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feat: chatglm3 support (#1616)
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takatost
GitHub
parent
0e627c920f
commit
ea526d0822
@ -1,27 +1,45 @@
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import decimal
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import logging
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from typing import List, Optional, Any
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import openai
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from langchain.callbacks.manager import Callbacks
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from langchain.llms import ChatGLM
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from langchain.schema import LLMResult
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from langchain.schema import LLMResult, get_buffer_string
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from core.model_providers.error import LLMBadRequestError
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from core.model_providers.error import LLMBadRequestError, LLMRateLimitError, LLMAuthorizationError, \
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LLMAPIUnavailableError, LLMAPIConnectionError
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from core.model_providers.models.llm.base import BaseLLM
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from core.model_providers.models.entity.message import PromptMessage, MessageType
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from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs
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from core.third_party.langchain.llms.chat_open_ai import EnhanceChatOpenAI
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class ChatGLMModel(BaseLLM):
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model_mode: ModelMode = ModelMode.COMPLETION
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model_mode: ModelMode = ModelMode.CHAT
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def _init_client(self) -> Any:
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provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
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return ChatGLM(
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extra_model_kwargs = {
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'top_p': provider_model_kwargs.get('top_p')
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}
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if provider_model_kwargs.get('max_length') is not None:
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extra_model_kwargs['max_length'] = provider_model_kwargs.get('max_length')
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client = EnhanceChatOpenAI(
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model_name=self.name,
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temperature=provider_model_kwargs.get('temperature'),
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max_tokens=provider_model_kwargs.get('max_tokens'),
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model_kwargs=extra_model_kwargs,
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streaming=self.streaming,
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callbacks=self.callbacks,
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endpoint_url=self.credentials.get('api_base'),
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**provider_model_kwargs
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request_timeout=60,
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openai_api_key="1",
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openai_api_base=self.credentials['api_base'] + '/v1'
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)
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return client
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def _run(self, messages: List[PromptMessage],
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stop: Optional[List[str]] = None,
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callbacks: Callbacks = None,
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@ -45,19 +63,40 @@ class ChatGLMModel(BaseLLM):
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:return:
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"""
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prompts = self._get_prompt_from_messages(messages)
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return max(self._client.get_num_tokens(prompts), 0)
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return max(sum([self._client.get_num_tokens(get_buffer_string([m])) for m in prompts]) - len(prompts), 0)
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def get_currency(self):
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return 'RMB'
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def _set_model_kwargs(self, model_kwargs: ModelKwargs):
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provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
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for k, v in provider_model_kwargs.items():
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if hasattr(self.client, k):
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setattr(self.client, k, v)
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extra_model_kwargs = {
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'top_p': provider_model_kwargs.get('top_p')
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}
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self.client.temperature = provider_model_kwargs.get('temperature')
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self.client.max_tokens = provider_model_kwargs.get('max_tokens')
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self.client.model_kwargs = extra_model_kwargs
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def handle_exceptions(self, ex: Exception) -> Exception:
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if isinstance(ex, ValueError):
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return LLMBadRequestError(f"ChatGLM: {str(ex)}")
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if isinstance(ex, openai.error.InvalidRequestError):
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logging.warning("Invalid request to ChatGLM API.")
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return LLMBadRequestError(str(ex))
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elif isinstance(ex, openai.error.APIConnectionError):
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logging.warning("Failed to connect to ChatGLM API.")
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return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex))
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elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)):
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logging.warning("ChatGLM service unavailable.")
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return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex))
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elif isinstance(ex, openai.error.RateLimitError):
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return LLMRateLimitError(str(ex))
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elif isinstance(ex, openai.error.AuthenticationError):
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return LLMAuthorizationError(str(ex))
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elif isinstance(ex, openai.error.OpenAIError):
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return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex))
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else:
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return ex
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@classmethod
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def support_streaming(cls):
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return True
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@ -2,6 +2,7 @@ import json
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from json import JSONDecodeError
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from typing import Type
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import requests
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from langchain.llms import ChatGLM
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from core.helper import encrypter
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@ -25,21 +26,26 @@ class ChatGLMProvider(BaseModelProvider):
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if model_type == ModelType.TEXT_GENERATION:
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return [
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{
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'id': 'chatglm2-6b',
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'name': 'ChatGLM2-6B',
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'mode': ModelMode.COMPLETION.value,
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'id': 'chatglm3-6b',
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'name': 'ChatGLM3-6B',
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'mode': ModelMode.CHAT.value,
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},
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{
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'id': 'chatglm-6b',
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'name': 'ChatGLM-6B',
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'mode': ModelMode.COMPLETION.value,
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'id': 'chatglm3-6b-32k',
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'name': 'ChatGLM3-6B-32K',
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'mode': ModelMode.CHAT.value,
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},
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{
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'id': 'chatglm2-6b',
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'name': 'ChatGLM2-6B',
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'mode': ModelMode.CHAT.value,
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}
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]
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else:
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return []
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def _get_text_generation_model_mode(self, model_name) -> str:
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return ModelMode.COMPLETION.value
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return ModelMode.CHAT.value
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def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]:
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"""
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@ -64,16 +70,19 @@ class ChatGLMProvider(BaseModelProvider):
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:return:
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"""
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model_max_tokens = {
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'chatglm-6b': 2000,
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'chatglm2-6b': 32000,
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'chatglm3-6b-32k': 32000,
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'chatglm3-6b': 8000,
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'chatglm2-6b': 8000,
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}
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max_tokens_alias = 'max_length' if model_name == 'chatglm2-6b' else 'max_tokens'
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return ModelKwargsRules(
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temperature=KwargRule[float](min=0, max=2, default=1, precision=2),
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top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2),
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presence_penalty=KwargRule[float](enabled=False),
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frequency_penalty=KwargRule[float](enabled=False),
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max_tokens=KwargRule[int](alias='max_token', min=10, max=model_max_tokens.get(model_name), default=2048, precision=0),
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max_tokens=KwargRule[int](alias=max_tokens_alias, min=10, max=model_max_tokens.get(model_name), default=2048, precision=0),
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)
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@classmethod
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@ -85,16 +94,10 @@ class ChatGLMProvider(BaseModelProvider):
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raise CredentialsValidateFailedError('ChatGLM Endpoint URL must be provided.')
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try:
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credential_kwargs = {
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'endpoint_url': credentials['api_base']
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}
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response = requests.get(f"{credentials['api_base']}/v1/models", timeout=5)
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llm = ChatGLM(
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max_token=10,
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**credential_kwargs
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)
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llm("ping")
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if response.status_code != 200:
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raise Exception('ChatGLM Endpoint URL is invalid.')
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except Exception as ex:
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raise CredentialsValidateFailedError(str(ex))
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