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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
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GitHub
249
libs/community/langchain_community/llms/cohere.py
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249
libs/community/langchain_community/llms/cohere.py
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from __future__ import annotations
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import logging
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from typing import Any, Callable, Dict, List, Optional
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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.load.serializable import Serializable
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from langchain_core.pydantic_v1 import Extra, Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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from tenacity import (
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before_sleep_log,
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_exponential,
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)
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from langchain_community.llms.utils import enforce_stop_tokens
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logger = logging.getLogger(__name__)
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def _create_retry_decorator(llm: Cohere) -> Callable[[Any], Any]:
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import cohere
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min_seconds = 4
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max_seconds = 10
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# Wait 2^x * 1 second between each retry starting with
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# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
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return retry(
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reraise=True,
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stop=stop_after_attempt(llm.max_retries),
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wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
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retry=(retry_if_exception_type(cohere.error.CohereError)),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def completion_with_retry(llm: Cohere, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(llm)
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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return llm.client.generate(**kwargs)
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return _completion_with_retry(**kwargs)
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def acompletion_with_retry(llm: Cohere, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(llm)
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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return await llm.async_client.generate(**kwargs)
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return _completion_with_retry(**kwargs)
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class BaseCohere(Serializable):
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"""Base class for Cohere models."""
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client: Any #: :meta private:
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async_client: Any #: :meta private:
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model: Optional[str] = Field(default=None)
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"""Model name to use."""
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temperature: float = 0.75
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"""A non-negative float that tunes the degree of randomness in generation."""
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cohere_api_key: Optional[str] = None
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stop: Optional[List[str]] = None
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streaming: bool = Field(default=False)
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"""Whether to stream the results."""
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user_agent: str = "langchain"
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"""Identifier for the application making the request."""
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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 and python package exists in environment."""
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try:
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import cohere
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except ImportError:
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raise ImportError(
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"Could not import cohere python package. "
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"Please install it with `pip install cohere`."
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)
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else:
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cohere_api_key = get_from_dict_or_env(
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values, "cohere_api_key", "COHERE_API_KEY"
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)
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client_name = values["user_agent"]
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values["client"] = cohere.Client(cohere_api_key, client_name=client_name)
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values["async_client"] = cohere.AsyncClient(
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cohere_api_key, client_name=client_name
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)
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return values
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class Cohere(LLM, BaseCohere):
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"""Cohere large language models.
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To use, you should have the ``cohere`` python package installed, and the
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environment variable ``COHERE_API_KEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain_community.llms import Cohere
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cohere = Cohere(model="gptd-instruct-tft", cohere_api_key="my-api-key")
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"""
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max_tokens: int = 256
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"""Denotes the number of tokens to predict per generation."""
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k: int = 0
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"""Number of most likely tokens to consider at each step."""
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p: int = 1
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"""Total probability mass of tokens to consider at each step."""
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frequency_penalty: float = 0.0
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"""Penalizes repeated tokens according to frequency. Between 0 and 1."""
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presence_penalty: float = 0.0
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"""Penalizes repeated tokens. Between 0 and 1."""
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truncate: Optional[str] = None
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"""Specify how the client handles inputs longer than the maximum token
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length: Truncate from START, END or NONE"""
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max_retries: int = 10
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"""Maximum number of retries to make when generating."""
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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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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling Cohere API."""
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return {
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"k": self.k,
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"p": self.p,
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"frequency_penalty": self.frequency_penalty,
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"presence_penalty": self.presence_penalty,
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"truncate": self.truncate,
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}
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"cohere_api_key": "COHERE_API_KEY"}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model": self.model}, **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 "cohere"
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def _invocation_params(self, stop: Optional[List[str]], **kwargs: Any) -> dict:
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params = self._default_params
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if self.stop is not None and stop is not None:
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raise ValueError("`stop` found in both the input and default params.")
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elif self.stop is not None:
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params["stop_sequences"] = self.stop
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else:
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params["stop_sequences"] = stop
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return {**params, **kwargs}
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def _process_response(self, response: Any, stop: Optional[List[str]]) -> str:
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text = response.generations[0].text
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# If stop tokens are provided, Cohere's endpoint returns them.
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# In order to make this consistent with other endpoints, we strip them.
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if stop:
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text = enforce_stop_tokens(text, stop)
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return text
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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 Cohere's generate 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 = cohere("Tell me a joke.")
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"""
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params = self._invocation_params(stop, **kwargs)
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response = completion_with_retry(
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self, model=self.model, prompt=prompt, **params
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)
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_stop = params.get("stop_sequences")
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return self._process_response(response, _stop)
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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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"""Async call out to Cohere's generate 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 = await cohere("Tell me a joke.")
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"""
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params = self._invocation_params(stop, **kwargs)
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response = await acompletion_with_retry(
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self, model=self.model, prompt=prompt, **params
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)
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_stop = params.get("stop_sequences")
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return self._process_response(response, _stop)
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