Iris: Add MCQ carousel, response persistence, and content-aware quiz generation

iris/.claudeignore

@@ -0,0 +1,4 @@
+application.example.yml
+application.local.yml
+llm_config.local.yml
+llm_config.example.yml

iris/src/iris/common/pipeline_enum.py

@@ -24,5 +24,6 @@ class PipelineEnum(str, Enum):
     IRIS_LECTURE_SUMMARY_PIPELINE = "IRIS_LECTURE_SUMMARY_PIPELINE"
     IRIS_TUTOR_SUGGESTION_PIPELINE = "IRIS_TUTOR_SUGGESTION_PIPELINE"
     IRIS_SESSION_TITLE_GENERATION_PIPELINE = "IRIS_SESSION_TITLE_GENERATION_PIPELINE"
+    IRIS_MCQ_GENERATION_PIPELINE = "IRIS_MCQ_GENERATION_PIPELINE"
     IRIS_LECTURE_SEARCH_ANSWER_PIPELINE = "IRIS_LECTURE_SEARCH_ANSWER_PIPELINE"
     NOT_SET = "NOT_SET"

iris/src/iris/domain/status/stage_dto.py

@@ -1,15 +1,18 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 from iris.domain.status.stage_state_dto import StageStateEnum
 
 
 class StageDTO(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
+
     name: Optional[str] = None
     weight: int
     state: StageStateEnum
     message: Optional[str] = None
     internal: bool = Field(
         default=False
     )  # An internal stage is not shown in the UI and hidden from the user
+    chat_message: Optional[str] = Field(alias="chatMessage", default=None)

iris/src/iris/llm/request_handler/rerank_request_handler.py

@@ -3,12 +3,15 @@
 from langchain_core.tools import BaseTool
 from pydantic import BaseModel, ConfigDict
 
+from iris.common.logging_config import get_logger
 from iris.common.pyris_message import PyrisMessage
 from iris.domain.data.image_message_content_dto import ImageMessageContentDTO
 from iris.llm import CompletionArguments, RequestHandler
 from iris.llm.external.model import LanguageModel
 from iris.llm.llm_manager import LlmManager
 
+logger = get_logger(__name__)
+
 
 class RerankRequestHandler(RequestHandler):
     """RerankRequestHandler handles document reranking based on a query and a specified content field using a language
@@ -17,6 +20,7 @@ class RerankRequestHandler(RequestHandler):
 
     model_id: str
     llm_manager: LlmManager | None = None
+    _rerank_available: bool = True
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
     def __init__(self, model_id: str):
@@ -63,18 +67,32 @@ def rerank(self, query, documents: List, top_n: int, content_field_name: str):
         if not valid_documents:
             return []
 
+        # Skip Cohere entirely after first failure to avoid repeated timeout delays
+        if not self._rerank_available:
+            return valid_documents[:top_n]
+
         document_contents = list(
             map(lambda x: getattr(x, content_field_name), valid_documents)
         )
 
         cohere_client = self.llm_manager.get_llm_by_id(self.model_id)
 
-        _, reranked_results, _ = cohere_client.rerank(
-            query=query,
-            documents=document_contents,
-            top_n=top_n,
-        )
-        ranked_documents = []
-        for result in reranked_results[1]:
-            ranked_documents.append(valid_documents[result.index])
-        return ranked_documents
+        try:
+            _, reranked_results, _ = cohere_client.rerank(
+                query=query,
+                documents=document_contents,
+                top_n=top_n,
+            )
+            ranked_documents = []
+            for result in reranked_results[1]:
+                ranked_documents.append(valid_documents[result.index])
+            return ranked_documents
+        except Exception as e:
+            logger.warning(
+                "Reranking failed, disabling for subsequent calls. "
+                "Returning top %d unranked documents: %s",
+                top_n,
+                str(e),
+            )
+            RerankRequestHandler._rerank_available = False
+            return valid_documents[:top_n]