status_code >= 400:
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raise ValueError(f"Together received an invalid payload: {response.text}")
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elif response.status_code != 200:
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raise Exception(
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f"Together returned an unexpected response with status "
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f"{response.status_code}: {response.text}"
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)
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data = response.json()
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if data.get("status") != "finished":
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err_msg = data.get("error", "Undefined Error")
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raise Exception(err_msg)
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output = self._format_output(data)
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return output
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async def _acall(
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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[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Call Together model to get predictions based on the prompt.
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Args:
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prompt: The prompt to pass into the model.
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Returns:
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The string generated by the model.
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"""
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headers = {
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"Authorization": f"Bearer {self.together_api_key.get_secret_value()}",
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"Content-Type": "application/json",
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}
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stop_to_use = stop[0] if stop and len(stop) == 1 else stop
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payload: Dict[str, Any] = {
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**self.default_params,
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"prompt": prompt,
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"stop": stop_to_use,
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**kwargs,
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}
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# filter None values to not pass them to the http payload
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payload = {k: v for k, v in payload.items() if v is not None}
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async with ClientSession() as session:
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async with session.post(
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self.base_url, json=payload, headers=headers
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) as response:
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if response.status >= 500:
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raise Exception(f"Together Server: Error {response.status}")
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elif response.status >= 400:
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raise ValueError(
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f"Together received an invalid payload: {response.text}"
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)
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elif response.status != 200:
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raise Exception(
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f"Together returned an unexpected response with status "
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f"{response.status}: {response.text}"
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)
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response_json = await response.json()
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if response_json.get("status") != "finished":
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err_msg = response_json.get("error", "Undefined Error")
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raise Exception(err_msg)
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output = self._format_output(response_json)
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return output
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