Apply autoformatter

app/llm/basic_request_handler.py

@@ -15,7 +15,9 @@ def complete(self, prompt: str, arguments: CompletionArguments) -> str:
         llm = self.llm_manager.get_by_id(self.model_id)
         return llm.complete(prompt, arguments)
 
-    def chat(self, messages: list[IrisMessage], arguments: CompletionArguments) -> IrisMessage:
+    def chat(
+        self, messages: list[IrisMessage], arguments: CompletionArguments
+    ) -> IrisMessage:
         llm = self.llm_manager.get_by_id(self.model_id)
         return llm.chat(messages, arguments)
 

app/llm/langchain/iris_langchain_completion_model.py

@@ -26,9 +26,7 @@ def _generate(
         generations = []
         args = CompletionArguments(stop=stop)
         for prompt in prompts:
-            completion = self.request_handler.complete(
-                prompt=prompt, arguments=args
-            )
+            completion = self.request_handler.complete(prompt=prompt, arguments=args)
             generations.append([Generation(text=completion)])
         return LLMResult(generations=generations)
 

app/llm/llm_manager.py

@@ -21,9 +21,7 @@ def load_llms() -> dict[str, LanguageModel]:
         yaml_dict = yaml.safe_load(file)
 
     llms = LLMList.model_validate({"llms": yaml_dict}).llms
-    return {
-        llm.id: llm for llm in llms
-    }
+    return {llm.id: llm for llm in llms}
 
 
 class LlmManager(metaclass=Singleton):

app/llm/request_handler_interface.py

@@ -10,9 +10,12 @@ class RequestHandler(metaclass=ABCMeta):
     @classmethod
     def __subclasshook__(cls, subclass) -> bool:
         return (
-                hasattr(subclass, "complete") and callable(subclass.complete)
-                and hasattr(subclass, "chat") and callable(subclass.chat)
-                and hasattr(subclass, "embed") and callable(subclass.embed)
+            hasattr(subclass, "complete")
+            and callable(subclass.complete)
+            and hasattr(subclass, "chat")
+            and callable(subclass.chat)
+            and hasattr(subclass, "embed")
+            and callable(subclass.embed)
         )
 
     @abstractmethod

app/llm/wrapper/__init__.py

@@ -1,15 +1,21 @@
 from llm.wrapper.model import LanguageModel
-from llm.wrapper.openai_completion import NativeOpenAICompletionModel, AzureOpenAICompletionModel
+from llm.wrapper.openai_completion import (
+    NativeOpenAICompletionModel,
+    AzureOpenAICompletionModel,
+)
 from llm.wrapper.openai_chat import NativeOpenAIChatModel, AzureOpenAIChatModel
-from llm.wrapper.openai_embeddings import NativeOpenAIEmbeddingModel, AzureOpenAIEmbeddingModel
+from llm.wrapper.openai_embeddings import (
+    NativeOpenAIEmbeddingModel,
+    AzureOpenAIEmbeddingModel,
+)
 from llm.wrapper.ollama import OllamaModel
 
 type AnyLLM = (
-        NativeOpenAICompletionModel
-        | AzureOpenAICompletionModel
-        | NativeOpenAIChatModel
-        | AzureOpenAIChatModel
-        | NativeOpenAIEmbeddingModel
-        | AzureOpenAIEmbeddingModel
-        | OllamaModel
+    NativeOpenAICompletionModel
+    | AzureOpenAICompletionModel
+    | NativeOpenAIChatModel
+    | AzureOpenAIChatModel
+    | NativeOpenAIEmbeddingModel
+    | AzureOpenAIEmbeddingModel
+    | OllamaModel
 )

app/llm/wrapper/model.py

@@ -23,7 +23,9 @@ def __subclasshook__(cls, subclass) -> bool:
     @abstractmethod
     def complete(self, prompt: str, arguments: CompletionArguments) -> str:
         """Create a completion from the prompt"""
-        raise NotImplementedError(f"The LLM {self.__str__()} does not support completion")
+        raise NotImplementedError(
+            f"The LLM {self.__str__()} does not support completion"
+        )
 
 
 class ChatModel(LanguageModel, metaclass=ABCMeta):
@@ -34,9 +36,13 @@ def __subclasshook__(cls, subclass) -> bool:
         return hasattr(subclass, "chat") and callable(subclass.chat)
 
     @abstractmethod
-    def chat(self, messages: list[IrisMessage], arguments: CompletionArguments) -> IrisMessage:
+    def chat(
+        self, messages: list[IrisMessage], arguments: CompletionArguments
+    ) -> IrisMessage:
         """Create a completion from the chat messages"""
-        raise NotImplementedError(f"The LLM {self.__str__()} does not support chat completion")
+        raise NotImplementedError(
+            f"The LLM {self.__str__()} does not support chat completion"
+        )
 
 
 class EmbeddingModel(LanguageModel, metaclass=ABCMeta):
@@ -49,4 +55,6 @@ def __subclasshook__(cls, subclass) -> bool:
     @abstractmethod
     def embed(self, text: str) -> list[float]:
         """Create an embedding from the text"""
-        raise NotImplementedError(f"The LLM {self.__str__()} does not support embeddings")
+        raise NotImplementedError(
+            f"The LLM {self.__str__()} does not support embeddings"
+        )

app/llm/wrapper/ollama.py

@@ -34,8 +34,12 @@ def complete(self, prompt: str, arguments: CompletionArguments) -> str:
         response = self._client.generate(model=self.model, prompt=prompt)
         return response["response"]
 
-    def chat(self, messages: list[IrisMessage], arguments: CompletionArguments) -> IrisMessage:
-        response = self._client.chat(model=self.model, messages=convert_to_ollama_messages(messages))
+    def chat(
+        self, messages: list[IrisMessage], arguments: CompletionArguments
+    ) -> IrisMessage:
+        response = self._client.chat(
+            model=self.model, messages=convert_to_ollama_messages(messages)
+        )
         return convert_to_iris_message(response["message"])
 
     def embed(self, text: str) -> list[float]:

app/llm/wrapper/openai_chat.py

@@ -9,8 +9,12 @@
 from llm.wrapper.model import ChatModel
 
 
-def convert_to_open_ai_messages(messages: list[IrisMessage]) -> list[ChatCompletionMessageParam]:
-    return [{"role": message.role.value, "content": message.text} for message in messages]
+def convert_to_open_ai_messages(
+    messages: list[IrisMessage],
+) -> list[ChatCompletionMessageParam]:
+    return [
+        {"role": message.role.value, "content": message.text} for message in messages
+    ]
 
 
 def convert_to_iris_message(message: ChatCompletionMessage) -> IrisMessage:
@@ -24,7 +28,9 @@ class OpenAIChatModel(ChatModel):
     api_key: str
     _client: OpenAI
 
-    def chat(self, messages: list[IrisMessage], arguments: CompletionArguments) -> IrisMessage:
+    def chat(
+        self, messages: list[IrisMessage], arguments: CompletionArguments
+    ) -> IrisMessage:
         response = self._client.chat.completions.create(
             model=self.model,
             messages=convert_to_open_ai_messages(messages),