- RAG Specialist fully implemented new style

- Selection Specialist - VA version - fully implemented
- Correction of TRAICIE_ROLE_DEFINITION_SPECIALIST - adaptation to new style
- Removal of 'debug' statements

common/eveai_model/tracked_mistral_embeddings.py

@@ -44,7 +44,6 @@ class TrackedMistralAIEmbeddings(EveAIEmbeddings):
         for i in range(0, len(texts), self.batch_size):
             batch = texts[i:i + self.batch_size]
             batch_num = i // self.batch_size + 1
-            current_app.logger.debug(f"Processing embedding batch {batch_num}, size: {len(batch)}")
 
             start_time = time.time()
             try:
@@ -70,9 +69,6 @@ class TrackedMistralAIEmbeddings(EveAIEmbeddings):
                 }
                 current_event.log_llm_metrics(metrics)
 
-                current_app.logger.debug(f"Batch {batch_num} processed: {len(batch)} texts, "
-                                         f"{result.usage.total_tokens} tokens, {batch_time:.2f}s")
-
                 # If processing multiple batches, add a small delay to avoid rate limits
                 if len(texts) > self.batch_size and i + self.batch_size < len(texts):
                     time.sleep(0.25)  # 250ms pause between batches
@@ -82,7 +78,6 @@ class TrackedMistralAIEmbeddings(EveAIEmbeddings):
                 # If a batch fails, try to process each text individually
                 for j, text in enumerate(batch):
                     try:
-                        current_app.logger.debug(f"Attempting individual embedding for item {i + j}")
                         single_start_time = time.time()
                         single_result = self.client.embeddings.create(
                             model=self.model,