Fix lint errors

Fix last lint error

Fix last lint errors

app/domain/data/token_usage_dto.py

@@ -7,4 +7,4 @@ class TokenUsageDTO(BaseModel):
     model_info: str
     num_input_tokens: int
     num_output_tokens: int
-    pipeline: PipelineEnum
+    pipeline: PipelineEnum

app/llm/external/LLMTokenCount.py

@@ -8,7 +8,13 @@ class LLMTokenCount:
     num_output_tokens: int
     pipeline: PipelineEnum
 
-    def __init__(self, model_info: str, num_input_tokens: int, num_output_tokens: int, pipeline: PipelineEnum):
+    def __init__(
+        self,
+        model_info: str,
+        num_input_tokens: int,
+        num_output_tokens: int,
+        pipeline: PipelineEnum,
+    ):
         self.model_info = model_info
         self.num_input_tokens = num_input_tokens
         self.num_output_tokens = num_output_tokens

app/llm/external/ollama.py

@@ -57,7 +57,9 @@ def convert_to_ollama_messages(messages: list[PyrisMessage]) -> list[Message]:
     return messages_to_return
 
 
-def convert_to_iris_message(message: Message, num_input_tokens: int, num_output_tokens: int, model: str) -> PyrisMessage:
+def convert_to_iris_message(
+    message: Message, num_input_tokens: int, num_output_tokens: int, model: str
+) -> PyrisMessage:
     """
     Convert a Message to a PyrisMessage
     """
@@ -111,7 +113,12 @@ def chat(
             format="json" if arguments.response_format == "JSON" else "",
             options=self.options,
         )
-        return convert_to_iris_message(response["message"], response["prompt_eval_count"], response["eval_count"], response["model"])
+        return convert_to_iris_message(
+            response["message"],
+            response["prompt_eval_count"],
+            response["eval_count"],
+            response["model"],
+        )
 
     def embed(self, text: str) -> list[float]:
         response = self._client.embeddings(

app/llm/external/openai_chat.py

@@ -62,23 +62,26 @@ def convert_to_open_ai_messages(
     return openai_messages
 
 
-def convert_to_iris_message(message: ChatCompletionMessage, usage: Optional[CompletionUsage], model: str) -> PyrisMessage:
+def convert_to_iris_message(
+    message: ChatCompletionMessage, usage: Optional[CompletionUsage], model: str
+) -> PyrisMessage:
     """
     Convert a ChatCompletionMessage to a PyrisMessage
     """
-    num_input_tokens = getattr(usage, 'prompt_tokens', -1)
-    num_output_tokens = getattr(usage, 'completion_tokens', -1)
+    num_input_tokens = getattr(usage, "prompt_tokens", -1)
+    num_output_tokens = getattr(usage, "completion_tokens", -1)
 
     message = PyrisMessage(
         sender=map_str_to_role(message.role),
         contents=[TextMessageContentDTO(textContent=message.content)],
         send_at=datetime.now(),
         num_input_tokens=num_input_tokens,
         num_output_tokens=num_output_tokens,
-        model_info=model
+        model_info=model,
     )
     return message
 
+
 class OpenAIChatModel(ChatModel):
     model: str
     api_key: str
@@ -110,7 +113,9 @@ def chat(
                         temperature=arguments.temperature,
                         max_tokens=arguments.max_tokens,
                     )
-                return convert_to_iris_message(response.choices[0].message, response.usage, response.model)
+                return convert_to_iris_message(
+                    response.choices[0].message, response.usage, response.model
+                )
             except Exception as e:
                 wait_time = initial_delay * (backoff_factor**attempt)
                 logging.warning(f"Exception on attempt {attempt + 1}: {e}")

app/llm/langchain/iris_langchain_chat_model.py

@@ -45,10 +45,12 @@ def _generate(
         iris_message = self.request_handler.chat(iris_messages, self.completion_args)
         base_message = convert_iris_message_to_langchain_message(iris_message)
         chat_generation = ChatGeneration(message=base_message)
-        self.tokens = LLMTokenCount(model_info=iris_message.model_info,
-                                         num_input_tokens=iris_message.num_input_tokens,
-                                         num_output_tokens=iris_message.num_output_tokens,
-                                         pipeline=PipelineEnum.NOT_SET)
+        self.tokens = LLMTokenCount(
+            model_info=iris_message.model_info,
+            num_input_tokens=iris_message.num_input_tokens,
+            num_output_tokens=iris_message.num_output_tokens,
+            pipeline=PipelineEnum.NOT_SET,
+        )
         return ChatResult(generations=[chat_generation])
 
     @property

app/pipeline/chat/code_feedback_pipeline.py

@@ -7,7 +7,6 @@
 from langchain_core.runnables import Runnable
 from langsmith import traceable
 from pydantic import BaseModel
-from sipbuild.generator.parser.tokens import tokens
 
 from ...domain import PyrisMessage
 from ...domain.data.build_log_entry import BuildLogEntryDTO

app/pipeline/chat/course_chat_pipeline.py

@@ -14,7 +14,6 @@
 from langchain_core.runnables import Runnable
 from langchain_core.tools import tool
 from langsmith import traceable
-from sipbuild.generator.parser.tokens import tokens
 from weaviate.collections.classes.filters import Filter
 
 from .interaction_suggestion_pipeline import (
@@ -42,7 +41,6 @@
     elicit_begin_agent_jol_prompt,
 )
 from ...domain import CourseChatPipelineExecutionDTO
-from ...llm.external.LLMTokenCount import LLMTokenCount
 from ...llm.external.PipelineEnum import PipelineEnum
 from ...retrieval.lecture_retrieval import LectureRetrieval
 from ...vector_database.database import VectorDatabase

app/pipeline/chat/exercise_chat_pipeline.py

@@ -10,7 +10,6 @@
 )
 from langchain_core.runnables import Runnable
 from langsmith import traceable, get_current_run_tree
-from sipbuild.generator.parser.tokens import tokens
 from weaviate.collections.classes.filters import Filter
 
 from .code_feedback_pipeline import CodeFeedbackPipeline
@@ -35,7 +34,6 @@
 from ...domain.data.programming_submission_dto import ProgrammingSubmissionDTO
 from ...llm import CapabilityRequestHandler, RequirementList
 from ...llm import CompletionArguments
-from ...llm.external.LLMTokenCount import LLMTokenCount
 from ...llm.external.PipelineEnum import PipelineEnum
 from ...llm.langchain import IrisLangchainChatModel
 from ...retrieval.lecture_retrieval import LectureRetrieval
@@ -102,7 +100,9 @@ def __call__(self, dto: ExerciseChatPipelineExecutionDTO):
             )
             self._run_exercise_chat_pipeline(dto, should_execute_lecture_pipeline),
             self.callback.done(
-                "Generated response", final_result=self.exercise_chat_response, tokens=self.tokens
+                "Generated response",
+                final_result=self.exercise_chat_response,
+                tokens=self.tokens,
             )
 
             try:
@@ -116,7 +116,11 @@ def __call__(self, dto: ExerciseChatPipelineExecutionDTO):
                     suggestion_dto.last_message = self.exercise_chat_response
                     suggestion_dto.problem_statement = dto.exercise.problem_statement
                     suggestions = self.suggestion_pipeline(suggestion_dto)
-                    self.callback.done(final_result=None, suggestions=suggestions, tokens=[self.suggestion_pipeline.tokens])
+                    self.callback.done(
+                        final_result=None,
+                        suggestions=suggestions,
+                        tokens=[self.suggestion_pipeline.tokens],
+                    )
                 else:
                     # This should never happen but whatever
                     self.callback.skip(

app/pipeline/chat/lecture_chat_pipeline.py

@@ -16,7 +16,6 @@
     LectureChatPipelineExecutionDTO,
 )
 from ...llm import CapabilityRequestHandler, RequirementList
-from ...llm.external import LLMTokenCount
 from ...llm.external.PipelineEnum import PipelineEnum
 from ...retrieval.lecture_retrieval import LectureRetrieval
 from ...vector_database.database import VectorDatabase

app/pipeline/competency_extraction_pipeline.py

@@ -5,7 +5,6 @@
 from langchain_core.prompts import (
     ChatPromptTemplate,
 )
-from sipbuild.generator.parser.tokens import tokens
 
 from app.domain import (
     CompetencyExtractionPipelineExecutionDTO,
@@ -80,10 +79,12 @@ def __call__(
         response = self.request_handler.chat(
             [prompt], CompletionArguments(temperature=0.4)
         )
-        num_tokens = LLMTokenCount(model_info=response.model_info,
-                                   num_input_tokens=response.num_input_tokens,
-                                   num_output_tokens=response.num_output_tokens,
-                                   pipeline=PipelineEnum.IRIS_COMPETENCY_GENERATION)
+        num_tokens = LLMTokenCount(
+            model_info=response.model_info,
+            num_input_tokens=response.num_input_tokens,
+            num_output_tokens=response.num_output_tokens,
+            pipeline=PipelineEnum.IRIS_COMPETENCY_GENERATION,
+        )
         self.tokens.append(num_tokens)
         response = response.contents[0].text_content
 

app/pipeline/shared/citation_pipeline.py

@@ -7,7 +7,6 @@
 from langchain_core.runnables import Runnable
 
 from app.llm import CapabilityRequestHandler, RequirementList, CompletionArguments
-from app.llm.external import LLMTokenCount
 from app.llm.external.PipelineEnum import PipelineEnum
 from app.llm.langchain import IrisLangchainChatModel
 from app.pipeline import Pipeline

app/pipeline/shared/reranker_pipeline.py

@@ -6,7 +6,6 @@
 from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
 from langchain_core.runnables import Runnable
 from langsmith import traceable
-from sipbuild.generator.parser.tokens import tokens
 
 from app.domain import PyrisMessage
 from app.llm import CapabilityRequestHandler, RequirementList, CompletionArguments

app/retrieval/lecture_retrieval.py

@@ -2,7 +2,6 @@
 from typing import List
 
 from langsmith import traceable
-from sipbuild.generator.parser.tokens import tokens
 from weaviate import WeaviateClient
 from weaviate.classes.query import Filter
 
@@ -82,6 +81,7 @@ class LectureRetrieval(Pipeline):
     """
     Class for retrieving lecture data from the database.
     """
+
     tokens: LLMTokenCount
 
     def __init__(self, client: WeaviateClient, **kwargs):