mirror of
https://github.com/1Panel-dev/MaxKB.git
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79 lines
2.8 KiB
Python
79 lines
2.8 KiB
Python
# coding=utf-8
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"""
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@project: maxkb
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@Author：虎
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@file： __init__.py.py
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@date：2024/04/19 15:55
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@desc:
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"""
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from typing import List, Optional, Any, Iterator, Dict
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from langchain_community.chat_models.sparkllm import \
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ChatSparkLLM, convert_message_to_dict, _convert_delta_to_message_chunk
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.messages import BaseMessage, AIMessageChunk
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from langchain_core.outputs import ChatGenerationChunk
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from models_provider.base_model_provider import MaxKBBaseModel
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class XFChatSparkLLM(MaxKBBaseModel, ChatSparkLLM):
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@staticmethod
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def is_cache_model():
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return False
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@staticmethod
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def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
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optional_params = MaxKBBaseModel.filter_optional_params(model_kwargs)
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return XFChatSparkLLM(
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spark_app_id=model_credential.get('spark_app_id'),
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spark_api_key=model_credential.get('spark_api_key'),
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spark_api_secret=model_credential.get('spark_api_secret'),
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spark_api_url=model_credential.get('spark_api_url'),
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spark_llm_domain=model_name,
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streaming=model_kwargs.get('streaming', False),
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**optional_params
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)
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usage_metadata: dict = {}
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def get_last_generation_info(self) -> Optional[Dict[str, Any]]:
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return self.usage_metadata
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def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
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return self.usage_metadata.get('prompt_tokens', 0)
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def get_num_tokens(self, text: str) -> int:
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return self.usage_metadata.get('completion_tokens', 0)
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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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default_chunk_class = AIMessageChunk
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self.client.arun(
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[convert_message_to_dict(m) for m in messages],
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self.spark_user_id,
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self.model_kwargs,
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True,
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)
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for content in self.client.subscribe(timeout=self.request_timeout):
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if "data" in content:
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delta = content["data"]
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chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
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cg_chunk = ChatGenerationChunk(message=chunk)
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elif "usage" in content:
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generation_info = content["usage"]
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self.usage_metadata = generation_info
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continue
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else:
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continue
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if cg_chunk is not None:
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if run_manager:
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run_manager.on_llm_new_token(str(cg_chunk.message.content), chunk=cg_chunk)
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yield cg_chunk
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