"""Wrapper around Together AI's Completion API."""
import logging
import warnings
from typing import Any, Dict, List, Optional
import requests
from aiohttp import ClientSession
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import (
secret_from_env,
)
logger = logging.getLogger(__name__)
[docs]class Together(LLM):
"""LLM models from `Together`.
To use, you'll need an API key which you can find here:
https://api.together.ai/settings/api-keys. This can be passed in as init param
``together_api_key`` or set as environment variable ``TOGETHER_API_KEY``.
Together AI API reference: https://docs.together.ai/reference/completions
Example:
.. code-block:: python
from langchain_together import Together
model = Together(model_name="mistralai/Mixtral-8x7B-Instruct-v0.1")
"""
base_url: str = "https://api.together.xyz/v1/completions"
"""Base completions API URL."""
together_api_key: SecretStr = Field(
alias="api_key",
default_factory=secret_from_env("TOGETHER_API_KEY"),
)
"""Together AI API key.
Automatically read from env variable `TOGETHER_API_KEY` if not provided.
"""
model: str
"""Model name. Available models listed here:
Base Models: https://docs.together.ai/docs/inference-models#language-models
Chat Models: https://docs.together.ai/docs/inference-models#chat-models
"""
temperature: Optional[float] = None
"""Model temperature."""
top_p: Optional[float] = None
"""Used to dynamically adjust the number of choices for each predicted token based
on the cumulative probabilities. A value of 1 will always yield the same
output. A temperature less than 1 favors more correctness and is appropriate
for question answering or summarization. A value greater than 1 introduces more
randomness in the output.
"""
top_k: Optional[int] = None
"""Used to limit the number of choices for the next predicted word or token. It
specifies the maximum number of tokens to consider at each step, based on their
probability of occurrence. This technique helps to speed up the generation
process and can improve the quality of the generated text by focusing on the
most likely options.
"""
max_tokens: Optional[int] = None
"""The maximum number of tokens to generate."""
repetition_penalty: Optional[float] = None
"""A number that controls the diversity of generated text by reducing the
likelihood of repeated sequences. Higher values decrease repetition.
"""
logprobs: Optional[int] = None
"""An integer that specifies how many top token log probabilities are included in
the response for each token generation step.
"""
class Config:
"""Configuration for this pydantic object."""
extra = "forbid"
allow_population_by_field_name = True
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
if values.get("max_tokens") is None:
warnings.warn(
"The completions endpoint, has 'max_tokens' as required argument. "
"The default value is being set to 200 "
"Consider setting this value, when initializing LLM"
)
values["max_tokens"] = 200 # Default Value
return values
@property
def _llm_type(self) -> str:
"""Return type of model."""
return "together"
def _format_output(self, output: dict) -> str:
return output["choices"][0]["text"]
@property
def default_params(self) -> Dict[str, Any]:
"""Return the default parameters for the Together model.
Returns:
A dictionary containing the default parameters.
"""
return {
"model": self.model,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
"max_tokens": self.max_tokens,
"repetition_penalty": self.repetition_penalty,
}
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Together's text generation endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
run_manager: The CallbackManager for LLM run, it's not used at the moment.
kwargs: Additional parameters to pass to the model.
Returns:
The string generated by the model..
"""
headers = {
"Authorization": f"Bearer {self.together_api_key.get_secret_value()}",
"Content-Type": "application/json",
}
stop_to_use = stop[0] if stop and len(stop) == 1 else stop
payload: Dict[str, Any] = {
**self.default_params,
"prompt": prompt,
"stop": stop_to_use,
**kwargs,
}
# filter None values to not pass them to the http payload
payload = {k: v for k, v in payload.items() if v is not None}
response = requests.post(url=self.base_url, json=payload, headers=headers)
if response.status_code >= 500:
raise Exception(f"Together Server: Error {response.status_code}")
elif response.status_code >= 400:
raise ValueError(f"Together received an invalid payload: {response.text}")
elif response.status_code != 200:
raise Exception(
f"Together returned an unexpected response with status "
f"{response.status_code}: {response.text}"
)
data = response.json()
output = self._format_output(data)
return output
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call Together model to get predictions based on the prompt.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
run_manager: The CallbackManager for LLM run, it's not used at the moment.
kwargs: Additional parameters to pass to the model.
Returns:
The string generated by the model.
"""
headers = {
"Authorization": f"Bearer {self.together_api_key.get_secret_value()}",
"Content-Type": "application/json",
}
stop_to_use = stop[0] if stop and len(stop) == 1 else stop
payload: Dict[str, Any] = {
**self.default_params,
"prompt": prompt,
"stop": stop_to_use,
**kwargs,
}
# filter None values to not pass them to the http payload
payload = {k: v for k, v in payload.items() if v is not None}
async with ClientSession() as session:
async with session.post(
self.base_url, json=payload, headers=headers
) as response:
if response.status >= 500:
raise Exception(f"Together Server: Error {response.status}")
elif response.status >= 400:
raise ValueError(
f"Together received an invalid payload: {response.text}"
)
elif response.status != 200:
raise Exception(
f"Together returned an unexpected response with status "
f"{response.status}: {response.text}"
)
response_json = await response.json()
output = self._format_output(response_json)
return output
