mirror of
https://github.com/RockChinQ/QChatGPT.git
synced 2024-11-16 03:32:33 +08:00
feat: 通过元数据生成模型列表
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RockChinQ
parent
9489783846
commit
97449065df
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@ -53,6 +53,8 @@ class Application:
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plugin_setting_meta: config_mgr.ConfigManager = None
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llm_models_meta: config_mgr.ConfigManager = None
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# =========================
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ctr_mgr: center_mgr.V2CenterAPI = None
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@ -18,7 +18,6 @@ class LoadConfigStage(stage.BootingStage):
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ap.provider_cfg = await config.load_json_config("data/config/provider.json", "templates/provider.json")
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ap.system_cfg = await config.load_json_config("data/config/system.json", "templates/system.json")
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ap.plugin_setting_meta = await config.load_json_config("plugins/plugins.json", "templates/plugin-settings.json")
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await ap.plugin_setting_meta.dump_config()
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@ -27,3 +26,6 @@ class LoadConfigStage(stage.BootingStage):
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ap.adapter_qq_botpy_meta = await config.load_json_config("data/metadata/adapter-qq-botpy.json", "templates/metadata/adapter-qq-botpy.json")
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await ap.adapter_qq_botpy_meta.dump_config()
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ap.llm_models_meta = await config.load_json_config("data/metadata/llm-models.json", "templates/metadata/llm-models.json")
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await ap.llm_models_meta.dump_config()
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@ -1,11 +1,15 @@
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from __future__ import annotations
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import aiohttp
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from . import entities
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from ...core import app
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from . import token
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from . import token, api
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from .apis import chatcmpl
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FETCH_MODEL_LIST_URL = "https://api.qchatgpt.rockchin.top/api/v2/fetch/model_list"
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class ModelManager:
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"""模型管理器"""
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@ -13,10 +17,16 @@ class ModelManager:
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ap: app.Application
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model_list: list[entities.LLMModelInfo]
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requesters: dict[str, api.LLMAPIRequester]
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token_mgrs: dict[str, token.TokenManager]
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def __init__(self, ap: app.Application):
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self.ap = ap
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self.model_list = []
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self.requesters = {}
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self.token_mgrs = {}
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async def get_model_by_name(self, name: str) -> entities.LLMModelInfo:
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"""通过名称获取模型
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@ -24,171 +34,73 @@ class ModelManager:
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for model in self.model_list:
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if model.name == name:
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return model
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raise ValueError(f"不支持模型: {name} , 请检查配置文件")
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raise ValueError(f"无法确定模型 {name} 的信息，请在元数据中配置")
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async def initialize(self):
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openai_chat_completion = chatcmpl.OpenAIChatCompletion(self.ap)
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await openai_chat_completion.initialize()
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openai_token_mgr = token.TokenManager("openai", list(self.ap.provider_cfg.data['openai-config']['api-keys']))
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# 初始化token_mgr, requester
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self.token_mgrs = {
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"openai": token.TokenManager("openai", list(self.ap.provider_cfg.data['openai-config']['api-keys']))
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}
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model_list = [
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entities.LLMModelInfo(
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name="gpt-3.5-turbo",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-3.5-turbo-1106",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-3.5-turbo-16k",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-3.5-turbo-0613",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-3.5-turbo-16k-0613",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-3.5-turbo-0301",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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)
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]
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for api_cls in api.preregistered_requesters:
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api_inst = api_cls(self.ap)
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await api_inst.initialize()
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self.requesters[api_inst.name] = api_inst
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self.model_list.extend(model_list)
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# 尝试从api获取最新的模型信息
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try:
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async with aiohttp.ClientSession() as session:
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async with session.request(
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method="GET",
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url=FETCH_MODEL_LIST_URL,
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) as resp:
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model_list = (await resp.json())['data']['list']
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gpt4_model_list = [
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entities.LLMModelInfo(
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name="gpt-4-0125-preview",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-4-turbo-preview",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-4-1106-preview",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-4-vision-preview",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-4",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-4-0613",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-4-32k",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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),
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entities.LLMModelInfo(
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name="gpt-4-32k-0613",
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=True,
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)
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]
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for model in model_list:
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self.model_list.extend(gpt4_model_list)
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for index, local_model in enumerate(self.ap.llm_models_meta.data['list']):
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if model['name'] == local_model['name']:
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self.ap.llm_models_meta.data['list'][index] = model
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break
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else:
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self.ap.llm_models_meta.data['list'].append(model)
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one_api_model_list = [
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entities.LLMModelInfo(
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name="OneAPI/SparkDesk",
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model_name='SparkDesk',
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=False,
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),
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entities.LLMModelInfo(
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name="OneAPI/chatglm_pro",
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model_name='chatglm_pro',
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=False,
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),
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entities.LLMModelInfo(
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name="OneAPI/chatglm_std",
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model_name='chatglm_std',
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=False,
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),
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entities.LLMModelInfo(
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name="OneAPI/chatglm_lite",
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model_name='chatglm_lite',
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=False,
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),
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entities.LLMModelInfo(
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name="OneAPI/qwen-v1",
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model_name='qwen-v1',
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=False,
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),
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entities.LLMModelInfo(
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name="OneAPI/qwen-plus-v1",
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model_name='qwen-plus-v1',
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=False,
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),
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entities.LLMModelInfo(
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name="OneAPI/ERNIE-Bot",
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model_name='ERNIE-Bot',
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=False,
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),
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entities.LLMModelInfo(
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name="OneAPI/ERNIE-Bot-turbo",
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model_name='ERNIE-Bot-turbo',
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=False,
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),
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entities.LLMModelInfo(
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name="OneAPI/gemini-pro",
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model_name='gemini-pro',
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token_mgr=openai_token_mgr,
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requester=openai_chat_completion,
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tool_call_supported=False,
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),
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]
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await self.ap.llm_models_meta.dump_config()
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self.model_list.extend(one_api_model_list)
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except Exception as e:
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self.ap.logger.debug(f'获取最新模型列表失败: {e}')
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default_model_info: entities.LLMModelInfo = None
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for model in self.ap.llm_models_meta.data['list']:
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if model['name'] == 'default':
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default_model_info = entities.LLMModelInfo(
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name=model['name'],
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model_name=None,
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token_mgr=self.token_mgrs[model['token_mgr']],
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requester=self.requesters[model['requester']],
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tool_call_supported=model['tool_call_supported']
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)
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break
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for model in self.ap.llm_models_meta.data['list']:
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try:
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model_name = model.get('model_name', default_model_info.model_name)
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token_mgr = self.token_mgrs[model['token_mgr']] if 'token_mgr' in model else default_model_info.token_mgr
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requester = self.requesters[model['requester']] if 'requester' in model else default_model_info.requester
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tool_call_supported = model.get('tool_call_supported', default_model_info.tool_call_supported)
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model_info = entities.LLMModelInfo(
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name=model['name'],
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model_name=model_name,
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token_mgr=token_mgr,
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requester=requester,
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tool_call_supported=tool_call_supported
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)
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self.model_list.append(model_info)
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except Exception as e:
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self.ap.logger.error(f"初始化模型 {model['name']} 失败: {e} ,请检查配置文件")
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34
templates/metadata/llm-models.json
Normal file
34
templates/metadata/llm-models.json
Normal file
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@ -0,0 +1,34 @@
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{
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"list": [
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{
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"name": "default",
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"requester": "openai-chat-completion",
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"token_mgr": "openai",
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"tool_call_supported": false
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},
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{
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"name": "gpt-3.5-turbo",
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"tool_call_supported": true
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},
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{
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"name": "gpt-4",
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"tool_call_supported": true
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},
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{
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"name": "gpt-4-turbo-preview",
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"tool_call_supported": true
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},
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{
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"name": "gpt-4-32k",
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"tool_call_supported": true
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},
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{
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"model_name": "SparkDesk",
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"name": "OneAPI/SparkDesk"
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},
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{
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"model_name": "gemini-pro",
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"name": "OneAPI/gemini-pro"
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}
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]
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}
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