mirror of https://github.com/hwchase17/langchain
@etveritas reverting for now until this is resolved https://github.com/langchain-ai/langchain/pull/9797/files#r1330795585, apologies for merging too eagerly!pull/9856/head^2
Bagatur
11 months ago
committed by
GitHub
parent
c86a1a6710
commit
c4a6de3fc9
@ -1,595 +0,0 @@
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"""ChatGLM chat wrapper."""
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from __future__ import annotations
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import copy
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import logging
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import sys
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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 langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.chat_models.base import BaseChatModel
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from langchain.llms.base import create_base_retry_decorator
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from langchain.pydantic_v1 import Field, root_validator
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from langchain.schema import ChatGeneration, ChatResult
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from langchain.schema.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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FunctionMessage,
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FunctionMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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)
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from langchain.schema.output import ChatGenerationChunk
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from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
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if TYPE_CHECKING:
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import tiktoken
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logger = logging.getLogger(__name__)
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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 _create_retry_decorator(
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llm: ChatChatGLM,
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run_manager: Optional[
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Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
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] = None,
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) -> Callable[[Any], Any]:
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errors = [
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BaseException,
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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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async def acompletion_with_retry(
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llm: ChatChatGLM,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the async completion call."""
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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# Use ChatGLM's async api
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# https://open.bigmodel.cn/api/paas/v3/model-api/chatglm_pro/invoke
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m_kwargs = copy.deepcopy(kwargs)
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m_kwargs["prompt"] = kwargs["messages"]
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if len(m_kwargs["prompt"]) // 2 == 0:
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raise ValueError("The length of the Prompt must be an odd number.")
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if m_kwargs.get("streaming") or m_kwargs.get("stream"):
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try:
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from aiostream.stream import list as alist
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except ImportError as e:
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raise ImportError(
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"Streaming with ChatChatGLMrequires optional dependency aiostream. "
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"To install please run `pip install aiostream`."
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) from e
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async def async_gen(**m_kwargs: Any) -> Any:
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for event in llm.client.sse_invoke(**m_kwargs).events():
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yield event.data
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return alist(async_gen(**m_kwargs))
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else:
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return llm.client.invoke(**m_kwargs)
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return await _completion_with_retry(**kwargs)
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any], default_class: type[BaseMessageChunk]
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) -> 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(_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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# Fix for azure
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# Also ChatGLM returns None for tool invocations
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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=eval(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 convert_chatglm_messages(messages: List[dict]) -> List[BaseMessage]:
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"""Convert dictionaries representing ChatGLM messages to LangChain format.
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Args:
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messages: List of dictionaries representing ChatGLM messages
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Returns:
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List of LangChain BaseMessage objects.
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"""
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return [_convert_dict_to_message(m) for m in messages]
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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 ChatChatGLM(BaseChatModel):
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"""Wrapper around ChatGLM Chat large language models.
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To use, you should have the ``zhipuai`` python package installed, and the
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environment variable ``CHATGLM_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the chatglm.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 ChatChatGLM
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chatglm = ChatChatGLM(model_name="gpt-3.5-turbo")
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"""
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"chatglm_api_key": "CHATGLM_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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client: Any = None #: :meta private:
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model_name: str = Field(default="chatglm_pro", alias="model")
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature 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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chatglm_api_key: Optional[str] = None
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"""Base URL path for API requests,
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leave blank if not using a proxy or service emulator."""
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chatglm_api_base: Optional[str] = None
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chatglm_organization: Optional[str] = None
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# to support explicit proxy for ChatGLM
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chatglm_proxy: Optional[str] = None
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request_timeout: Optional[Union[float, Tuple[float, float]]] = None
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"""Timeout for requests to ChatGLM completion API. Default is 600 seconds."""
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max_retries: int = 6
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"""Maximum number of retries to make when generating."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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n: int = 1
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"""Number of chat completions to generate for each prompt."""
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max_tokens: Optional[int] = None
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"""Maximum number of tokens to generate."""
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tiktoken_model_name: Optional[str] = None
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"""The model name to pass to tiktoken when using this class.
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Tiktoken is used to count the number of tokens in documents to constrain
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them to be under a certain limit. By default, when set to None, this will
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be the same as the embedding model name. However, there are some cases
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where you may want to use this Embedding class with a model name not
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supported by tiktoken. This can include when using Azure embeddings or
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when using one of the many model providers that expose an ChatGLM-like
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API but with different models. In those cases, in order to avoid erroring
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when tiktoken is called, you can specify a model name to use here."""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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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 = get_pydantic_field_names(cls)
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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 in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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logger.warning(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
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f"Instead they were passed in as part of `model_kwargs` parameter."
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)
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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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values["chatglm_api_key"] = get_from_dict_or_env(
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values, "chatglm_api_key", "CHATGLM_API_KEY"
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)
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values["chatglm_organization"] = get_from_dict_or_env(
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values,
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"chatglm_organization",
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"CHATGLM_ORGANIZATION",
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default="",
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)
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values["chatglm_api_base"] = get_from_dict_or_env(
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values,
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"chatglm_api_base",
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"CHATGLM_API_BASE",
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default="",
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)
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values["chatglm_proxy"] = get_from_dict_or_env(
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values,
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"chatglm_proxy",
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"CHATGLM_PROXY",
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default="",
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)
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try:
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import zhipuai
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zhipuai.api_key = values["chatglm_api_key"]
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except ImportError:
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raise ValueError(
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"Could not import zhipuai python package. "
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"Please install it with `pip install zhipuai`."
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)
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try:
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values["client"] = zhipuai.model_api
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except AttributeError:
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raise ValueError(
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"`zhipuai` has no `model_api` attribute, this is likely "
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"due to an old version of the zhipuai package. Try upgrading it "
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"with `pip install --upgrade zhipuai`."
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)
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if values["n"] < 1:
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raise ValueError("n must be at least 1.")
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if values["n"] > 1 and values["streaming"]:
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raise ValueError("n must be 1 when streaming.")
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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 ChatGLM 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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def completion_with_retry(
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self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
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) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _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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m_kwargs = copy.deepcopy(kwargs)
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m_kwargs["prompt"] = kwargs["messages"]
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if len(m_kwargs["prompt"]) // 2 == 0:
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raise ValueError("The length of the Prompt must be an odd number.")
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if m_kwargs.get("streaming") or m_kwargs.get("stream"):
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return self.client.sse_invoke(**m_kwargs)
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else:
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return self.client.invoke(**m_kwargs)
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return _completion_with_retry(**kwargs)
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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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def _stream(
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self,
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messages: List[BaseMessage],
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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[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs, "stream": True}
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default_chunk_class = AIMessageChunk
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for event in self.completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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).events():
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delta = {"role": "assistant", "content": event.data}
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chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
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yield ChatGenerationChunk(message=chunk)
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if run_manager:
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run_manager.on_llm_new_token(chunk.content)
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
|
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run_manager: Optional[CallbackManagerForLLMRun] = None,
|
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stream: Optional[bool] = None,
|
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**kwargs: Any,
|
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) -> ChatResult:
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if stream if stream is not None else self.streaming:
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generation: Optional[ChatGenerationChunk] = None
|
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for chunk in self._stream(
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messages=messages, stop=stop, run_manager=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 ChatResult(generations=[generation])
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs}
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response = self.completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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)
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return self._create_chat_result(response)
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def _create_message_dicts(
|
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self, messages: List[BaseMessage], stop: Optional[List[str]]
|
||||
) -> 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:
|
||||
if "stop" in params:
|
||||
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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|
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def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
|
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generations = []
|
||||
for res in response["data"]["choices"]:
|
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message = _convert_dict_to_message(res)
|
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gen = ChatGeneration(
|
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message=message,
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||||
generation_info=dict(finish_reason=res.get("finish_reason")),
|
||||
)
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generations.append(gen)
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token_usage = response.get("usage", {})
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llm_output = {"token_usage": token_usage, "model_name": self.model_name}
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return ChatResult(generations=generations, llm_output=llm_output)
|
||||
|
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async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
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message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
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async for event in await acompletion_with_retry(
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self, messages=message_dicts, run_manager=run_manager, **params
|
||||
):
|
||||
if len(event):
|
||||
delta = {"role": "assistant", "content": event[-1]}
|
||||
else:
|
||||
delta = {"role": "assistant", "content": ""}
|
||||
chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
|
||||
yield ChatGenerationChunk(message=chunk)
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(chunk.content)
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if stream if stream is not None else self.streaming:
|
||||
generation: Optional[ChatGenerationChunk] = None
|
||||
async for chunk in self._astream(
|
||||
messages=messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
):
|
||||
if generation is None:
|
||||
generation = chunk
|
||||
else:
|
||||
generation += chunk
|
||||
assert generation is not None
|
||||
return ChatResult(generations=[generation])
|
||||
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs}
|
||||
response = await acompletion_with_retry(
|
||||
self, messages=message_dicts, run_manager=run_manager, **params
|
||||
)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Dict[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {**{"model_name": self.model_name}, **self._default_params}
|
||||
|
||||
@property
|
||||
def _client_params(self) -> Dict[str, Any]:
|
||||
"""Get the parameters used for the chatglm client."""
|
||||
chatglm_creds: Dict[str, Any] = {
|
||||
"api_key": self.chatglm_api_key,
|
||||
"api_base": self.chatglm_api_base,
|
||||
"organization": self.chatglm_organization,
|
||||
"model": self.model_name,
|
||||
}
|
||||
if self.chatglm_proxy:
|
||||
import zhipuai
|
||||
|
||||
zhipuai.api_key = self.chatglm_api_key
|
||||
# zhipuai.proxy = {"http": self.chatglm_proxy, "https": self.chatglm_proxy}
|
||||
# type: ignore[assignment] # noqa: E501
|
||||
return {**self._default_params, **chatglm_creds}
|
||||
|
||||
def _get_invocation_params(
|
||||
self, stop: Optional[List[str]] = None, **kwargs: Any
|
||||
) -> Dict[str, Any]:
|
||||
"""Get the parameters used to invoke the model."""
|
||||
return {
|
||||
"model": self.model_name,
|
||||
**super()._get_invocation_params(stop=stop),
|
||||
**self._default_params,
|
||||
**kwargs,
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of chat model."""
|
||||
return "chatglm-chat"
|
||||
|
||||
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
|
||||
tiktoken_ = _import_tiktoken()
|
||||
if self.tiktoken_model_name is not None:
|
||||
model = self.tiktoken_model_name
|
||||
else:
|
||||
model = self.model_name
|
||||
# Returns the number of tokens used by a list of messages.
|
||||
try:
|
||||
encoding = tiktoken_.encoding_for_model(model)
|
||||
except KeyError:
|
||||
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
||||
model = "cl100k_base"
|
||||
encoding = tiktoken_.get_encoding(model)
|
||||
return model, encoding
|
||||
|
||||
def get_token_ids(self, text: str) -> List[int]:
|
||||
"""Get the tokens present in the text with tiktoken package."""
|
||||
# tiktoken NOT supported for Python 3.7 or below
|
||||
if sys.version_info[1] <= 7:
|
||||
return super().get_token_ids(text)
|
||||
_, encoding_model = self._get_encoding_model()
|
||||
return encoding_model.encode(text)
|
||||
|
||||
def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
|
||||
"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
|
||||
|
||||
Official documentation: https://open.bigmodel.cn/dev/api
|
||||
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
|
||||
if sys.version_info[1] <= 7:
|
||||
return super().get_num_tokens_from_messages(messages)
|
||||
model, encoding = self._get_encoding_model()
|
||||
if model.startswith("chatglm_pro"):
|
||||
# every message follows <im_start>{role/name}\n{content}<im_end>\n
|
||||
tokens_per_message = 4
|
||||
# if there's a name, the role is omitted
|
||||
tokens_per_name = -1
|
||||
elif model.startswith("chatglm_std") or model.startswith("chatglm_lite"):
|
||||
tokens_per_message = 3
|
||||
tokens_per_name = 1
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f"get_num_tokens_from_messages() is not presently implemented "
|
||||
f"for model {model}."
|
||||
"See https://open.bigmodel.cn/dev/api for "
|
||||
"information on how messages are converted to tokens."
|
||||
)
|
||||
num_tokens = 0
|
||||
messages_dict = [_convert_message_to_dict(m) for m in messages]
|
||||
for message in messages_dict:
|
||||
num_tokens += tokens_per_message
|
||||
for key, value in message.items():
|
||||
# Cast str(value) in case the message value is not a string
|
||||
# This occurs with function messages
|
||||
num_tokens += len(encoding.encode(str(value)))
|
||||
if key == "name":
|
||||
num_tokens += tokens_per_name
|
||||
# every reply is primed with <im_start>assistant
|
||||
num_tokens += 3
|
||||
return num_tokens
|
||||
Loading…
Reference in New Issue