mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
265 lines
8.4 KiB
Python
265 lines
8.4 KiB
Python
from __future__ import annotations
|
|
|
|
import logging
|
|
from typing import Any, Callable, Dict, List, Optional
|
|
|
|
from langchain_core._api.deprecation import deprecated
|
|
from langchain_core.callbacks import (
|
|
AsyncCallbackManagerForLLMRun,
|
|
CallbackManagerForLLMRun,
|
|
)
|
|
from langchain_core.language_models.llms import LLM
|
|
from langchain_core.load.serializable import Serializable
|
|
from langchain_core.pydantic_v1 import Field, SecretStr
|
|
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
|
|
from tenacity import (
|
|
before_sleep_log,
|
|
retry,
|
|
retry_if_exception_type,
|
|
stop_after_attempt,
|
|
wait_exponential,
|
|
)
|
|
|
|
from langchain_community.llms.utils import enforce_stop_tokens
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def _create_retry_decorator(max_retries: int) -> Callable[[Any], Any]:
|
|
import cohere
|
|
|
|
# support v4 and v5
|
|
retry_conditions = (
|
|
retry_if_exception_type(cohere.error.CohereError)
|
|
if hasattr(cohere, "error")
|
|
else retry_if_exception_type(Exception)
|
|
)
|
|
|
|
min_seconds = 4
|
|
max_seconds = 10
|
|
# Wait 2^x * 1 second between each retry starting with
|
|
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
|
|
return retry(
|
|
reraise=True,
|
|
stop=stop_after_attempt(max_retries),
|
|
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
|
|
retry=retry_conditions,
|
|
before_sleep=before_sleep_log(logger, logging.WARNING),
|
|
)
|
|
|
|
|
|
def completion_with_retry(llm: Cohere, **kwargs: Any) -> Any:
|
|
"""Use tenacity to retry the completion call."""
|
|
retry_decorator = _create_retry_decorator(llm.max_retries)
|
|
|
|
@retry_decorator
|
|
def _completion_with_retry(**kwargs: Any) -> Any:
|
|
return llm.client.generate(**kwargs)
|
|
|
|
return _completion_with_retry(**kwargs)
|
|
|
|
|
|
def acompletion_with_retry(llm: Cohere, **kwargs: Any) -> Any:
|
|
"""Use tenacity to retry the completion call."""
|
|
retry_decorator = _create_retry_decorator(llm.max_retries)
|
|
|
|
@retry_decorator
|
|
async def _completion_with_retry(**kwargs: Any) -> Any:
|
|
return await llm.async_client.generate(**kwargs)
|
|
|
|
return _completion_with_retry(**kwargs)
|
|
|
|
|
|
@deprecated(
|
|
since="0.0.30", removal="1.0", alternative_import="langchain_cohere.BaseCohere"
|
|
)
|
|
class BaseCohere(Serializable):
|
|
"""Base class for Cohere models."""
|
|
|
|
client: Any #: :meta private:
|
|
async_client: Any #: :meta private:
|
|
model: Optional[str] = Field(default=None)
|
|
"""Model name to use."""
|
|
|
|
temperature: float = 0.75
|
|
"""A non-negative float that tunes the degree of randomness in generation."""
|
|
|
|
cohere_api_key: Optional[SecretStr] = None
|
|
"""Cohere API key. If not provided, will be read from the environment variable."""
|
|
|
|
stop: Optional[List[str]] = None
|
|
|
|
streaming: bool = Field(default=False)
|
|
"""Whether to stream the results."""
|
|
|
|
user_agent: str = "langchain"
|
|
"""Identifier for the application making the request."""
|
|
|
|
@pre_init
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
|
"""Validate that api key and python package exists in environment."""
|
|
try:
|
|
import cohere
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import cohere python package. "
|
|
"Please install it with `pip install cohere`."
|
|
)
|
|
else:
|
|
values["cohere_api_key"] = convert_to_secret_str(
|
|
get_from_dict_or_env(values, "cohere_api_key", "COHERE_API_KEY")
|
|
)
|
|
client_name = values["user_agent"]
|
|
values["client"] = cohere.Client(
|
|
api_key=values["cohere_api_key"].get_secret_value(),
|
|
client_name=client_name,
|
|
)
|
|
values["async_client"] = cohere.AsyncClient(
|
|
api_key=values["cohere_api_key"].get_secret_value(),
|
|
client_name=client_name,
|
|
)
|
|
return values
|
|
|
|
|
|
@deprecated(since="0.1.14", removal="1.0", alternative_import="langchain_cohere.Cohere")
|
|
class Cohere(LLM, BaseCohere):
|
|
"""Cohere large language models.
|
|
|
|
To use, you should have the ``cohere`` python package installed, and the
|
|
environment variable ``COHERE_API_KEY`` set with your API key, or pass
|
|
it as a named parameter to the constructor.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.llms import Cohere
|
|
|
|
cohere = Cohere(model="gptd-instruct-tft", cohere_api_key="my-api-key")
|
|
"""
|
|
|
|
max_tokens: int = 256
|
|
"""Denotes the number of tokens to predict per generation."""
|
|
|
|
k: int = 0
|
|
"""Number of most likely tokens to consider at each step."""
|
|
|
|
p: int = 1
|
|
"""Total probability mass of tokens to consider at each step."""
|
|
|
|
frequency_penalty: float = 0.0
|
|
"""Penalizes repeated tokens according to frequency. Between 0 and 1."""
|
|
|
|
presence_penalty: float = 0.0
|
|
"""Penalizes repeated tokens. Between 0 and 1."""
|
|
|
|
truncate: Optional[str] = None
|
|
"""Specify how the client handles inputs longer than the maximum token
|
|
length: Truncate from START, END or NONE"""
|
|
|
|
max_retries: int = 10
|
|
"""Maximum number of retries to make when generating."""
|
|
|
|
class Config:
|
|
extra = "forbid"
|
|
|
|
@property
|
|
def _default_params(self) -> Dict[str, Any]:
|
|
"""Get the default parameters for calling Cohere API."""
|
|
return {
|
|
"max_tokens": self.max_tokens,
|
|
"temperature": self.temperature,
|
|
"k": self.k,
|
|
"p": self.p,
|
|
"frequency_penalty": self.frequency_penalty,
|
|
"presence_penalty": self.presence_penalty,
|
|
"truncate": self.truncate,
|
|
}
|
|
|
|
@property
|
|
def lc_secrets(self) -> Dict[str, str]:
|
|
return {"cohere_api_key": "COHERE_API_KEY"}
|
|
|
|
@property
|
|
def _identifying_params(self) -> Dict[str, Any]:
|
|
"""Get the identifying parameters."""
|
|
return {**{"model": self.model}, **self._default_params}
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of llm."""
|
|
return "cohere"
|
|
|
|
def _invocation_params(self, stop: Optional[List[str]], **kwargs: Any) -> dict:
|
|
params = self._default_params
|
|
if self.stop is not None and stop is not None:
|
|
raise ValueError("`stop` found in both the input and default params.")
|
|
elif self.stop is not None:
|
|
params["stop_sequences"] = self.stop
|
|
else:
|
|
params["stop_sequences"] = stop
|
|
return {**params, **kwargs}
|
|
|
|
def _process_response(self, response: Any, stop: Optional[List[str]]) -> str:
|
|
text = response.generations[0].text
|
|
# If stop tokens are provided, Cohere's endpoint returns them.
|
|
# In order to make this consistent with other endpoints, we strip them.
|
|
if stop:
|
|
text = enforce_stop_tokens(text, stop)
|
|
return text
|
|
|
|
def _call(
|
|
self,
|
|
prompt: str,
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> str:
|
|
"""Call out to Cohere's generate endpoint.
|
|
|
|
Args:
|
|
prompt: The prompt to pass into the model.
|
|
stop: Optional list of stop words to use when generating.
|
|
|
|
Returns:
|
|
The string generated by the model.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
response = cohere("Tell me a joke.")
|
|
"""
|
|
params = self._invocation_params(stop, **kwargs)
|
|
response = completion_with_retry(
|
|
self, model=self.model, prompt=prompt, **params
|
|
)
|
|
_stop = params.get("stop_sequences")
|
|
return self._process_response(response, _stop)
|
|
|
|
async def _acall(
|
|
self,
|
|
prompt: str,
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> str:
|
|
"""Async call out to Cohere's generate endpoint.
|
|
|
|
Args:
|
|
prompt: The prompt to pass into the model.
|
|
stop: Optional list of stop words to use when generating.
|
|
|
|
Returns:
|
|
The string generated by the model.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
response = await cohere("Tell me a joke.")
|
|
"""
|
|
params = self._invocation_params(stop, **kwargs)
|
|
response = await acompletion_with_retry(
|
|
self, model=self.model, prompt=prompt, **params
|
|
)
|
|
_stop = params.get("stop_sequences")
|
|
return self._process_response(response, _stop)
|