feat: support config temperature in bot

fix: lint

server/agent/bot/__init__.py

@@ -10,7 +10,12 @@ class Bot:
     def __init__(self, bot: BotModel, llm_token: LLMTokenLike):
         self._bot = bot
         self._llm_token = llm_token
-        self._llm = LLM(llm_token=llm_token)
+        self._llm = LLM(
+            llm_token=llm_token,
+            temperature=bot.temperature,
+            n=bot.n,
+            top_p=bot.top_p,
+        )
 
     @property
     def id(self):

server/agent/llm/__init__.py

@@ -3,55 +3,77 @@
 from typing import Dict, Optional, Type
 from agent.llm.base import BaseLLMClient
 
-class LLMTokenLike():
+
+class LLMTokenLike:
     token: str
     llm: str
 
-llm_client_registry: Dict[str, Type['BaseLLMClient']] = {}
+
+llm_client_registry: Dict[str, Type["BaseLLMClient"]] = {}
+
 
 def register_llm_client(name: str):
     """Decorator to register a new LLM client class."""
+
     def decorator(cls):
         if name in llm_client_registry:
             raise ValueError(f"Client '{name}' is already registered.")
         llm_client_registry[name] = cls
         return cls
+
     return decorator
 
+
 def get_registered_llm_client():
-   return llm_client_registry
+    return llm_client_registry
 
-def import_clients(directory: str = 'clients'):
+
+def import_clients(directory: str = "clients"):
     """Dynamically import all Python modules in the given directory."""
     # 获取当前文件的绝对路径
     current_dir = os.path.dirname(os.path.abspath(__file__))
     clients_dir = os.path.join(current_dir, directory)
-    
+
     for filename in os.listdir(clients_dir):
-        if filename.endswith('.py') and not filename.startswith('__'):
+        if filename.endswith(".py") and not filename.startswith("__"):
             module_name = f"agent.llm.{directory}.{filename[:-3]}"  # 去掉 .py 后缀
             importlib.import_module(module_name)
 
-class LLM():
-  llm_token: LLMTokenLike
-  client: Optional[BaseLLMClient]
-
-  def __init__(self, llm_token: LLMTokenLike):
-    self._llm_token = llm_token
-    self._client = self.get_llm_client(llm_token.llm, api_key=llm_token.token)
-
-  def get_llm_client(
-    self,
-    llm: str = 'openai',
-    api_key: Optional[str | None] = None,
-    temperature: Optional[int] = 0.2,
-    max_tokens: Optional[int] = 1500,
-    streaming: Optional[bool] = False
-  ) -> BaseLLMClient:
-
-    """Get an LLM client based on the specified name."""
-    if llm in llm_client_registry:
-        client_class = llm_client_registry[llm]
-        return client_class(temperature=temperature, api_key=api_key, streaming=streaming, max_tokens=max_tokens)
-
-    return None
+
+class LLM:
+    llm_token: LLMTokenLike
+    client: Optional[BaseLLMClient]
+
+    def __init__(
+            self,
+            llm_token: LLMTokenLike,
+            temperature: Optional[float] = 0.2,
+            n: Optional[int] = 1,
+            top_p: Optional[float] = None
+    ):
+        self._llm_token = llm_token
+        self._client = self.get_llm_client(llm_token.llm, api_key=llm_token.token, temperature=temperature, n=n, top_p=top_p)
+
+    def get_llm_client(
+        self,
+        llm: str = "openai",
+        api_key: Optional[str | None] = None,
+        temperature: Optional[float] = 0.2,
+        n: Optional[int] = 1,
+        top_p: Optional[float] = None,
+        max_tokens: Optional[int] = 1500,
+        streaming: Optional[bool] = False,
+    ) -> BaseLLMClient:
+        """Get an LLM client based on the specified name."""
+        if llm in llm_client_registry:
+            client_class = llm_client_registry[llm]
+            return client_class(
+                temperature=temperature,
+                n=n,
+                top_p=top_p,
+                api_key=api_key,
+                streaming=streaming,
+                max_tokens=max_tokens,
+            )
+
+        return None

server/agent/llm/base.py

@@ -1,29 +1,30 @@
-
 from abc import abstractmethod
 from typing import Any, Dict, List, Optional
 from langchain_core.language_models import BaseChatModel
 
 from petercat_utils.data_class import MessageContent
 
-class BaseLLMClient():
-  def __init__(self,
-               temperature: Optional[int] = 0.2,
-               max_tokens: Optional[int] = 1500,
-               streaming: Optional[bool] = False,
-               api_key: Optional[str] = ''
-              ):
-    pass
-
-  @abstractmethod
-  def get_client() -> BaseChatModel:
-    pass
-
-  @abstractmethod
-  def get_tools(self, tool: List[Any]) -> list[Dict[str, Any]]:
-    pass
-
-  @abstractmethod
-  def parse_content(self, content: List[MessageContent]) -> List[MessageContent]:
-    pass
-
 
+class BaseLLMClient:
+    def __init__(
+        self,
+        temperature: Optional[float] = 0.2,
+        n: Optional[int] = 1,
+        top_p: Optional[float] = None,
+        max_tokens: Optional[int] = 1500,
+        streaming: Optional[bool] = False,
+        api_key: Optional[str] = "",
+    ):
+        pass
+
+    @abstractmethod
+    def get_client() -> BaseChatModel:
+        pass
+
+    @abstractmethod
+    def get_tools(self, tool: List[Any]) -> list[Dict[str, Any]]:
+        pass
+
+    @abstractmethod
+    def parse_content(self, content: List[MessageContent]) -> List[MessageContent]:
+        pass

server/agent/llm/clients/gemini.py

@@ -10,38 +10,47 @@
 
 GEMINI_API_KEY = get_env_variable("GEMINI_API_KEY")
 
+
 def parse_gemini_input(message: MessageContent):
-  match message.type:
-    case "image_url": 
-      return ImageRawURLContentBlock(image_url=message.image_url.url, type="image_url")
-    case _:
-      return message
+    match message.type:
+        case "image_url":
+            return ImageRawURLContentBlock(
+                image_url=message.image_url.url, type="image_url"
+            )
+        case _:
+            return message
+
 
 @register_llm_client("gemini")
 class GeminiClient(BaseLLMClient):
-  _client: ChatOpenAI
-
-  def __init__(self,
-               temperature: Optional[int] = 0.2,
-               max_tokens: Optional[int] = 1500,
-               streaming: Optional[bool] = False,
-               api_key: Optional[str] = GEMINI_API_KEY,
-              ):
-    self._client = ChatGoogleGenerativeAI(
-        model="gemini-1.5-flash",
-        temperature=temperature,
-        streaming=streaming,
-        max_tokens=max_tokens,
-        google_api_key=api_key,
-    )
-
-  def get_client(self):
-    return self._client
-
-  def get_tools(self, tools: List[Any]):
-    return [convert_to_genai_function_declarations(tool) for tool in tools]
-
-  def parse_content(self, content: List[MessageContent]):
-    result = [parse_gemini_input(message=message) for message in content]
-    print(f"parse_content, content={content}, result={result}")
-    return result
+    _client: ChatOpenAI
+
+    def __init__(
+        self,
+        temperature: Optional[float] = 0.2,
+        n: Optional[int] = 1,
+        top_p: Optional[float] = None,
+        max_tokens: Optional[int] = 1500,
+        streaming: Optional[bool] = False,
+        api_key: Optional[str] = GEMINI_API_KEY,
+    ):
+        self._client = ChatGoogleGenerativeAI(
+            model="gemini-1.5-flash",
+            temperature=temperature,
+            top_p=top_p,
+            n=n,
+            streaming=streaming,
+            max_tokens=max_tokens,
+            google_api_key=api_key,
+        )
+
+    def get_client(self):
+        return self._client
+
+    def get_tools(self, tools: List[Any]):
+        return [convert_to_genai_function_declarations(tool) for tool in tools]
+
+    def parse_content(self, content: List[MessageContent]):
+        result = [parse_gemini_input(message=message) for message in content]
+        print(f"parse_content, content={content}, result={result}")
+        return result

server/agent/llm/clients/openai.py

@@ -17,14 +17,18 @@ class OpenAIClient(BaseLLMClient):
 
     def __init__(
         self,
-        temperature: Optional[int] = 0.2,
+        temperature: Optional[float] = 0.2,
+        n: Optional[int] = 1,
+        top_p: Optional[float] = None,
         max_tokens: Optional[int] = 1500,
         streaming: Optional[bool] = False,
         api_key: Optional[str] = OPEN_API_KEY,
     ):
         self._client = ChatOpenAI(
             model_name="gpt-4o",
             temperature=temperature,
+            n=n,
+            top_p=top_p,
             streaming=streaming,
             max_tokens=max_tokens,
             openai_api_key=api_key,

server/bot/builder.py

@@ -1,4 +1,3 @@
-import json
 from typing import List, Optional
 
 from github import Github

server/core/models/bot.py

@@ -16,6 +16,9 @@ class BotModel(BaseModel):
     token_id: Optional[str] = ""
     created_at: datetime = datetime.now()
     domain_whitelist: Optional[list[str]] = []
+    temperature: Optional[float] = 0.2
+    n: Optional[int] = 1
+    top_p: Optional[float]
 
 
 class RepoBindBotConfigVO(BaseModel):