fix: improve usage of llm config

libro-ai/src/libro_ai/chat/__init__.py

@@ -1,19 +1,29 @@
-from .tongyi import TongyiChatObjectProvider
+from .tongyi_chat_provider import TongyiChatObjectProvider
 from .debug_provider import DebugChatObjectProvider
 from .executor import LLMChat, ChatExecutor
-from .openai import OpenAIChatObjectProvider
+from .openai_chat_provider import OpenAIChatObjectProvider
 from .object_manager import ChatObjectManager
 from .source import CHAT_SOURCE
 from .object import ChatObject, ChatObjectProvider
 from .langchain_variable import LangChainVariableChatObjectProvider
 from .chat_record import ChatMessage, ChatRecord, ChatRecordProvider
-from .utils import get_message_str
+from libro_core.config import libro_config
 
 chat_object_manager = ChatObjectManager()
-chat_object_manager.register_provider(OpenAIChatObjectProvider())
-chat_object_manager.register_provider(TongyiChatObjectProvider())
+libro_ai_config = libro_config.get_config().get("llm")
+model_type = []
+api_key:str = None
+if libro_ai_config is not None:
+    if tongyi_api_key := libro_ai_config.get("DASHSCOPE_API_KEY"):
+        api_key = tongyi_api_key
+        chat_object_manager.register_provider(TongyiChatObjectProvider(api_key = api_key))
+        model_type.append('tongyi')
+    if openai_api_key := libro_ai_config.get("OPENAI_API_KEY"):
+        api_key = openai_api_key
+        chat_object_manager.register_provider(OpenAIChatObjectProvider(api_key = api_key))
+        model_type.append('openai')
 chat_object_manager.register_provider(LangChainVariableChatObjectProvider())
-chat_object_manager.register_provider(DebugChatObjectProvider())
+chat_object_manager.register_provider(DebugChatObjectProvider(model_type = model_type,api_key = api_key))
 
 chat_record_provider = ChatRecordProvider()
 

libro-ai/src/libro_ai/chat/debug_executor.py

@@ -1,4 +1,6 @@
+from typing import List, Union
 from langchain_openai import ChatOpenAI
+from langchain_community.chat_models.tongyi import ChatTongyi
 from langchain.callbacks import get_openai_callback
 from .executor import LLMChat
 from ..utils import is_langchain_installed
@@ -9,21 +11,32 @@
 from libro_core.config import libro_config
 from pydantic import Field
 
+OPENAI = ['text-davinci-003',"gpt-4","gpt-4o","gpt-3.5-turbo"]
+TONGYI = ["qwen-max","qwen-plus","qwen-turbo"]
+
 class DebugChat(LLMChat):
     name: str = "debug"
     model: str = Field(default="gpt-4o")
-    system_message:SystemMessage = SystemMessage(content="你是一个代码调试小助手，在 notebook 执行时，输出了一些报错信息，请尝试解释报错并给出解决方案，每次对话都会给出代码以及报错信息")
-    chat: ChatOpenAI = None
+    system_message: SystemMessage = SystemMessage(content="你是一个代码调试小助手，在 notebook 执行时，输出了一些报错信息，请尝试解释报错并给出解决方案，每次对话都会给出代码以及报错信息")
+    chat: Union[ChatOpenAI, ChatTongyi] = None
+    api_key: str = None
+    model_type: List[str] = ['openai']
 
     def load(self):
         if is_langchain_installed():
             extra_params = {}
-            libro_ai_config = libro_config.get_config().get("llm")
-            if libro_ai_config is not None:
-                if api_key := libro_ai_config.get("OPENAI_API_KEY"):
-                    extra_params["api_key"] = api_key
-
-            self.chat = ChatOpenAI(model_name=self.model,**extra_params)
+            config = libro_config.get_config().get('llm')
+            if config is not None:
+                default_model = config.get("default_model")
+                self.model = default_model
+                if default_model in OPENAI:
+                    if api_key := config.get("OPENAI_API_KEY"):
+                        extra_params["api_key"] = api_key
+                        self.chat = ChatOpenAI(model_name=self.model,**extra_params)
+                elif default_model in TONGYI:
+                    if api_key := config.get("DASHSCOPE_API_KEY"):
+                        extra_params["api_key"] = api_key
+                        self.chat = ChatTongyi(model_name=self.model,**extra_params)
             return True
         return False
 

libro-ai/src/libro_ai/chat/debug_provider.py

@@ -13,13 +13,15 @@ class DebugChatObjectProvider(ChatObjectProvider):
     name: str = "debug"
     cache: Dict[str, ChatExecutor] = {}
     LLMs: List[str] = ["gpt-3.5-turbo", "gpt-4"]
-
+    model_type: List[str] = ['openai']
+    is_system_provider: bool = True
+
     def get_or_create_executor(self, name: str) -> ChatExecutor:
         if name in self.cache:
             return self.cache[name]
         from .debug_executor import DebugChat
 
-        executor = DebugChat(model=name, name=name)
+        executor = DebugChat(model=name, name=name, model_type = self.model_type, api_key=self.api_key)
         if executor.load():
             self.cache[name] = executor
         return executor  
@@ -31,7 +33,7 @@ def list(self) -> List[ChatObject]:
             *list(
                 map(
                     lambda n: ChatObject(
-                        name=MODEL_NAME_ALIASES.get(n, n),
+                        name='debug',
                         to_executor=lambda: self.get_or_create_executor(n),
                         type=CHAT_SOURCE["LLM"],
                     ),

libro-ai/src/libro_ai/chat/object.py

@@ -26,6 +26,7 @@ def model_dump(self):
 class ChatObjectProvider(BaseModel, ABC):
     name: str = "custom"
     is_system_provider: bool = False
+    api_key: str = None
 
     @abstractmethod
     def list(self) -> List[ChatObject]:

libro-ai/src/libro_ai/chat/object_manager.py

@@ -4,7 +4,7 @@
 from pydantic import BaseModel
 from .object import ChatObject, ChatObjectProvider
 from .executor import ChatExecutor
-
+from .utils import MODEL_NAME_ALIASES
 
 class ChatObjectManager(BaseModel):
     providers: List[ChatObjectProvider] = []
@@ -41,6 +41,15 @@ def get_executor(self, key: str) -> ChatExecutor:
                 return executor
         raise Exception(f"Executor {key} not found")
 
+    def get_key(self, model_name: str):
+        dict = self.get_object_dict()
+        model_alias_name = MODEL_NAME_ALIASES.get(model_name)
+        if model_alias_name is not None:
+            for value in dict.values():
+                if value.name == model_alias_name:
+                    return value.key
+        return None
+
     def get_object_dict(self) -> Dict[str, ChatObject]:
         chat_objects: Dict[str, ChatObject] = {}
         for provider in self.providers:

libro-ai/src/libro_ai/chat/openai_chat_executor.py

@@ -15,14 +15,13 @@ class OpenAIChat(LLMChat):
     name: str = "chatgpt"
     model: str = Field(default="gpt-3.5-turbo")
     chat: ChatOpenAI = None
+    api_key: str = None
 
     def load(self):
         if is_langchain_installed():
             extra_params = {}
-            libro_ai_config = libro_config.get_config().get("llm")
-            if libro_ai_config is not None:
-                if api_key := libro_ai_config.get("OPENAI_API_KEY"):
-                    extra_params["api_key"] = api_key
+            if self.api_key:
+                extra_params["api_key"] = self.api_key
             self.chat = ChatOpenAI(model_name=self.model, **extra_params)
             return True
         return False
@@ -79,10 +78,14 @@ class DalleChat(LLMChat):
     name: str = "dalle-3"
     model: str = Field(default="dall-e-3")
     dalle: DallEAPIWrapper = None
+    api_key:str = None
 
     def load(self):
+        extra_params = {}
         if is_langchain_installed():
-            self.dalle = DallEAPIWrapper()
+            if self.api_key:
+                extra_params["api_key"] = self.api_key
+            self.dalle = DallEAPIWrapper(**extra_params)
             self.dalle.model_name = self.model
             return True
         return False

libro-ai/src/libro_ai/chat/openai.py → libro-ai/src/libro_ai/chat/openai_chat_provider.py

@@ -5,22 +5,7 @@
 from .executor import ChatExecutor
 from .object import ChatObject, ChatObjectProvider
 from ..utils import is_langchain_installed
-
-MODEL_NAME_ALIASES = {
-    "text-davinci-003": "gpt3",
-    "gpt-3.5-turbo": "chatgpt",
-    "gpt-4": "gpt4",
-    "dall-e-3": "dalle-3",
-    "dall-e-2": "dalle-2",
-}
-
-ALIASE_NAME_MODEL = {
-    "gpt3": "text-davinci-003",
-    "chatgpt": "gpt-3.5-turbo",
-    "gpt4": "gpt-4",
-    "dalle-3": "dall-e-3",
-    "dalle-2": "dall-e-2",
-}
+from .utils import ALIASE_NAME_MODEL, MODEL_NAME_ALIASES
 
 
 class OpenAIChatObjectProvider(ChatObjectProvider):
@@ -35,7 +20,7 @@ def get_or_create_executor(self, name: str) -> ChatExecutor:
             return self.cache[model]
         from .openai_chat_executor import OpenAIChat
 
-        executor = OpenAIChat(model=model, name=name)
+        executor = OpenAIChat(model=model, name=name, api_key=self.api_key)
         if executor.load():
             self.cache[model] = executor
         return executor
@@ -46,7 +31,7 @@ def get_or_create_lmm_executor(self, name: str) -> ChatExecutor:
             return self.cache[model]
         from .openai_chat_executor import DalleChat
 
-        executor = DalleChat(model=model, name=name)
+        executor = DalleChat(model=model, name=name,api_key=self.api_key)
         if executor.load():
             self.cache[model] = executor
         return executor

libro-ai/src/libro_ai/chat/tongyi_executor.py → libro-ai/src/libro_ai/chat/tongyi_chat_executor.py

@@ -13,14 +13,13 @@ class TongyiChat(LLMChat):
     name: str = "tongyi"
     model: str = Field(default="qwen-max")
     chat: ChatTongyi = None
+    api_key: str = None
 
     def load(self):
         if is_langchain_installed():
             extra_params = {}
-            libro_ai_config = libro_config.get_config().get("llm")
-            if libro_ai_config is not None:
-                if api_key := libro_ai_config.get("DASHSCOPE_API_KEY"):
-                    extra_params["api_key"] = api_key
+            if self.api_key:
+                extra_params["api_key"] = self.api_key
             self.chat = ChatTongyi(model_name=self.model, **extra_params)
             return True
         return False
@@ -47,6 +46,7 @@ def run(self, value:StringPromptValue,stream=False, sync=True, system_prompt = N
                     result = chat.astream(input, streaming = True, **kwargs)
                     return result
             except Exception as e:
+
                 return ""
         else:
             try:

libro-ai/src/libro_ai/chat/tongyi.py → libro-ai/src/libro_ai/chat/tongyi_chat_provider.py

@@ -5,19 +5,7 @@
 from .executor import ChatExecutor
 from .object import ChatObject, ChatObjectProvider
 from ..utils import is_langchain_installed
-
-MODEL_NAME_ALIASES = {
-    "qwen-max": "qwen-max",
-    "qwen-plus": "qwen-plus",
-    "qwen-turbo": "qwen-turbo",
-}
-
-ALIASE_NAME_MODEL = {
-    "qwen-max": "qwen-max",
-    "qwen-plus": "qwen-plus",
-    "qwen-turbo": "qwen-turbo",
-}
-
+from .utils import ALIASE_NAME_MODEL, MODEL_NAME_ALIASES
 
 class TongyiChatObjectProvider(ChatObjectProvider):
     name: str = "tongyi"
@@ -29,9 +17,9 @@ def get_or_create_executor(self, name: str) -> ChatExecutor:
         model = ALIASE_NAME_MODEL.get(name, name)
         if model in self.cache:
             return self.cache[model]
-        from .tongyi_executor import TongyiChat
+        from .tongyi_chat_executor import TongyiChat
 
-        executor = TongyiChat(model=model, name=name)
+        executor = TongyiChat(model=model, name=name,api_key=self.api_key)
         if executor.load():
             self.cache[model] = executor
         return executor

libro-ai/src/libro_ai/chat/utils.py

@@ -2,6 +2,27 @@
 from langchain.schema.messages import BaseMessage
 from langchain_core.prompt_values import StringPromptValue
 
+MODEL_NAME_ALIASES = {
+    "text-davinci-003": "gpt3",
+    "gpt-3.5-turbo": "chatgpt",
+    "gpt-4": "gpt4",
+    "dall-e-3": "dalle-3",
+    "dall-e-2": "dalle-2",
+    "qwen-max": "qwen-max",
+    "qwen-plus": "qwen-plus",
+    "qwen-turbo": "qwen-turbo",
+}
+
+ALIASE_NAME_MODEL = {
+    "gpt3": "text-davinci-003",
+    "chatgpt": "gpt-3.5-turbo",
+    "gpt4": "gpt-4",
+    "dalle-3": "dall-e-3",
+    "dalle-2": "dall-e-2",
+    "qwen-max": "qwen-max",
+    "qwen-plus": "qwen-plus",
+    "qwen-turbo": "qwen-turbo",
+}
 
 def get_message_str(message: Union[StringPromptValue, BaseMessage, List[BaseMessage]]):
     if isinstance(message, list):

libro-server/src/libro_server/libro_ai_handler.py

@@ -32,10 +32,8 @@ async def post(self):
         config = libro_config.get_config().get('llm')
         if chat_key is None or chat_key == "":
             if config is not None:
-                model = config.get("model")
-                model_type = config.get("model_type")
-                if model is not None and model_type is not None:
-                    chat_key = model_type + ":"+ model
+                model = config.get("default_model")
+                chat_key = chat_object_manager.get_key(model)
         prompt: str = data.get("prompt")
         system_prompt: str = data.get("system_prompt")
         if (
@@ -87,10 +85,10 @@ def post(self):
         config = libro_config.get_config().get('llm')
         if chat_key is None or chat_key == "":
             if config is not None:
-                model = config.get("model")
-                model_type = config.get("model_type")
-                if model is not None and model_type is not None:
-                    chat_key = model_type + ":"+ model
+                model = config.get("default_model")
+                chat_key = chat_object_manager.get_key(model)
+                if chat_key is None:
+                    raise Exception("Invalid api key and default model!")
         prompt: str = data.get("prompt")
         system_prompt: str = data.get("system_prompt")
         # 流式输出响应