test: unskip qa_headless.py linter tests (#1041)

backend/llm/qa_headless.py

@@ -6,7 +6,7 @@ from uuid import UUID
 from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler
 from langchain.chains import LLMChain
 from langchain.chat_models import ChatOpenAI
-from langchain.llms.base import BaseLLM
+from langchain.chat_models.base import BaseChatModel
 from langchain.prompts.chat import (
     ChatPromptTemplate,
     HumanMessagePromptTemplate,
@@ -33,15 +33,15 @@ SYSTEM_MESSAGE = "Your name is Quivr. You're a helpful assistant. If you don't k
 
 
 class HeadlessQA(BaseModel):
-    model: str = None  # type: ignore
+    model: str
     temperature: float = 0.0
     max_tokens: int = 256
-    user_openai_api_key: str = None  # type: ignore
-    openai_api_key: str = None  # type: ignore
+    user_openai_api_key: Optional[str] = None
+    openai_api_key: Optional[str] = None
     streaming: bool = False
-    chat_id: str = None  # type: ignore
-    callbacks: List[AsyncIteratorCallbackHandler] = None  # type: ignore
-    prompt_id: Optional[UUID]
+    chat_id: str
+    callbacks: Optional[List[AsyncIteratorCallbackHandler]] = None
+    prompt_id: Optional[UUID] = None
 
     def _determine_api_key(self, openai_api_key, user_openai_api_key):
         """If user provided an API key, use it."""
@@ -50,31 +50,28 @@ class HeadlessQA(BaseModel):
         else:
             return openai_api_key
 
-    def _determine_streaming(self, model: str, streaming: bool) -> bool:
+    def _determine_streaming(self, streaming: bool) -> bool:
         """If the model name allows for streaming and streaming is declared, set streaming to True."""
         return streaming
 
     def _determine_callback_array(
         self, streaming
-    ) -> List[AsyncIteratorCallbackHandler]:  # pyright: ignore reportPrivateUsage=none
+    ) -> List[AsyncIteratorCallbackHandler]:
         """If streaming is set, set the AsyncIteratorCallbackHandler as the only callback."""
         if streaming:
-            return [
-                AsyncIteratorCallbackHandler()  # pyright: ignore reportPrivateUsage=none
-            ]
+            return [AsyncIteratorCallbackHandler()]
+        else:
+            return []
 
     def __init__(self, **data):
         super().__init__(**data)
+        print("in HeadlessQA")
 
         self.openai_api_key = self._determine_api_key(
             self.openai_api_key, self.user_openai_api_key
         )
-        self.streaming = self._determine_streaming(
-            self.model, self.streaming
-        )  # pyright: ignore reportPrivateUsage=none
-        self.callbacks = self._determine_callback_array(
-            self.streaming
-        )  # pyright: ignore reportPrivateUsage=none
+        self.streaming = self._determine_streaming(self.streaming)
+        self.callbacks = self._determine_callback_array(self.streaming)
 
     @property
     def prompt_to_use(self) -> Optional[Prompt]:
@@ -86,7 +83,7 @@ class HeadlessQA(BaseModel):
 
     def _create_llm(
         self, model, temperature=0, streaming=False, callbacks=None
-    ) -> BaseLLM:
+    ) -> BaseChatModel:
         """
         Determine the language model to be used.
         :param model: Language model name to be used.
@@ -101,7 +98,7 @@ class HeadlessQA(BaseModel):
             verbose=True,
             callbacks=callbacks,
             openai_api_key=self.openai_api_key,
-        )  # pyright: ignore reportPrivateUsage=none
+        )
 
     def _create_prompt_template(self):
         messages = [
@@ -124,9 +121,7 @@ class HeadlessQA(BaseModel):
         answering_llm = self._create_llm(
             model=self.model, streaming=False, callbacks=self.callbacks
         )
-        model_prediction = answering_llm.predict_messages(
-            messages  # pyright: ignore reportPrivateUsage=none
-        )
+        model_prediction = answering_llm.predict_messages(messages)
         answer = model_prediction.content
 
         new_chat = update_chat_history(

backend/repository/chat/format_chat_history.py

@@ -1,5 +1,6 @@
 from typing import List, Tuple
-from langchain.schema import AIMessage, HumanMessage, SystemMessage
+
+from langchain.schema import AIMessage, BaseMessage, HumanMessage, SystemMessage
 
 
 def format_chat_history(history) -> List[Tuple[str, str]]:
@@ -8,7 +9,9 @@ def format_chat_history(history) -> List[Tuple[str, str]]:
     return [(chat.user_message, chat.assistant) for chat in history]
 
 
-def format_history_to_openai_mesages(tuple_history: List[Tuple[str, str]], system_message: str, question: str) -> List[SystemMessage | HumanMessage | AIMessage]:
+def format_history_to_openai_mesages(
+    tuple_history: List[Tuple[str, str]], system_message: str, question: str
+) -> List[BaseMessage]:
     """Format the chat history into a list of Base Messages"""
     messages = []
     messages.append(SystemMessage(content=system_message))

backend/routes/chat_routes.py

@@ -220,7 +220,7 @@ async def create_question_handler(
                 model=chat_question.model,
                 temperature=chat_question.temperature,
                 max_tokens=chat_question.max_tokens,
-                user_openai_api_key=current_user.openai_api_key,  # pyright: ignore reportPrivateUsage=none
+                user_openai_api_key=current_user.openai_api_key,
                 chat_id=str(chat_id),
                 prompt_id=chat_question.prompt_id,
             )