Adjust file selector pipeline

app/common/message_converters.py

@@ -1,6 +1,25 @@
+from datetime import datetime
+
 from langchain_core.messages import BaseMessage
 
-from domain import IrisMessage, IrisMessageRole
+from domain.iris_message import IrisMessage, IrisMessageRole
+from domain.data.message_dto import MessageDTO, MessageContentDTO, IrisMessageSender
+
+
+def convert_iris_message_to_message_dto(iris_message: IrisMessage) -> MessageDTO:
+    match iris_message.role:
+        case "user":
+            sender = IrisMessageSender.USER
+        case "assistant":
+            sender = IrisMessageSender.LLM
+        case _:
+            raise ValueError(f"Unknown message role: {iris_message.role}")
+
+    return MessageDTO(
+        sent_at=datetime.now(),
+        sender=sender,
+        contents=[MessageContentDTO(textContent=iris_message.text)],
+    )
 
 
 def convert_iris_message_to_langchain_message(iris_message: IrisMessage) -> BaseMessage:

app/domain/data/build_log_entry.py

@@ -5,3 +5,6 @@
 class BuildLogEntryDTO(BaseModel):
     timestamp: datetime
     message: str
+
+    def __str__(self):
+        return f"{self.timestamp}: {self.message}"

app/domain/data/message_dto.py

@@ -1,6 +1,8 @@
 from datetime import datetime
 from enum import Enum
-from typing import List
+from typing import List, Literal
+
+from domain.iris_message import IrisMessage
 from message_content_dto import MessageContentDTO
 
 from pydantic import BaseModel, Field
@@ -13,5 +15,26 @@ class IrisMessageSender(str, Enum):
 
 class MessageDTO(BaseModel):
     sent_at: datetime = Field(alias="sentAt")
-    sender: IrisMessageSender
+    sender: Literal[IrisMessageSender.USER, IrisMessageSender.LLM]
     contents: List[MessageContentDTO]
+
+    def __str__(self):
+        match self.sender:
+            case IrisMessageSender.USER:
+                sender = "user"
+            case IrisMessageSender.LLM:
+                sender = "ai"
+            case _:
+                raise ValueError(f"Unknown message sender: {self.sender}")
+        return f"{sender}: {self.contents[0].textContent}"
+
+    def convert_to_iris_message(self):
+        match self.sender:
+            case IrisMessageSender.USER:
+                sender = "user"
+            case IrisMessageSender.LLM:
+                sender = "assistant"
+            case _:
+                raise ValueError(f"Unknown message sender: {self.sender}")
+
+        return IrisMessage(text=self.contents[0].textContent, role=sender)

app/domain/data/programming_exercise_dto.py

@@ -1,3 +1,5 @@
+from typing import Dict
+
 from pydantic import BaseModel, Field
 from datetime import datetime
 from enum import Enum
@@ -20,9 +22,9 @@ class ProgrammingExerciseDTO(BaseModel):
     id: int
     name: str
     programming_language: ProgrammingLanguage = Field(alias="programmingLanguage")
-    template_repository: dict[str, str] = Field(alias="templateRepository")
-    solution_repository: dict[str, str] = Field(alias="solutionRepository")
-    test_repository: dict[str, str] = Field(alias="testRepository")
+    template_repository: Dict[str, str] = Field(alias="templateRepository")
+    solution_repository: Dict[str, str] = Field(alias="solutionRepository")
+    test_repository: Dict[str, str] = Field(alias="testRepository")
     problem_statement: str = Field(alias="problemStatement")
     start_date: datetime = Field(alias="startDate")
     end_date: datetime = Field(alias="endDate")

app/domain/data/submission_dto.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Dict
 
 from pydantic import BaseModel, Field
 
@@ -10,7 +10,7 @@
 class SubmissionDTO(BaseModel):
     id: int
     date: datetime
-    repository: dict[str, str]
+    repository: Dict[str, str]
     is_practice: bool = Field(alias="isPractice")
     build_failed: bool = Field(alias="buildFailed")
     build_log_entries: List[BuildLogEntryDTO] = Field(alias="buildLogEntries")

app/domain/iris_message.py

@@ -0,0 +1,20 @@
+from enum import Enum
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class IrisMessageRole(str, Enum):
+    USER = "USER"
+    ASSISTANT = "ASSISTANT"
+    SYSTEM = "SYSTEM"
+
+
+class IrisMessage(BaseModel):
+    text: str
+    role: Literal[
+        IrisMessageRole.USER, IrisMessageRole.ASSISTANT, IrisMessageRole.SYSTEM
+    ]
+
+    def __str__(self):
+        return f"{self.role.lower()}: {self.text}"

app/domain/status/stage_dto.py

@@ -1,3 +1,5 @@
+from typing import Literal
+
 from pydantic import BaseModel
 
 from domain.status.stage_state_dto import StageStateDTO
@@ -6,5 +8,11 @@
 class StageDTO(BaseModel):
     name: str
     weight: int
-    state: StageStateDTO
+    state: Literal[
+        StageStateDTO.NOT_STARTED,
+        StageStateDTO.IN_PROGRESS,
+        StageStateDTO.DONE,
+        StageStateDTO.SKIPPED,
+        StageStateDTO.ERROR,
+    ]
     message: str

app/pipeline/chat/file_selector_pipeline.py

@@ -1,24 +1,25 @@
 import logging
 import os
-from typing import List
+from typing import List, Dict
 
 from langchain.output_parsers import PydanticOutputParser
 from langchain_core.prompts import PromptTemplate
 from langchain_core.runnables import Runnable
 from pydantic import BaseModel
 
-from domain.submission import BuildLogEntry
+from domain.data.build_log_entry import BuildLogEntryDTO
 from llm import BasicRequestHandler
 from llm.langchain import IrisLangchainChatModel
 from pipeline import Pipeline
 from pipeline.chat.output_models.output_models.selected_file_model import SelectedFile
+from web.status.status_update import StatusCallback
 
 logger = logging.getLogger(__name__)
 
 
 class FileSelectionDTO(BaseModel):
-    files: List[str]
-    build_logs: List[BuildLogEntry]
+    files: Dict[str, str]
+    build_logs: List[BuildLogEntryDTO]
 
     def __str__(self):
         return (
@@ -32,11 +33,13 @@ class FileSelectorPipeline(Pipeline):
 
     llm: IrisLangchainChatModel
     pipeline: Runnable
+    callback: StatusCallback
 
-    def __init__(self):
+    def __init__(self, callback: StatusCallback):
         super().__init__(implementation_id="file_selector_pipeline_reference_impl")
         request_handler = BasicRequestHandler("gpt35")
         self.llm = IrisLangchainChatModel(request_handler)
+        self.callback = callback
         # Load prompt from file
         dirname = os.path.dirname(__file__)
         with open(
@@ -48,26 +51,26 @@ def __init__(self):
         # Create the prompt
         prompt = PromptTemplate(
             template=prompt_str,
-            input_variables=["repository", "build_log"],
+            input_variables=["file_names", "build_logs"],
             partial_variables={"format_instructions": parser.get_format_instructions()},
         )
         logger.debug(parser.get_format_instructions())
         # Create the pipeline
         self.pipeline = prompt | self.llm | parser
 
-    def __call__(self, dto: FileSelectionDTO, **kwargs) -> SelectedFile:
+    def __call__(self, dto: FileSelectionDTO, **kwargs) -> str:
         """
         Runs the pipeline
             :param query: The query
-            :return: IrisMessage
+            :return: Selected file content
         """
         logger.debug("Running file selector pipeline...")
-        repository = dto.files
-        build_log = dto.build_logs
+        file_names = list(dto.files.keys())
+        build_logs = dto.build_logs
         response = self.pipeline.invoke(
             {
-                "repository": repository,
-                "build_log": build_log,
+                "file_names": file_names,
+                "build_logs": build_logs,
             }
         )
-        return response
+        return dto.files[response.selected_file] if response.selected_file else ""

app/pipeline/chat/tutor_chat_pipeline.py

@@ -13,7 +13,9 @@
 
 from domain import TutorChatPipelineExecutionDTO
 from domain.data.message_dto import MessageDTO
+from domain.iris_message import IrisMessage
 from web.status.status_update import TutorChatStatusCallback
+from .file_selector_pipeline import FileSelectorPipeline, FileSelectionDTO
 from ...llm import BasicRequestHandler
 from ...llm.langchain import IrisLangchainChatModel
 
@@ -23,17 +25,23 @@
 logger = logging.getLogger(__name__)
 
 
+class IrisMessageRole:
+    pass
+
+
 class TutorChatPipeline(Pipeline):
     """Tutor chat pipeline that answers exercises related questions from students."""
 
     llm: IrisLangchainChatModel
     pipeline: Runnable
+    callback: TutorChatStatusCallback
 
     def __init__(self, callback: TutorChatStatusCallback):
-        super().__init__(implementation_id="tutor_chat_pipeline_reference_impl")
+        super().__init__(implementation_id="tutor_chat_pipeline")
         # Set the langchain chat model
         request_handler = BasicRequestHandler("gpt35")
         self.llm = IrisLangchainChatModel(request_handler)
+        self.callback = callback
         # Load the prompt from a file
         dirname = os.path.dirname(__file__)
         with open(
@@ -51,16 +59,26 @@ def __init__(self, callback: TutorChatStatusCallback):
         )
         # Create the pipeline
         summary_pipeline = SummaryPipeline()
+        # Create file selector pipeline
+        file_selector_pipeline = FileSelectorPipeline()
         self.pipeline = (
             {
                 "question": itemgetter("question"),
                 "history": itemgetter("history"),
                 "exercise_title": itemgetter("exercise_title"),
-                "summary": itemgetter("problem_statement")
+                "summary": itemgetter("problem_statement"),
+                "file_content": itemgetter("file_map")
+                | RunnableLambda(
+                    lambda file_map: file_selector_pipeline(
+                        dto=FileSelectionDTO(files=file_map.keys(), build_logs=[])
+                    ),
+                    callback=None,
+                )
                 | RunnableLambda(
-                    lambda stmt: summary_pipeline(query=stmt), callback=None
+                    lambda selected_file: (
+                        itemgetter("file_map")[selected_file] if selected_file else ""
+                    ),
                 ),
-                "file_content": itemgetter("file_content"),
             }
             | prompt
             | self.llm
@@ -77,25 +95,25 @@ def __call__(self, dto: TutorChatPipelineExecutionDTO, **kwargs):
         """
         Runs the pipeline
             :param query: The query
-            :return: IrisMessage
         """
         logger.debug("Running tutor chat pipeline...")
         logger.debug(f"DTO: {dto}")
         history: List[MessageDTO] = dto.chat_history[:-1]
-        query: MessageDTO = dto.chat_history[-1]
+        query: IrisMessage = dto.chat_history[-1].convert_to_iris_message()
         problem_statement: str = dto.exercise.problem_statement
-        exercise_title: str = dto.exercise.title
-        message = query.contents[0].textContent
+        exercise_title: str = dto.exercise.name
+        message = query.text
+        file_map = dto.latest_submission.repository
         if not message:
             raise ValueError("IrisMessage must not be empty")
         response = self.pipeline.invoke(
             {
                 "question": message,
                 "history": [message.__str__() for message in history],
                 "problem_statement": problem_statement,
-                "file_content": "",  # TODO add file selector pipeline and get file content
+                "file_map": file_map,
                 "exercise_title": exercise_title,
             }
         )
         logger.debug(f"Response from tutor chat pipeline: {response}")
-        return MessageDTO(role=IrisMessageRole.ASSISTANT, text=response)
+        # TODO: Convert response to status update

app/pipeline/prompts/file_selector_prompt.txt

@@ -1,10 +1,10 @@
 Select a file from the files list below that is mentioned in the build logs. If no file is in the list is mentioned in the build logs answer with empty string.
 
 Here are the paths of all files:
-{repository}
+{file_names}
 
 Build logs:
-{build_log}
+{build_logs}
 
 {format_instructions}
 

app/pipeline/prompts/iris_tutor_chat_prompt.txt

@@ -37,9 +37,9 @@ A: I am Iris, the AI programming tutor integrated into Artemis, the online learn
 
 Consider the following exercise context:
     - Title: {exercise_title}
-    - Problem Statement: {summary}
-    - Exercise skeleton code in markdown format:
-    ```java
+    - Problem Statement: {problem_statement}
+    - Exercise skeleton code in Markdown format:
+    ```[{programming_language}]
         {file_content}
     ```