Kye
1 year ago
parent
6a1bca74b7
commit
6a89e9165d
@ -1,318 +1,318 @@
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from __future__ import annotations
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import logging
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import sys
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import warnings
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from typing import (
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AbstractSet,
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Any,
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AsyncIterator,
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Collection,
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Dict,
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Iterator,
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List,
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Literal,
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Mapping,
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Optional,
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Tuple,
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Union,
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)
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.pydantic_v1 import Field, root_validator
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from langchain.schema import Generation, LLMResult
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from langchain.schema.output import GenerationChunk
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from langchain.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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import os
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def get_from_dict_or_env(
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data: Dict[str, Any],
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key: str,
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env_key: str,
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default: Optional[str] = None
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) -> str:
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"""Get a value from a dictionary or an environment variable."""
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if key in data and data[key]:
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return data[key]
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else:
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return get_from_env(key, env_key, default=default)
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def get_from_env(key: str, env_key: str, default: Optional[str] = None) -> str:
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"""Get a value from a dictionary or an environment variable."""
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if env_key in os.environ and os.environ[env_key]:
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return os.environ[env_key]
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elif default is not None:
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return default
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else:
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raise ValueError(
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f"Did not find {key}, please add an environment variable"
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f" `{env_key}` which contains it, or pass"
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f" `{key}` as a named parameter."
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)
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class OpenAIChat:
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"""OpenAI Chat large language models.
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To use, you should have the ``openai`` python package installed, and the
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environment variable ``OPENAI_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the openai.create call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain.llms import OpenAIChat
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openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
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"""
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client: Any #: :meta private:
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model_name: str = "gpt-3.5-turbo"
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"""Model name to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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openai_api_key: Optional[str] = None
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openai_api_base: Optional[str] = None
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# to support explicit proxy for OpenAI
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openai_proxy: Optional[str] = None
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max_retries: int = 6
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"""Maximum number of retries to make when generating."""
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prefix_messages: List = Field(default_factory=list)
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"""Series of messages for Chat input."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
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"""Set of special tokens that are allowed。"""
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disallowed_special: Union[Literal["all"], Collection[str]] = "all"
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"""Set of special tokens that are not allowed。"""
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = {field.alias for field in cls.__fields__.values()}
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name not in all_required_field_names:
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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extra[field_name] = values.pop(field_name)
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values["model_kwargs"] = extra
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return values
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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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openai_api_key = get_from_dict_or_env(
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values, "openai_api_key", "OPENAI_API_KEY"
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)
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openai_api_base = get_from_dict_or_env(
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values,
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"openai_api_base",
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"OPENAI_API_BASE",
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default="",
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)
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openai_proxy = get_from_dict_or_env(
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values,
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"openai_proxy",
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"OPENAI_PROXY",
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default="",
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)
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openai_organization = get_from_dict_or_env(
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values, "openai_organization", "OPENAI_ORGANIZATION", default=""
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)
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try:
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import openai
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openai.api_key = openai_api_key
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if openai_api_base:
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openai.api_base = openai_api_base
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if openai_organization:
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openai.organization = openai_organization
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if openai_proxy:
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openai.proxy = {"http": openai_proxy, "https": openai_proxy} # type: ignore[assignment] # noqa: E501
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except ImportError:
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raise ImportError(
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"Could not import openai python package. "
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"Please install it with `pip install openai`."
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)
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try:
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values["client"] = openai.ChatCompletion
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except AttributeError:
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raise ValueError(
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"`openai` has no `ChatCompletion` attribute, this is likely "
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"due to an old version of the openai package. Try upgrading it "
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"with `pip install --upgrade openai`."
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)
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warnings.warn(
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"You are trying to use a chat model. This way of initializing it is "
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"no longer supported. Instead, please use: "
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"`from langchain.chat_models import ChatOpenAI`"
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)
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return values
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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 OpenAI API."""
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return self.model_kwargs
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def _get_chat_params(
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self, prompts: List[str], stop: Optional[List[str]] = None
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) -> Tuple:
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if len(prompts) > 1:
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raise ValueError(
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f"OpenAIChat currently only supports single prompt, got {prompts}"
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)
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messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}]
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params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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if params.get("max_tokens") == -1:
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# for ChatGPT api, omitting max_tokens is equivalent to having no limit
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del params["max_tokens"]
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return messages, params
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def _stream(
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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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) -> Iterator[GenerationChunk]:
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messages, params = self._get_chat_params([prompt], stop)
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params = {**params, **kwargs, "stream": True}
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for stream_resp in completion_with_retry(
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self, messages=messages, run_manager=run_manager, **params
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):
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token = stream_resp["choices"][0]["delta"].get("content", "")
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chunk = GenerationChunk(text=token)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(token, chunk=chunk)
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async def _astream(
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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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) -> AsyncIterator[GenerationChunk]:
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messages, params = self._get_chat_params([prompt], stop)
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params = {**params, **kwargs, "stream": True}
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async for stream_resp in await acompletion_with_retry(
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self, messages=messages, run_manager=run_manager, **params
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):
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token = stream_resp["choices"][0]["delta"].get("content", "")
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chunk = GenerationChunk(text=token)
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yield chunk
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if run_manager:
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await run_manager.on_llm_new_token(token, chunk=chunk)
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def _generate(
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self,
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prompts: List[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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) -> LLMResult:
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if self.streaming:
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generation: Optional[GenerationChunk] = None
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for chunk in self._stream(prompts[0], stop, run_manager, **kwargs):
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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return LLMResult(generations=[[generation]])
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messages, params = self._get_chat_params(prompts, stop)
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params = {**params, **kwargs}
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full_response = completion_with_retry(
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self, messages=messages, run_manager=run_manager, **params
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)
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llm_output = {
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"token_usage": full_response["usage"],
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"model_name": self.model_name,
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}
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return LLMResult(
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generations=[
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[Generation(text=full_response["choices"][0]["message"]["content"])]
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],
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llm_output=llm_output,
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)
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async def _agenerate(
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self,
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prompts: List[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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) -> LLMResult:
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if self.streaming:
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generation: Optional[GenerationChunk] = None
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async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs):
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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return LLMResult(generations=[[generation]])
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messages, params = self._get_chat_params(prompts, stop)
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params = {**params, **kwargs}
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full_response = await acompletion_with_retry(
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self, messages=messages, run_manager=run_manager, **params
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)
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llm_output = {
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"token_usage": full_response["usage"],
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"model_name": self.model_name,
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}
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return LLMResult(
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generations=[
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[Generation(text=full_response["choices"][0]["message"]["content"])]
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],
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llm_output=llm_output,
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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_name": self.model_name}, **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 "openai-chat"
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def get_token_ids(self, text: str) -> List[int]:
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"""Get the token IDs using the tiktoken package."""
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# tiktoken NOT supported for Python < 3.8
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if sys.version_info[1] < 8:
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return super().get_token_ids(text)
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try:
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import tiktoken
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except ImportError:
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raise ImportError(
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"Could not import tiktoken python package. "
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"This is needed in order to calculate get_num_tokens. "
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"Please install it with `pip install tiktoken`."
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)
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enc = tiktoken.encoding_for_model(self.model_name)
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return enc.encode(
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text,
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allowed_special=self.allowed_special,
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disallowed_special=self.disallowed_special,
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)
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# from __future__ import annotations
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# import logging
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# import sys
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# import warnings
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# from typing import (
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# AbstractSet,
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# Any,
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# AsyncIterator,
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# Collection,
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# Dict,
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# Iterator,
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# List,
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# Literal,
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# Mapping,
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# Optional,
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# Tuple,
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# Union,
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# )
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# from langchain.callbacks.manager import (
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# AsyncCallbackManagerForLLMRun,
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# CallbackManagerForLLMRun,
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# )
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# from langchain.pydantic_v1 import Field, root_validator
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# from langchain.schema import Generation, LLMResult
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# from langchain.schema.output import GenerationChunk
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# from langchain.utils import get_from_dict_or_env
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# logger = logging.getLogger(__name__)
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# import os
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# def get_from_dict_or_env(
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# data: Dict[str, Any],
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# key: str,
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# env_key: str,
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# default: Optional[str] = None
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# ) -> str:
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# """Get a value from a dictionary or an environment variable."""
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# if key in data and data[key]:
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# return data[key]
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# else:
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# return get_from_env(key, env_key, default=default)
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# def get_from_env(key: str, env_key: str, default: Optional[str] = None) -> str:
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# """Get a value from a dictionary or an environment variable."""
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# if env_key in os.environ and os.environ[env_key]:
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# return os.environ[env_key]
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# elif default is not None:
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# return default
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# else:
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# raise ValueError(
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# f"Did not find {key}, please add an environment variable"
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# f" `{env_key}` which contains it, or pass"
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# f" `{key}` as a named parameter."
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# )
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# class OpenAIChat:
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# """OpenAI Chat large language models.
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# To use, you should have the ``openai`` python package installed, and the
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# environment variable ``OPENAI_API_KEY`` set with your API key.
|
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# Any parameters that are valid to be passed to the openai.create call can be passed
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# in, even if not explicitly saved on this class.
|
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# Example:
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# .. code-block:: python
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# from langchain.llms import OpenAIChat
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# openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
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# """
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# client: Any #: :meta private:
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# model_name: str = "gpt-3.5-turbo"
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# """Model name to use."""
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# model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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# """Holds any model parameters valid for `create` call not explicitly specified."""
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# openai_api_key: Optional[str] = None
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# openai_api_base: Optional[str] = None
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# # to support explicit proxy for OpenAI
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# openai_proxy: Optional[str] = None
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# max_retries: int = 6
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# """Maximum number of retries to make when generating."""
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# prefix_messages: List = Field(default_factory=list)
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# """Series of messages for Chat input."""
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# streaming: bool = False
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# """Whether to stream the results or not."""
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# allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
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# """Set of special tokens that are allowed。"""
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# disallowed_special: Union[Literal["all"], Collection[str]] = "all"
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# """Set of special tokens that are not allowed。"""
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# @root_validator(pre=True)
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# def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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# """Build extra kwargs from additional params that were passed in."""
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# all_required_field_names = {field.alias for field in cls.__fields__.values()}
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# extra = values.get("model_kwargs", {})
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# for field_name in list(values):
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# if field_name not in all_required_field_names:
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# if field_name in extra:
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# raise ValueError(f"Found {field_name} supplied twice.")
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# extra[field_name] = values.pop(field_name)
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# values["model_kwargs"] = extra
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# return values
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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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# openai_api_key = get_from_dict_or_env(
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# values, "openai_api_key", "OPENAI_API_KEY"
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# )
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# openai_api_base = get_from_dict_or_env(
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# values,
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# "openai_api_base",
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# "OPENAI_API_BASE",
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# default="",
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# )
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# openai_proxy = get_from_dict_or_env(
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# values,
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# "openai_proxy",
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# "OPENAI_PROXY",
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# default="",
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# )
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# openai_organization = get_from_dict_or_env(
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# values, "openai_organization", "OPENAI_ORGANIZATION", default=""
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# )
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# try:
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# import openai
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# openai.api_key = openai_api_key
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# if openai_api_base:
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# openai.api_base = openai_api_base
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# if openai_organization:
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# openai.organization = openai_organization
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# if openai_proxy:
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# openai.proxy = {"http": openai_proxy, "https": openai_proxy} # type: ignore[assignment] # noqa: E501
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# except ImportError:
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# raise ImportError(
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# "Could not import openai python package. "
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||||
# "Please install it with `pip install openai`."
|
||||
# )
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# try:
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# values["client"] = openai.ChatCompletion
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# except AttributeError:
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||||
# raise ValueError(
|
||||
# "`openai` has no `ChatCompletion` attribute, this is likely "
|
||||
# "due to an old version of the openai package. Try upgrading it "
|
||||
# "with `pip install --upgrade openai`."
|
||||
# )
|
||||
# warnings.warn(
|
||||
# "You are trying to use a chat model. This way of initializing it is "
|
||||
# "no longer supported. Instead, please use: "
|
||||
# "`from langchain.chat_models import ChatOpenAI`"
|
||||
# )
|
||||
# return values
|
||||
|
||||
# @property
|
||||
# def _default_params(self) -> Dict[str, Any]:
|
||||
# """Get the default parameters for calling OpenAI API."""
|
||||
# return self.model_kwargs
|
||||
|
||||
# def _get_chat_params(
|
||||
# self, prompts: List[str], stop: Optional[List[str]] = None
|
||||
# ) -> Tuple:
|
||||
# if len(prompts) > 1:
|
||||
# raise ValueError(
|
||||
# f"OpenAIChat currently only supports single prompt, got {prompts}"
|
||||
# )
|
||||
# messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}]
|
||||
# params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
|
||||
# if stop is not None:
|
||||
# if "stop" in params:
|
||||
# raise ValueError("`stop` found in both the input and default params.")
|
||||
# params["stop"] = stop
|
||||
# if params.get("max_tokens") == -1:
|
||||
# # for ChatGPT api, omitting max_tokens is equivalent to having no limit
|
||||
# del params["max_tokens"]
|
||||
# return messages, params
|
||||
|
||||
# def _stream(
|
||||
# self,
|
||||
# prompt: str,
|
||||
# stop: Optional[List[str]] = None,
|
||||
# run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
# **kwargs: Any,
|
||||
# ) -> Iterator[GenerationChunk]:
|
||||
# messages, params = self._get_chat_params([prompt], stop)
|
||||
# params = {**params, **kwargs, "stream": True}
|
||||
# for stream_resp in completion_with_retry(
|
||||
# self, messages=messages, run_manager=run_manager, **params
|
||||
# ):
|
||||
# token = stream_resp["choices"][0]["delta"].get("content", "")
|
||||
# chunk = GenerationChunk(text=token)
|
||||
# yield chunk
|
||||
# if run_manager:
|
||||
# run_manager.on_llm_new_token(token, chunk=chunk)
|
||||
|
||||
# async def _astream(
|
||||
# self,
|
||||
# prompt: str,
|
||||
# stop: Optional[List[str]] = None,
|
||||
# run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
# **kwargs: Any,
|
||||
# ) -> AsyncIterator[GenerationChunk]:
|
||||
# messages, params = self._get_chat_params([prompt], stop)
|
||||
# params = {**params, **kwargs, "stream": True}
|
||||
# async for stream_resp in await acompletion_with_retry(
|
||||
# self, messages=messages, run_manager=run_manager, **params
|
||||
# ):
|
||||
# token = stream_resp["choices"][0]["delta"].get("content", "")
|
||||
# chunk = GenerationChunk(text=token)
|
||||
# yield chunk
|
||||
# if run_manager:
|
||||
# await run_manager.on_llm_new_token(token, chunk=chunk)
|
||||
|
||||
# def _generate(
|
||||
# self,
|
||||
# prompts: List[str],
|
||||
# stop: Optional[List[str]] = None,
|
||||
# run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
# **kwargs: Any,
|
||||
# ) -> LLMResult:
|
||||
# if self.streaming:
|
||||
# generation: Optional[GenerationChunk] = None
|
||||
# for chunk in self._stream(prompts[0], stop, run_manager, **kwargs):
|
||||
# if generation is None:
|
||||
# generation = chunk
|
||||
# else:
|
||||
# generation += chunk
|
||||
# assert generation is not None
|
||||
# return LLMResult(generations=[[generation]])
|
||||
|
||||
# messages, params = self._get_chat_params(prompts, stop)
|
||||
# params = {**params, **kwargs}
|
||||
# full_response = completion_with_retry(
|
||||
# self, messages=messages, run_manager=run_manager, **params
|
||||
# )
|
||||
# llm_output = {
|
||||
# "token_usage": full_response["usage"],
|
||||
# "model_name": self.model_name,
|
||||
# }
|
||||
# return LLMResult(
|
||||
# generations=[
|
||||
# [Generation(text=full_response["choices"][0]["message"]["content"])]
|
||||
# ],
|
||||
# llm_output=llm_output,
|
||||
# )
|
||||
|
||||
# async def _agenerate(
|
||||
# self,
|
||||
# prompts: List[str],
|
||||
# stop: Optional[List[str]] = None,
|
||||
# run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
# **kwargs: Any,
|
||||
# ) -> LLMResult:
|
||||
# if self.streaming:
|
||||
# generation: Optional[GenerationChunk] = None
|
||||
# async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs):
|
||||
# if generation is None:
|
||||
# generation = chunk
|
||||
# else:
|
||||
# generation += chunk
|
||||
# assert generation is not None
|
||||
# return LLMResult(generations=[[generation]])
|
||||
|
||||
# messages, params = self._get_chat_params(prompts, stop)
|
||||
# params = {**params, **kwargs}
|
||||
# full_response = await acompletion_with_retry(
|
||||
# self, messages=messages, run_manager=run_manager, **params
|
||||
# )
|
||||
# llm_output = {
|
||||
# "token_usage": full_response["usage"],
|
||||
# "model_name": self.model_name,
|
||||
# }
|
||||
# return LLMResult(
|
||||
# generations=[
|
||||
# [Generation(text=full_response["choices"][0]["message"]["content"])]
|
||||
# ],
|
||||
# llm_output=llm_output,
|
||||
# )
|
||||
|
||||
# @property
|
||||
# def _identifying_params(self) -> Mapping[str, Any]:
|
||||
# """Get the identifying parameters."""
|
||||
# return {**{"model_name": self.model_name}, **self._default_params}
|
||||
|
||||
# @property
|
||||
# def _llm_type(self) -> str:
|
||||
# """Return type of llm."""
|
||||
# return "openai-chat"
|
||||
|
||||
# def get_token_ids(self, text: str) -> List[int]:
|
||||
# """Get the token IDs using the tiktoken package."""
|
||||
# # tiktoken NOT supported for Python < 3.8
|
||||
# if sys.version_info[1] < 8:
|
||||
# return super().get_token_ids(text)
|
||||
# try:
|
||||
# import tiktoken
|
||||
# except ImportError:
|
||||
# raise ImportError(
|
||||
# "Could not import tiktoken python package. "
|
||||
# "This is needed in order to calculate get_num_tokens. "
|
||||
# "Please install it with `pip install tiktoken`."
|
||||
# )
|
||||
|
||||
# enc = tiktoken.encoding_for_model(self.model_name)
|
||||
# return enc.encode(
|
||||
# text,
|
||||
# allowed_special=self.allowed_special,
|
||||
# disallowed_special=self.disallowed_special,
|
||||
# )
|
||||
Loading…
Reference in new issue