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Revert "Add ChatGLM for llm and chat_model by using ChatGLM API (#9797)" (#10805)

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@etveritas reverting for now until this is resolved
https://github.com/langchain-ai/langchain/pull/9797/files#r1330795585,
apologies for merging too eagerly!
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Bagatur 2023-09-19 16:23:42 -07:00 committed by GitHub
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2
libs/langchain/langchain/chat_models/__init__.py
View File

@ -22,7 +22,6 @@ from langchain.chat_models.anyscale import ChatAnyscale
from langchain.chat_models.azure_openai import AzureChatOpenAI from langchain.chat_models.azure_openai import AzureChatOpenAI
from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
from langchain.chat_models.bedrock import BedrockChat 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.ernie import ErnieBotChat
from langchain.chat_models.fake import FakeListChatModel from langchain.chat_models.fake import FakeListChatModel
from langchain.chat_models.google_palm import ChatGooglePalm from langchain.chat_models.google_palm import ChatGooglePalm
@ -52,7 +51,6 @@ __all__ = [
"ChatAnyscale", "ChatAnyscale",
"ChatLiteLLM", "ChatLiteLLM",
"ErnieBotChat", "ErnieBotChat",
"ChatChatGLM",
"ChatKonko", "ChatKonko",
"QianfanChatEndpoint", "QianfanChatEndpoint",
] ]

595
libs/langchain/langchain/chat_models/chatchatglm.py
View File

@ -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 <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

26
libs/langchain/langchain/llms/chatglm.py
View File

@ -26,7 +26,6 @@ class ChatGLM(LLM):
""" """
endpoint_url: str = "http://127.0.0.1:8000/" endpoint_url: str = "http://127.0.0.1:8000/"
api_key: str = ""
"""Endpoint URL to use.""" """Endpoint URL to use."""
model_kwargs: Optional[dict] = None model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model.""" """Key word arguments to pass to the model."""
@ -79,21 +78,8 @@ class ChatGLM(LLM):
_model_kwargs = self.model_kwargs or {} _model_kwargs = self.model_kwargs or {}
# HTTP headers for authorization # HTTP headers for authorization
headers = { headers = {"Content-Type": "application/json"}
"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_})
payload = { payload = {
"prompt": prompt, "prompt": prompt,
"temperature": self.temperature, "temperature": self.temperature,
@ -119,11 +105,12 @@ class ChatGLM(LLM):
try: try:
parsed_response = response.json() parsed_response = response.json()
# Check if response content does exists # Check if response content does exists
if isinstance(parsed_response, dict): if isinstance(parsed_response, dict):
content_keys = "data" content_keys = "response"
if content_keys in parsed_response: if content_keys in parsed_response:
text = eval(parsed_response[content_keys]["choices"][0]["content"]) text = parsed_response[content_keys]
else: else:
raise ValueError(f"No content in response : {parsed_response}") raise ValueError(f"No content in response : {parsed_response}")
else: else:
@ -138,6 +125,5 @@ class ChatGLM(LLM):
if stop is not None: if stop is not None:
text = enforce_stop_tokens(text, stop) text = enforce_stop_tokens(text, stop)
if self.with_history: if self.with_history:
self.history = self.history + [[None, parsed_response["data"]["choices"]]] self.history = self.history + [[None, parsed_response["response"]]]
return text return text