- Revisiting RAG_SPECIALIST

- Adapt Catalogs & Retrievers to use specific types, removing tagging_fields
- Adding CrewAI Implementation Guide

eveai_chat_workers/specialists/traicie/TRAICIE_ROLE_DEFINITION_SPECIALIST/1_3.py

@@ -51,6 +51,9 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
     def _config_pydantic_outputs(self):
         self._add_pydantic_output("traicie_get_competencies_task", Competencies, "competencies")
 
+    def _config_state_result_relations(self):
+        self._add_state_result_relation("competencies")
+
     def _instantiate_specialist(self):
         verbose = self.tuning
 
@@ -83,13 +86,9 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
 
         flow_results = self.flow.kickoff(inputs=flow_inputs)
 
-        flow_state = self.flow.state
-
         results = RoleDefinitionSpecialistResult.create_for_type(self.type, self.type_version)
-        if flow_state.competencies:
-            results.competencies = flow_state.competencies
 
-        self.create_selection_specialist(arguments, flow_state.competencies)
+        self.create_selection_specialist(arguments, self.flow.state.competencies)
 
         self.log_tuning(f"Traicie Role Definition Specialist execution ended", {"Results": results.model_dump()})
 

eveai_chat_workers/specialists/traicie/TRAICIE_SELECTION_SPECIALIST/1_4.py

@@ -55,7 +55,7 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
     """
 
     def __init__(self, tenant_id, specialist_id, session_id, task_id, **kwargs):
-        self.role_definition_crew = None
+        self.rag_crew = None
 
         super().__init__(tenant_id, specialist_id, session_id, task_id)
 
@@ -407,8 +407,7 @@ class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
             if rag_output.rag_output.insufficient_info:
                 rag_output.rag_output.answer = insufficient_info_message
         else:
-            rag_output = RAGOutput(answer=insufficient_info_message,
-                                   insufficient_info=True)
+            rag_output = RAGOutput(answer=insufficient_info_message, insufficient_info=True)
 
         self.log_tuning(f"RAG Specialist execution ended", {"Results": rag_output.model_dump()})