Kye
1 year ago
parent
daabe8612e
commit
b91a5639d4
@ -1,559 +1,29 @@
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from typing import (
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TYPE_CHECKING,
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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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 pydantic import Field
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from swarm.utils.logger import logger
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######### helpers
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if TYPE_CHECKING:
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import tiktoken
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def _import_tiktoken() -> Any:
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try:
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import tiktoken
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except ImportError:
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raise ValueError(
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"Could not import tiktoken python package. "
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"This is needed in order to calculate get_token_ids. "
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"Please install it with `pip install tiktoken`."
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)
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return tiktoken
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, FunctionMessage):
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.name,
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}
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else:
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raise ValueError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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class BaseMessage:
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"""Base message class."""
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class HumanMessage(BaseMessage):
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"""Human message class."""
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def __init__(self, content: str):
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self.role = "user"
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self.content = content
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class AIMessage(BaseMessage):
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"""AI message class."""
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def __init__(self, content: str, additional_kwargs: Optional[Dict[str, Any]] = None):
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self.role = "assistant"
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self.content = content
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self.additional_kwargs = additional_kwargs or {}
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class SystemMessage(BaseMessage):
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"""System message class."""
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def __init__(self, content: str):
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self.role = "system"
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self.content = content
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class FunctionMessage(BaseMessage):
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"""Function message class."""
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def __init__(self, content: str, name: str):
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self.role = "function"
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self.content = content
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self.name = name
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class ChatMessage(BaseMessage):
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"""Chat message class."""
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def __init__(self, content: str, role: str):
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self.role = role
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self.content = content
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class BaseMessageChunk:
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"""Base message chunk class."""
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class HumanMessageChunk(BaseMessageChunk):
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"""Human message chunk class."""
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def __init__(self, content: str):
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self.role = "user"
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self.content = content
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class AIMessageChunk(BaseMessageChunk):
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"""AI message chunk class."""
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def __init__(self, content: str, additional_kwargs: Optional[Dict[str, Any]] = None):
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self.role = "assistant"
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self.content = content
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self.additional_kwargs = additional_kwargs or {}
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class SystemMessageChunk(BaseMessageChunk):
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"""System message chunk class."""
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def __init__(self, content: str):
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self.role = "system"
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self.content = content
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class FunctionMessageChunk(BaseMessageChunk):
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"""Function message chunk class."""
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def __init__(self, content: str, name: str):
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self.role = "function"
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self.content = content
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self.name = name
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class ChatMessageChunk(BaseMessageChunk):
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"""Chat message chunk class."""
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def __init__(self, content: str, role: str):
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self.role = role
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self.content = content
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def convert_openai_messages(messages: List[dict]) -> List[BaseMessage]:
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"""Convert dictionaries representing OpenAI messages to LangChain format.
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Args:
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messages: List of dictionaries representing OpenAI messages
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Returns:
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List of LangChain BaseMessage objects.
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"""
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converted_messages = []
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for m in messages:
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role = m.get("role")
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content = m.get("content", "")
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if m.get("function_call"):
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additional_kwargs = {"function_call": dict(m["function_call"])}
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else:
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additional_kwargs = {}
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if role == "user":
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converted_messages.append(HumanMessage(content=content))
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elif role == "assistant":
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converted_messages.append(AIMessage(content=content, additional_kwargs=additional_kwargs))
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elif role == "system":
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converted_messages.append(SystemMessage(content=content))
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elif role == "function":
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converted_messages.append(FunctionMessage(content=content, name=m["name"]))
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else:
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converted_messages.append(ChatMessage(content=content, role=role))
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return converted_messages
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class ChatGenerationChunk:
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"""Chat generation chunk class."""
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def __init__(self, message: BaseMessageChunk):
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self.message = message
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def __add__(self, other: "ChatGenerationChunk") -> "ChatGenerationChunk":
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if isinstance(self.message, AIMessageChunk) and isinstance(other.message, AIMessageChunk):
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combined_kwargs = {
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**self.message.additional_kwargs,
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**other.message.additional_kwargs,
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}
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return ChatGenerationChunk(
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AIMessageChunk(content=self.message.content + other.message.content, additional_kwargs=combined_kwargs)
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)
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return ChatGenerationChunk(BaseMessageChunk(content=self.message.content + other.message.content))
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@property
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def content(self) -> str:
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return self.message.content
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@property
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def additional_kwargs(self) -> Dict[str, Any]:
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return getattr(self.message, "additional_kwargs", {})
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class ChatResult:
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"""Chat result class."""
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def __init__(self, generations: List[ChatGenerationChunk]):
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self.generations = generations
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class BaseChatModel:
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"""Base chat model class."""
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def _convert_delta_to_message_chunk(self, _dict: Mapping[str, Any], default_class: type[BaseMessageChunk]) -> BaseMessageChunk:
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role = _dict.get("role")
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content = _dict.get("content") or ""
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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additional_kwargs = {}
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"])
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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def _convert_dict_to_message(self, _dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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content = _dict.get("content", "") or ""
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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additional_kwargs = {}
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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elif role == "function":
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return FunctionMessage(content=_dict["content"], name=_dict["name"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def completion_with_retry(self, run_manager: Optional[Callable] = None, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = self._create_retry_decorator(self, run_manager=run_manager)
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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return self.client.create(**kwargs)
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return _completion_with_retry(**kwargs)
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def _create_retry_decorator(
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self,
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llm: "ChatOpenAI",
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run_manager: Optional[Callable] = None,
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) -> Callable[[Any], Any]:
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import openai
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errors = [
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openai.error.Timeout,
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openai.error.APIError,
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openai.error.APIConnectionError,
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openai.error.RateLimitError,
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openai.error.ServiceUnavailableError,
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]
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return create_base_retry_decorator(
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error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
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)
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params = self._client_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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message_dicts = [_convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
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overall_token_usage: dict = {}
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for output in llm_outputs:
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if output is None:
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# Happens in streaming
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continue
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token_usage = output["token_usage"]
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for k, v in token_usage.items():
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if k in overall_token_usage:
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overall_token_usage[k] += v
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else:
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overall_token_usage[k] = v
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return {"token_usage": overall_token_usage, "model_name": self.model_name}
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class ChatOpenAI(BaseChatModel):
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"""Wrapper around 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.chat_models import ChatOpenAI
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openai = ChatOpenAI(model_name="gpt-3.5-turbo")
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"""
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def __init__(
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self,
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model_name: str = "gpt-3.5-turbo",
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temperature: float = 0.7,
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model_kwargs: Optional[Dict[str, Any]] = None,
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openai_api_key: Optional[str] = None,
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openai_api_base: Optional[str] = None,
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openai_organization: Optional[str] = None,
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openai_proxy: Optional[str] = None,
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request_timeout: Optional[Union[float, Tuple[float, float]]] = None,
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max_retries: int = 6,
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streaming: bool = False,
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n: int = 1,
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max_tokens: Optional[int] = None,
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tiktoken_model_name: Optional[str] = None,
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):
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self.model_name = model_name
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self.temperature = temperature
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self.model_kwargs = model_kwargs or {}
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self.openai_api_key = openai_api_key
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self.openai_api_base = openai_api_base
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self.openai_organization = openai_organization
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self.openai_proxy = openai_proxy
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self.request_timeout = request_timeout
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self.max_retries = max_retries
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self.streaming = streaming
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self.n = n
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self.max_tokens = max_tokens
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self.tiktoken_model_name = tiktoken_model_name
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"openai_api_key": "OPENAI_API_KEY"}
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@property
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def lc_serializable(self) -> bool:
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return True
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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 {
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"model": self.model_name,
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"request_timeout": self.request_timeout,
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"max_tokens": self.max_tokens,
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"stream": self.streaming,
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"n": self.n,
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"temperature": self.temperature,
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**self.model_kwargs,
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}
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@property
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def _client_params(self) -> Dict[str, Any]:
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"""Get the parameters used for the openai client."""
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openai_creds: Dict[str, Any] = {
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"api_key": self.openai_api_key,
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"api_base": self.openai_api_base,
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"organization": self.openai_organization,
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"model": self.model_name,
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}
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if self.openai_proxy:
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import openai
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openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}
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return {**self._default_params, **openai_creds}
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def _get_invocation_params(
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self, stop: Optional[List[str]] = None, **kwargs: Any
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) -> Dict[str, Any]:
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"""Get the parameters used to invoke the model."""
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return {
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"model": self.model_name,
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**super()._get_invocation_params(stop=stop),
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**self._default_params,
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**kwargs,
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}
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "openai-chat"
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def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
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tiktoken_ = _import_tiktoken()
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if self.tiktoken_model_name is not None:
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model = self.tiktoken_model_name
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else:
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model = self.model_name
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if model == "gpt-3.5-turbo":
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# gpt-3.5-turbo may change over time.
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# Returning num tokens assuming gpt-3.5-turbo-0301.
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model = "gpt-3.5-turbo-0301"
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elif model == "gpt-4":
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# gpt-4 may change over time.
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# Returning num tokens assuming gpt-4-0314.
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model = "gpt-4-0314"
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# Returns the number of tokens used by a list of messages.
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from simpleaichat import AIChat
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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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self.ai = AIChat(api_key=self.api_key, system=self.system, console=console, model=model, params=params, save_messages=save_messages)
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def generate(self, message, **kwargs):
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try:
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encoding = tiktoken_.encoding_for_model(model)
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except KeyError:
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logger.warning("Warning: model not found. Using cl100k_base encoding.")
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model = "cl100k_base"
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encoding = tiktoken_.get_encoding(model)
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return model, encoding
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def get_token_ids(self, text: str) -> List[int]:
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"""Get the tokens present in the text with tiktoken package."""
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# tiktoken NOT supported for Python 3.7 or below
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if sys.version_info[1] <= 7:
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return super().get_token_ids(text)
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_, encoding_model = self._get_encoding_model()
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return encoding_model.encode(text)
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def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
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"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
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Official documentation: https://github.com/openai/openai-cookbook/blob/
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main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
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if sys.version_info[1] <= 7:
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return super().get_num_tokens_from_messages(messages)
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model, encoding = self._get_encoding_model()
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if model.startswith("gpt-3.5-turbo-0301"):
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# every message follows <im_start>{role/name}\n{content}<im_end>\n
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tokens_per_message = 4
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# if there's a name, the role is omitted
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tokens_per_name = -1
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elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4"):
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tokens_per_message = 3
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tokens_per_name = 1
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else:
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raise NotImplementedError(
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f"get_num_tokens_from_messages() is not presently implemented "
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f"for model {model}."
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"See https://github.com/openai/openai-python/blob/main/chatml.md for "
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"information on how messages are converted to tokens."
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)
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num_tokens = 0
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messages_dict = [_convert_message_to_dict(m) for m in messages]
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for message in messages_dict:
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num_tokens += tokens_per_message
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for key, value in message.items():
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# Cast str(value) in case the message value is not a string
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# This occurs with function messages
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num_tokens += len(encoding.encode(str(value)))
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if key == "name":
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num_tokens += tokens_per_name
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# every reply is primed with <im_start>assistant
|
||||
num_tokens += 3
|
||||
return num_tokens
|
||||
|
||||
def get_token_usage(self, text: str, prefix_tokens: Optional[List[str]] = None) -> int:
|
||||
"""Get the number of tokens used by the provided text."""
|
||||
token_ids = self.get_token_ids(text)
|
||||
if prefix_tokens:
|
||||
prefix_token_ids = self.get_token_ids(" ".join(prefix_tokens))
|
||||
token_ids = prefix_token_ids + token_ids
|
||||
return len(token_ids)
|
||||
|
||||
def completion_with_retry(
|
||||
self, run_manager: Optional[Callable] = None, **kwargs: Any
|
||||
) -> Any:
|
||||
"""Use tenacity to retry the completion call."""
|
||||
retry_decorator = self._create_retry_decorator(run_manager=run_manager)
|
||||
|
||||
@retry_decorator
|
||||
def _completion_with_retry(**kwargs: Any) -> Any:
|
||||
return self.client.create(**kwargs)
|
||||
|
||||
return _completion_with_retry(**kwargs)
|
||||
|
||||
async def acompletion_with_retry(
|
||||
self, run_manager: Optional[Callable] = None, **kwargs: Any
|
||||
) -> Any:
|
||||
"""Use tenacity to retry the async completion call."""
|
||||
retry_decorator = self._create_retry_decorator(run_manager=run_manager)
|
||||
|
||||
@retry_decorator
|
||||
async def _completion_with_retry(**kwargs: Any) -> Any:
|
||||
# Use OpenAI's async api https://github.com/openai/openai-python#async-api
|
||||
return await self.client.acreate(**kwargs)
|
||||
|
||||
return await _completion_with_retry(**kwargs)
|
||||
|
||||
def _create_retry_decorator(
|
||||
self,
|
||||
run_manager: Optional[Callable] = None,
|
||||
) -> Callable[[Any], Any]:
|
||||
import openai
|
||||
|
||||
errors = [
|
||||
openai.error.Timeout,
|
||||
openai.error.APIError,
|
||||
openai.error.APIConnectionError,
|
||||
openai.error.RateLimitError,
|
||||
openai.error.ServiceUnavailableError,
|
||||
]
|
||||
return create_base_retry_decorator(
|
||||
error_types=errors, max_retries=self.max_retries, run_manager=run_manager
|
||||
)
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[Callable] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
||||
async for chunk in await self.acompletion_with_retry(
|
||||
messages=message_dicts, run_manager=run_manager, **params
|
||||
):
|
||||
if len(chunk["choices"]) == 0:
|
||||
continue
|
||||
delta = chunk["choices"][0]["delta"]
|
||||
chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
|
||||
default_chunk_class = chunk.__class__
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
return self.ai(message, **kwargs)
|
||||
except Exception as error:
|
||||
print(f"Error in OpenAI, {error}")
|
||||
|
||||
def fetch_api_key(self):
|
||||
pass
|
||||
|
||||
|
||||
#usage
|
||||
#from swarms import OpenAI()
|
||||
#chat = OpenAI()
|
||||
#response = chat("Hello World")
|
||||
#print(response)
|
||||
Loading…
Reference in new issue