feat: returning a description of each workflow node (#3539)

# Description

By returning a description of each node executed by a (LangGraph)
workflow we can show it to the user and thus inform him about the status
of the task execution

core/quivr_core/rag/entities/config.py

@@ -398,6 +398,7 @@ def resolve_special_edges(self):
 
 class NodeConfig(QuivrBaseConfig):
     name: str
+    description: str | None = None
     edges: List[str] | None = None
     conditional_edge: ConditionalEdgeConfig | None = None
     tools: List[Dict[str, Any]] | None = None

core/quivr_core/rag/entities/models.py

@@ -76,6 +76,7 @@ class RAGResponseMetadata(BaseModel):
     followup_questions: list[str] = Field(default_factory=list)
     sources: list[Any] = Field(default_factory=list)
     metadata_model: ChatLLMMetadata | None = None
+    workflow_step: str | None = None
 
 
 class ParsedRAGResponse(BaseModel):

core/quivr_core/rag/quivr_rag_langgraph.py

@@ -13,7 +13,6 @@
     TypedDict,
 )
 from uuid import UUID, uuid4
-
 import openai
 from langchain.retrievers import ContextualCompressionRetriever
 from langchain_cohere import CohereRerank
@@ -38,6 +37,7 @@
 from quivr_core.rag.entities.models import (
     ParsedRAGChunkResponse,
     QuivrKnowledge,
+    RAGResponseMetadata,
 )
 from quivr_core.rag.prompts import custom_prompts
 from quivr_core.rag.utils import (
@@ -950,6 +950,8 @@ async def answer_astream(
             version="v1",
             config={"metadata": metadata, "callbacks": [langfuse_handler]},
         ):
+            node_name = self._extract_node_name(event)
+
             if self._is_final_node_with_docs(event):
                 tasks = event["data"]["output"]["tasks"]
                 docs = tasks.docs if tasks else []
@@ -965,9 +967,17 @@ async def answer_astream(
 
                 if new_content:
                     chunk_metadata = get_chunk_metadata(rolling_message, docs)
+                    if node_name:
+                        chunk_metadata.workflow_step = node_name
                     yield ParsedRAGChunkResponse(
                         answer=new_content, metadata=chunk_metadata
                     )
+            else:
+                if node_name:
+                    yield ParsedRAGChunkResponse(
+                        answer="",
+                        metadata=RAGResponseMetadata(workflow_step=node_name),
+                    )
 
         # Yield final metadata chunk
         yield ParsedRAGChunkResponse(
@@ -991,6 +1001,17 @@ def _is_final_node_and_chat_model_stream(self, event: dict) -> bool:
             and event["metadata"]["langgraph_node"] in self.final_nodes
         )
 
+    def _extract_node_name(self, event: dict) -> str:
+        if "metadata" in event and "langgraph_node" in event["metadata"]:
+            name = event["metadata"]["langgraph_node"]
+            for node in self.retrieval_config.workflow_config.nodes:
+                if node.name == name:
+                    if node.description:
+                        return node.description
+                    else:
+                        return node.name
+        return ""
+
     async def ainvoke_structured_output(
         self, prompt: str, output_class: Type[BaseModel]
     ) -> Any: