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Refactor LLM analysis logging and status emissions for improved clarity and accuracy in memory retrieval processes

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mtayfur
2025-10-09 11:33:50 +03:00
parent 765408cea4
commit bb992906ad
1 changed files with 8 additions and 7 deletions
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15
memory_system.py
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@@ -770,14 +770,10 @@ CANDIDATE MEMORIES:
logger.info(f"🧠 LLM selected {len(selected_memories)} out of {len(candidate_memories)} candidates") logger.info(f"🧠 LLM selected {len(selected_memories)} out of {len(candidate_memories)} candidates")
if not selected_memories:
await self.memory_system._emit_status(emitter, f"📭 No Relevant Memories After LLM Analysis", done=True)
return selected_memories return selected_memories
except Exception as e: except Exception as e:
logger.warning(f"🤖 LLM reranking failed during memory relevance analysis: {str(e)}") logger.warning(f"🤖 LLM reranking failed during memory relevance analysis: {str(e)}")
await self.memory_system._emit_status(emitter, f"⚠️ LLM Analysis Failed, Using Similarity Ranking", done=True)
return candidate_memories return candidate_memories
async def rerank_memories( async def rerank_memories(
@@ -799,13 +795,18 @@ CANDIDATE MEMORIES:
logger.info(f"Using LLM reranking: {decision_reason}") logger.info(f"Using LLM reranking: {decision_reason}")
selected_memories = await self._llm_select_memories(user_message, llm_candidates, max_injection, emitter) selected_memories = await self._llm_select_memories(user_message, llm_candidates, max_injection, emitter)
if not selected_memories:
logger.info("📭 No relevant memories after LLM analysis")
await self.memory_system._emit_status(emitter, f"📭 No Relevant Memories After LLM Analysis", done=True)
return selected_memories, analysis_info
else: else:
logger.info(f"Skipping LLM reranking: {decision_reason}") logger.info(f"Skipping LLM reranking: {decision_reason}")
selected_memories = candidate_memories[:max_injection] selected_memories = candidate_memories[:max_injection]
duration = time.time() - start_time duration = time.time() - start_time
duration_text = f" in {duration:.2f}s" if duration >= 0.01 else "" duration_text = f" in {duration:.2f}s" if duration >= 0.01 else ""
await self.memory_system._emit_status(emitter, f"🎯 Memory Retrieval Complete{duration_text}", done=False) await self.memory_system._emit_status(emitter, f"🎯 Memory Retrieval Complete{duration_text}", done=True)
logger.info(f"🎯 Memory Retrieval Complete{duration_text}") logger.info(f"🎯 Memory Retrieval Complete{duration_text}")
return selected_memories, analysis_info return selected_memories, analysis_info
@@ -1036,10 +1037,10 @@ class LLMConsolidationService:
duration = time.time() - start_time duration = time.time() - start_time
logger.info(f"💾 Memory Consolidation Complete In {duration:.2f}s") logger.info(f"💾 Memory Consolidation Complete In {duration:.2f}s")
await self.memory_system._emit_status(emitter, f"💾 Memory Consolidation Complete in {duration:.2f}s", done=False)
total_operations = created_count + updated_count + deleted_count total_operations = created_count + updated_count + deleted_count
if total_operations > 0 or failed_count > 0: if total_operations > 0 or failed_count > 0:
await self.memory_system._emit_status(emitter, f"💾 Memory Consolidation Complete in {duration:.2f}s", done=False)
operation_details = self.memory_system._build_operation_details(created_count, updated_count, deleted_count) operation_details = self.memory_system._build_operation_details(created_count, updated_count, deleted_count)
memory_word = "Memory" if total_operations == 1 else "Memories" memory_word = "Memory" if total_operations == 1 else "Memories"
operations_summary = f"{', '.join(operation_details)} {memory_word}" operations_summary = f"{', '.join(operation_details)} {memory_word}"