From c4a6de3fc98a50f84a0acecc439d00e4874ef5a5 Mon Sep 17 00:00:00 2001 From: Bagatur <22008038+baskaryan@users.noreply.github.com> Date: Tue, 19 Sep 2023 16:23:42 -0700 Subject: [PATCH] Revert "Add ChatGLM for llm and chat_model by using ChatGLM API (#9797)" (#10805) @etveritas reverting for now until this is resolved https://github.com/langchain-ai/langchain/pull/9797/files#r1330795585, apologies for merging too eagerly! --- .../langchain/chat_models/__init__.py | 2 - .../langchain/chat_models/chatchatglm.py | 595 ------------------ libs/langchain/langchain/llms/chatglm.py | 26 +- 3 files changed, 6 insertions(+), 617 deletions(-) delete mode 100644 libs/langchain/langchain/chat_models/chatchatglm.py diff --git a/libs/langchain/langchain/chat_models/__init__.py b/libs/langchain/langchain/chat_models/__init__.py index 3dbf09269e..2febdc1fe6 100644 --- a/libs/langchain/langchain/chat_models/__init__.py +++ b/libs/langchain/langchain/chat_models/__init__.py @@ -22,7 +22,6 @@ from langchain.chat_models.anyscale import ChatAnyscale from langchain.chat_models.azure_openai import AzureChatOpenAI from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint from langchain.chat_models.bedrock import BedrockChat -from langchain.chat_models.chatchatglm import ChatChatGLM from langchain.chat_models.ernie import ErnieBotChat from langchain.chat_models.fake import FakeListChatModel from langchain.chat_models.google_palm import ChatGooglePalm @@ -52,7 +51,6 @@ __all__ = [ "ChatAnyscale", "ChatLiteLLM", "ErnieBotChat", - "ChatChatGLM", "ChatKonko", "QianfanChatEndpoint", ] diff --git a/libs/langchain/langchain/chat_models/chatchatglm.py b/libs/langchain/langchain/chat_models/chatchatglm.py deleted file mode 100644 index be725db829..0000000000 --- a/libs/langchain/langchain/chat_models/chatchatglm.py +++ /dev/null @@ -1,595 +0,0 @@ -"""ChatGLM chat wrapper.""" -from __future__ import annotations - -import copy -import logging -import sys -from typing import ( - TYPE_CHECKING, - Any, - AsyncIterator, - Callable, - Dict, - Iterator, - List, - Mapping, - Optional, - Tuple, - Union, -) - -from langchain.callbacks.manager import ( - AsyncCallbackManagerForLLMRun, - CallbackManagerForLLMRun, -) -from langchain.chat_models.base import BaseChatModel -from langchain.llms.base import create_base_retry_decorator -from langchain.pydantic_v1 import Field, root_validator -from langchain.schema import ChatGeneration, ChatResult -from langchain.schema.messages import ( - AIMessage, - AIMessageChunk, - BaseMessage, - BaseMessageChunk, - ChatMessage, - ChatMessageChunk, - FunctionMessage, - FunctionMessageChunk, - HumanMessage, - HumanMessageChunk, - SystemMessage, - SystemMessageChunk, -) -from langchain.schema.output import ChatGenerationChunk -from langchain.utils import get_from_dict_or_env, get_pydantic_field_names - -if TYPE_CHECKING: - import tiktoken - -logger = logging.getLogger(__name__) - - -def _import_tiktoken() -> Any: - try: - import tiktoken - except ImportError: - raise ValueError( - "Could not import tiktoken python package. " - "This is needed in order to calculate get_token_ids. " - "Please install it with `pip install tiktoken`." - ) - return tiktoken - - -def _create_retry_decorator( - llm: ChatChatGLM, - run_manager: Optional[ - Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] - ] = None, -) -> Callable[[Any], Any]: - errors = [ - BaseException, - ] - return create_base_retry_decorator( - error_types=errors, max_retries=llm.max_retries, run_manager=run_manager - ) - - -async def acompletion_with_retry( - llm: ChatChatGLM, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, -) -> Any: - """Use tenacity to retry the async completion call.""" - retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) - - @retry_decorator - async def _completion_with_retry(**kwargs: Any) -> Any: - # Use ChatGLM's async api - # https://open.bigmodel.cn/api/paas/v3/model-api/chatglm_pro/invoke - m_kwargs = copy.deepcopy(kwargs) - m_kwargs["prompt"] = kwargs["messages"] - if len(m_kwargs["prompt"]) // 2 == 0: - raise ValueError("The length of the Prompt must be an odd number.") - if m_kwargs.get("streaming") or m_kwargs.get("stream"): - try: - from aiostream.stream import list as alist - except ImportError as e: - raise ImportError( - "Streaming with ChatChatGLMrequires optional dependency aiostream. " - "To install please run `pip install aiostream`." - ) from e - - async def async_gen(**m_kwargs: Any) -> Any: - for event in llm.client.sse_invoke(**m_kwargs).events(): - yield event.data - - return alist(async_gen(**m_kwargs)) - else: - return llm.client.invoke(**m_kwargs) - - return await _completion_with_retry(**kwargs) - - -def _convert_delta_to_message_chunk( - _dict: Mapping[str, Any], default_class: type[BaseMessageChunk] -) -> BaseMessageChunk: - role = _dict.get("role") - content = _dict.get("content") or "" - if _dict.get("function_call"): - additional_kwargs = {"function_call": dict(_dict["function_call"])} - else: - additional_kwargs = {} - - if role == "user" or default_class == HumanMessageChunk: - return HumanMessageChunk(content=content) - elif role == "assistant" or default_class == AIMessageChunk: - return AIMessageChunk(content=content, additional_kwargs=additional_kwargs) - elif role == "system" or default_class == SystemMessageChunk: - return SystemMessageChunk(content=content) - elif role == "function" or default_class == FunctionMessageChunk: - return FunctionMessageChunk(content=content, name=_dict["name"]) - elif role or default_class == ChatMessageChunk: - return ChatMessageChunk(content=content, role=role) - else: - return default_class(content=content) - - -def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage: - role = _dict["role"] - if role == "user": - return HumanMessage(content=_dict["content"]) - elif role == "assistant": - # Fix for azure - # Also ChatGLM returns None for tool invocations - content = _dict.get("content", "") or "" - if _dict.get("function_call"): - additional_kwargs = {"function_call": dict(_dict["function_call"])} - else: - additional_kwargs = {} - return AIMessage(content=eval(content), additional_kwargs=additional_kwargs) - elif role == "system": - return SystemMessage(content=_dict["content"]) - elif role == "function": - return FunctionMessage(content=_dict["content"], name=_dict["name"]) - else: - return ChatMessage(content=_dict["content"], role=role) - - -def convert_chatglm_messages(messages: List[dict]) -> List[BaseMessage]: - """Convert dictionaries representing ChatGLM messages to LangChain format. - - Args: - messages: List of dictionaries representing ChatGLM messages - - Returns: - List of LangChain BaseMessage objects. - """ - return [_convert_dict_to_message(m) for m in messages] - - -def _convert_message_to_dict(message: BaseMessage) -> dict: - if isinstance(message, ChatMessage): - message_dict = {"role": message.role, "content": message.content} - elif isinstance(message, HumanMessage): - message_dict = {"role": "user", "content": message.content} - elif isinstance(message, AIMessage): - message_dict = {"role": "assistant", "content": message.content} - if "function_call" in message.additional_kwargs: - message_dict["function_call"] = message.additional_kwargs["function_call"] - elif isinstance(message, SystemMessage): - message_dict = {"role": "system", "content": message.content} - elif isinstance(message, FunctionMessage): - message_dict = { - "role": "function", - "content": message.content, - "name": message.name, - } - else: - raise ValueError(f"Got unknown type {message}") - if "name" in message.additional_kwargs: - message_dict["name"] = message.additional_kwargs["name"] - return message_dict - - -class ChatChatGLM(BaseChatModel): - """Wrapper around ChatGLM Chat large language models. - - To use, you should have the ``zhipuai`` python package installed, and the - environment variable ``CHATGLM_API_KEY`` set with your API key. - - Any parameters that are valid to be passed to the chatglm.create call can be passed - in, even if not explicitly saved on this class. - - Example: - .. code-block:: python - - from langchain.chat_models import ChatChatGLM - chatglm = ChatChatGLM(model_name="gpt-3.5-turbo") - """ - - @property - def lc_secrets(self) -> Dict[str, str]: - return {"chatglm_api_key": "CHATGLM_API_KEY"} - - @property - def lc_serializable(self) -> bool: - return True - - client: Any = None #: :meta private: - model_name: str = Field(default="chatglm_pro", alias="model") - """Model name to use.""" - temperature: float = 0.7 - """What sampling temperature to use.""" - model_kwargs: Dict[str, Any] = Field(default_factory=dict) - """Holds any model parameters valid for `create` call not explicitly specified.""" - chatglm_api_key: Optional[str] = None - """Base URL path for API requests, - leave blank if not using a proxy or service emulator.""" - chatglm_api_base: Optional[str] = None - chatglm_organization: Optional[str] = None - # to support explicit proxy for ChatGLM - chatglm_proxy: Optional[str] = None - request_timeout: Optional[Union[float, Tuple[float, float]]] = None - """Timeout for requests to ChatGLM completion API. Default is 600 seconds.""" - max_retries: int = 6 - """Maximum number of retries to make when generating.""" - streaming: bool = False - """Whether to stream the results or not.""" - n: int = 1 - """Number of chat completions to generate for each prompt.""" - max_tokens: Optional[int] = None - """Maximum number of tokens to generate.""" - tiktoken_model_name: Optional[str] = None - """The model name to pass to tiktoken when using this class. - Tiktoken is used to count the number of tokens in documents to constrain - them to be under a certain limit. By default, when set to None, this will - be the same as the embedding model name. However, there are some cases - where you may want to use this Embedding class with a model name not - supported by tiktoken. This can include when using Azure embeddings or - when using one of the many model providers that expose an ChatGLM-like - API but with different models. In those cases, in order to avoid erroring - when tiktoken is called, you can specify a model name to use here.""" - - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True - - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: - """Build extra kwargs from additional params that were passed in.""" - all_required_field_names = get_pydantic_field_names(cls) - extra = values.get("model_kwargs", {}) - for field_name in list(values): - if field_name in extra: - raise ValueError(f"Found {field_name} supplied twice.") - if field_name not in all_required_field_names: - logger.warning( - f"""WARNING! {field_name} is not default parameter. - {field_name} was transferred to model_kwargs. - Please confirm that {field_name} is what you intended.""" - ) - extra[field_name] = values.pop(field_name) - - invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) - if invalid_model_kwargs: - raise ValueError( - f"Parameters {invalid_model_kwargs} should be specified explicitly. " - f"Instead they were passed in as part of `model_kwargs` parameter." - ) - - values["model_kwargs"] = extra - return values - - @root_validator() - def validate_environment(cls, values: Dict) -> Dict: - """Validate that api key and python package exists in environment.""" - values["chatglm_api_key"] = get_from_dict_or_env( - values, "chatglm_api_key", "CHATGLM_API_KEY" - ) - values["chatglm_organization"] = get_from_dict_or_env( - values, - "chatglm_organization", - "CHATGLM_ORGANIZATION", - default="", - ) - values["chatglm_api_base"] = get_from_dict_or_env( - values, - "chatglm_api_base", - "CHATGLM_API_BASE", - default="", - ) - values["chatglm_proxy"] = get_from_dict_or_env( - values, - "chatglm_proxy", - "CHATGLM_PROXY", - default="", - ) - try: - import zhipuai - - zhipuai.api_key = values["chatglm_api_key"] - except ImportError: - raise ValueError( - "Could not import zhipuai python package. " - "Please install it with `pip install zhipuai`." - ) - try: - values["client"] = zhipuai.model_api - except AttributeError: - raise ValueError( - "`zhipuai` has no `model_api` attribute, this is likely " - "due to an old version of the zhipuai package. Try upgrading it " - "with `pip install --upgrade zhipuai`." - ) - if values["n"] < 1: - raise ValueError("n must be at least 1.") - if values["n"] > 1 and values["streaming"]: - raise ValueError("n must be 1 when streaming.") - return values - - @property - def _default_params(self) -> Dict[str, Any]: - """Get the default parameters for calling ChatGLM API.""" - return { - "model": self.model_name, - "request_timeout": self.request_timeout, - "max_tokens": self.max_tokens, - "stream": self.streaming, - "n": self.n, - "temperature": self.temperature, - **self.model_kwargs, - } - - def completion_with_retry( - self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any - ) -> Any: - """Use tenacity to retry the completion call.""" - retry_decorator = _create_retry_decorator(self, run_manager=run_manager) - - @retry_decorator - def _completion_with_retry(**kwargs: Any) -> Any: - m_kwargs = copy.deepcopy(kwargs) - m_kwargs["prompt"] = kwargs["messages"] - if len(m_kwargs["prompt"]) // 2 == 0: - raise ValueError("The length of the Prompt must be an odd number.") - if m_kwargs.get("streaming") or m_kwargs.get("stream"): - return self.client.sse_invoke(**m_kwargs) - else: - return self.client.invoke(**m_kwargs) - - return _completion_with_retry(**kwargs) - - def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: - overall_token_usage: dict = {} - for output in llm_outputs: - if output is None: - # Happens in streaming - continue - token_usage = output["token_usage"] - for k, v in token_usage.items(): - if k in overall_token_usage: - overall_token_usage[k] += v - else: - overall_token_usage[k] = v - return {"token_usage": overall_token_usage, "model_name": self.model_name} - - def _stream( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> Iterator[ChatGenerationChunk]: - message_dicts, params = self._create_message_dicts(messages, stop) - params = {**params, **kwargs, "stream": True} - - default_chunk_class = AIMessageChunk - for event in self.completion_with_retry( - messages=message_dicts, run_manager=run_manager, **params - ).events(): - delta = {"role": "assistant", "content": event.data} - chunk = _convert_delta_to_message_chunk(delta, default_chunk_class) - yield ChatGenerationChunk(message=chunk) - if run_manager: - run_manager.on_llm_new_token(chunk.content) - - def _generate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - stream: Optional[bool] = None, - **kwargs: Any, - ) -> ChatResult: - if stream if stream is not None else self.streaming: - generation: Optional[ChatGenerationChunk] = None - for chunk in self._stream( - 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 = self.completion_with_retry( - messages=message_dicts, run_manager=run_manager, **params - ) - return self._create_chat_result(response) - - def _create_message_dicts( - self, messages: List[BaseMessage], stop: Optional[List[str]] - ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: - params = self._client_params - if stop is not None: - if "stop" in params: - raise ValueError("`stop` found in both the input and default params.") - params["stop"] = stop - message_dicts = [_convert_message_to_dict(m) for m in messages] - return message_dicts, params - - def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult: - generations = [] - for res in response["data"]["choices"]: - message = _convert_dict_to_message(res) - gen = ChatGeneration( - message=message, - generation_info=dict(finish_reason=res.get("finish_reason")), - ) - generations.append(gen) - token_usage = response.get("usage", {}) - llm_output = {"token_usage": token_usage, "model_name": self.model_name} - return ChatResult(generations=generations, llm_output=llm_output) - - async def _astream( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = 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 event in await acompletion_with_retry( - 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 {role/name}\n{content}\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 assistant - num_tokens += 3 - return num_tokens diff --git a/libs/langchain/langchain/llms/chatglm.py b/libs/langchain/langchain/llms/chatglm.py index 9c8fe7974a..232f2f9af7 100644 --- a/libs/langchain/langchain/llms/chatglm.py +++ b/libs/langchain/langchain/llms/chatglm.py @@ -26,7 +26,6 @@ class ChatGLM(LLM): """ endpoint_url: str = "http://127.0.0.1:8000/" - api_key: str = "" """Endpoint URL to use.""" model_kwargs: Optional[dict] = None """Key word arguments to pass to the model.""" @@ -79,21 +78,8 @@ class ChatGLM(LLM): _model_kwargs = self.model_kwargs or {} # HTTP headers for authorization - headers = { - "Accept": "application/json", - "Content-Type": "application/json; charset=UTF-8", - } - try: - from zhipuai.utils import jwt_token - except Exception as e: - raise Exception("Must install zhipuai, use`pip install zhipuai`", e) - if not self.api_key: - raise Exception( - "api_key not provided, you could provide it with " - "`shell: export API_KEY=xxx` or `code: zhipuai.api_key=xxx`" - ) - jwt_api_key_ = jwt_token.generate_token(self.api_key) - headers.update({"Authorization": jwt_api_key_}) + headers = {"Content-Type": "application/json"} + payload = { "prompt": prompt, "temperature": self.temperature, @@ -119,11 +105,12 @@ class ChatGLM(LLM): try: parsed_response = response.json() + # Check if response content does exists if isinstance(parsed_response, dict): - content_keys = "data" + content_keys = "response" if content_keys in parsed_response: - text = eval(parsed_response[content_keys]["choices"][0]["content"]) + text = parsed_response[content_keys] else: raise ValueError(f"No content in response : {parsed_response}") else: @@ -138,6 +125,5 @@ class ChatGLM(LLM): if stop is not None: text = enforce_stop_tokens(text, stop) if self.with_history: - self.history = self.history + [[None, parsed_response["data"]["choices"]]] - + self.history = self.history + [[None, parsed_response["response"]]] return text