Kye
1 year ago
parent
6a352053fc
commit
680e997974
@ -1,99 +1,304 @@
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#kye
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#aug 8, 11:51
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import warnings
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import logging
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import sys
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from typing import (
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Any,
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Collection,
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Dict,
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Field,
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List,
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Literal,
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Optional,
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Tuple,
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Union,
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AbstractSet
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)
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from simpleaichat import AIChat, AsyncAIChat
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import asyncio
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import openai
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import tiktoken
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import os
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def get_from_dict_or_env(
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data: Dict[str, Any], key: str, env_key: str, 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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class OpenAI:
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def __init__(self,
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api_key=None,
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system=None,
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console=True,
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model=None,
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params=None,
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save_messages=True):
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self.api_key = api_key or self.fetch_api_key()
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self.system = system or "You are a helpful assistant"
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try:
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self.ai = AIChat(api_key=self.api_key,
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system=self.system,
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console=self.console,
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model=self.model,
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params=self.params,
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save_messages=self.save_messages)
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self.async_ai = AsyncAIChat(
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api_key=self.api_key,
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system=self.system,
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console=self.console,
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model=self.model,
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params=self.params,
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save_messages=self.save_messages
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)
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except Exception as error:
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raise ValueError(f"Failed to initialize the chat with error: {error}, check inputs and input types")
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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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def __call__(self, message, **kwargs):
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try:
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return self.ai(message, **kwargs)
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except Exception as error:
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print(f"Error in OpenAI, {error}")
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def generate(self, message, **kwargs):
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try:
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return self.ai(message, **kwargs)
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except Exception as error:
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print(f"Error in OpenAI, {error}")
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async def generate_async(self, message, **kwargs):
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try:
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return await self.async_ai(message, **kwargs)
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except Exception as error:
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raise Exception(f"Error in asynchronous OpenAI Call, {error}")
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def initialize_chat(self, ids):
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for id in ids:
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try:
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self.async_ai.new_session(api_key=self.api_key, id=id)
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except Exception as error:
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raise ValueError(f"Failed to initialize session for ID {id} with error: {error}")
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async def ask_multiple(self, ids, question_template):
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class OpenAIChat(BaseLLM):
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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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self.initialize_chat(ids)
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tasks = [self.async_ai(question_template.format(id=id), id=id) for id in ids]
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return await asyncio.gather(*tasks)
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except Exception as error:
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raise Exception(f"Error in ask_multiple: method: {error}")
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async def stream_multiple(self, ids, question_template):
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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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self.initialize_chat(ids)
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async def stream_id(id):
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async for chunk in await self.async_ai.stream(question_template.format(id=id), id=id):
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response = chunk["response"]
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return response
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tasks = [stream_id(id) for id in ids]
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return await asyncio.gather(*tasks)
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except Exception as error:
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raise Exception(f"Error in stream_multiple method: {error}")
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def fetch_api_key(self):
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pass
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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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#usage
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#from swarms import OpenAI()
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#chat = OpenAI()
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#response = chat.generate("Hello World")
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#print(response)
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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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#async
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# async_responses = asyncio.run(chat.ask_multiple(['id1', 'id2'], "How is {id}"))
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# print(async_responses)
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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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