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https://github.com/RYDE-WORK/Langchain-Chatchat.git
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108 lines
4.9 KiB
Python
108 lines
4.9 KiB
Python
import json
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from typing import List
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from loom_core.openai_plugins.core.adapter import LLMWorkerInfo
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from loom_core.openai_plugins.core.profile_endpoint.core import ProfileEndpointAdapter
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import os
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import sys
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import logging
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logger = logging.getLogger(__name__)
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# 为了能使用插件中的函数，需要将当前目录加入到sys.path中
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root_dir = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(root_dir)
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class OpenAIProfileEndpointAdapter(ProfileEndpointAdapter):
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"""Adapter for the profile endpoint."""
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def __init__(self, cfg=None, state_dict: dict = None):
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self._cfg = cfg
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super().__init__(state_dict=state_dict)
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def class_name(self) -> str:
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"""Get class name."""
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return self.__name__
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def list_running_models(self) -> List[LLMWorkerInfo]:
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"""模型列表及其配置项"""
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list_worker = []
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list_worker.append(LLMWorkerInfo(worker_id="gpt-3.5-turbo",
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model_name="gpt-3.5-turbo",
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model_description="gpt-3.5-turbo",
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providers=["model", "embedding"],
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model_extra_info="{}"))
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list_worker.append(LLMWorkerInfo(worker_id="gpt-4",
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model_name="gpt-4",
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model_description="gpt-4",
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providers=["model", "embedding"],
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model_extra_info="{}"))
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list_worker.append(LLMWorkerInfo(worker_id="gpt-4-1106-preview",
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model_name="gpt-4-1106-preview",
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model_description="gpt-4-1106-preview",
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providers=["model", "embedding"],
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model_extra_info="{}"))
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list_worker.append(LLMWorkerInfo(worker_id="text-embedding-ada-002",
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model_name="text-embedding-ada-002",
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model_description="text-embedding-ada-002",
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providers=["embedding"],
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model_extra_info="{}"))
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return list_worker
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def list_llm_models(self) -> List[LLMWorkerInfo]:
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"""获取已配置模型列表"""
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list_worker = []
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list_worker.append(LLMWorkerInfo(worker_id="gpt-3.5-turbo",
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model_name="gpt-3.5-turbo",
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model_description="gpt-3.5-turbo",
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providers=["model", "embedding"],
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model_extra_info="{}"))
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list_worker.append(LLMWorkerInfo(worker_id="gpt-4",
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model_name="gpt-4",
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model_description="gpt-4",
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providers=["model", "embedding"],
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model_extra_info="{}"))
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list_worker.append(LLMWorkerInfo(worker_id="gpt-4-1106-preview",
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model_name="gpt-4-1106-preview",
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model_description="gpt-4-1106-preview",
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providers=["model", "embedding"],
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model_extra_info="{}"))
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list_worker.append(LLMWorkerInfo(worker_id="text-embedding-ada-002",
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model_name="text-embedding-ada-002",
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model_description="text-embedding-ada-002",
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providers=["embedding"],
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model_extra_info="{}"))
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return list_worker
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def get_model_config(self, model_name) -> LLMWorkerInfo:
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'''
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获取LLM模型配置项（合并后的）
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'''
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info_obj = LLMWorkerInfo(worker_id=model_name,
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model_name=model_name,
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model_description="",
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providers=["model", "embedding"],
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model_extra_info="{}")
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return info_obj
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@classmethod
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def from_config(cls, cfg=None):
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_state_dict = {
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"profile_name": "openai",
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"profile_version": "0.0.1",
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"profile_description": "openai profile endpoint",
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"profile_author": "openai"
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}
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state_dict = cfg.get("state_dict", {})
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if state_dict is not None and _state_dict is not None:
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_state_dict = {**state_dict, **_state_dict}
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else:
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# 处理其中一个或两者都为 None 的情况
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_state_dict = state_dict or _state_dict or {}
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return cls(cfg=cfg, state_dict=_state_dict)
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