From 5d811246bae2c33e1ac8b38ebf0dc9fb7017bc70 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Kaan=20=C3=87ayl=C4=B1?=
 <38523756+kaancayli@users.noreply.github.com>
Date: Tue, 12 Nov 2024 00:45:54 +0100
Subject: [PATCH] fix linting

---
 app/llm/external/ollama.py                       |  9 +++++----
 app/llm/external/openai_chat.py                  |  2 +-
 app/llm/request_handler/basic_request_handler.py |  3 ++-
 .../capability_request_handler.py                | 16 +++++++++-------
 4 files changed, 17 insertions(+), 13 deletions(-)

diff --git a/app/llm/external/ollama.py b/app/llm/external/ollama.py
index 01e8d0e1..8739262a 100644
--- a/app/llm/external/ollama.py
+++ b/app/llm/external/ollama.py
@@ -138,19 +138,20 @@ def embed(self, text: str) -> list[float]:
         return list(response)
 
     """Bind tools to the language model for function calling capabilities.
-    
+
     Note: Tool binding is currently not supported in Ollama models. This feature
     is only available for OpenAI models.
-    
+
     Args:
         tools: A sequence of tools to bind to the model.
-    
+
     Returns:
         A runnable that can process inputs with the bound tools.
-    
+
     Raises:
         NotImplementedError: Always raised as Ollama does not support tool binding.
     """
+
     # TODO: Implement tool binding support for Ollama models
     def bind_tools(
         self,
diff --git a/app/llm/external/openai_chat.py b/app/llm/external/openai_chat.py
index 1b5e475a..e7197314 100644
--- a/app/llm/external/openai_chat.py
+++ b/app/llm/external/openai_chat.py
@@ -79,7 +79,7 @@ def create_openai_tool_calls(tool_calls):
     ]
 
 
-def convert_to_openai_messages(
+def convert_to_open_ai_messages(
     messages: list[PyrisMessage],
 ) -> list[ChatCompletionMessageParam]:
     """
diff --git a/app/llm/request_handler/basic_request_handler.py b/app/llm/request_handler/basic_request_handler.py
index dafb31a5..0c1c835a 100644
--- a/app/llm/request_handler/basic_request_handler.py
+++ b/app/llm/request_handler/basic_request_handler.py
@@ -46,7 +46,8 @@ def bind_tools(
         Binds a sequence of tools to the language model.
 
         Args:
-            tools (Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]]): A sequence of tools to be bound.
+            tools (Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]]):
+            A sequence of tools to be bound.
 
         Returns:
             LanguageModel: The language model with tools bound.
diff --git a/app/llm/request_handler/capability_request_handler.py b/app/llm/request_handler/capability_request_handler.py
index fb04707d..cd8c876b 100644
--- a/app/llm/request_handler/capability_request_handler.py
+++ b/app/llm/request_handler/capability_request_handler.py
@@ -79,27 +79,29 @@ def bind_tools(
         tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
     ) -> LanguageModel:
         """Bind the provided tools to the selected ChatModel.
-        
+
         Args:
             tools: A sequence of tools to bind. Can be one of:
                 - Dict[str, Any]: Tool configuration dictionary
                 - Type[BaseModel]: Pydantic model class
                 - Callable: Function to be used as a tool
                 - BaseTool: LangChain tool instance
-        
+
         Returns:
             LanguageModel: The selected chat model with tools bound
-            
+
         Raises:
             ValueError: If tools sequence is empty or contains unsupported tool types
             TypeError: If selected model doesn't support tool binding
         """
         if not tools:
             raise ValueError("Tools sequence cannot be empty")
-            
+
         llm = self._select_model(ChatModel)
-        if not hasattr(llm, 'bind_tools'):
-            raise TypeError(f"Selected model {llm.description} doesn't support tool binding")
-            
+        if not hasattr(llm, "bind_tools"):
+            raise TypeError(
+                f"Selected model {llm.description} doesn't support tool binding"
+            )
+
         llm.bind_tools(tools)
         return llm