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Refactor memory selection logic in LLMRerankingService for improved clarity; streamline response handling by directly using response.ids.

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mtayfur
2025-10-12 23:03:36 +03:00
parent 158f0d1983
commit 849dd71a01
1 changed files with 1 additions and 4 deletions
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5
memory_system.py
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@@ -730,11 +730,9 @@ CANDIDATE MEMORIES:
try:
response = await self.memory_system._query_llm(Prompts.MEMORY_RERANKING, user_prompt, response_model=Models.MemoryRerankingResponse)
selected_ids = response.ids
selected_memories = []
for memory in candidate_memories:
if memory["id"] in selected_ids and len(selected_memories) < max_count:
if memory["id"] in response.ids and len(selected_memories) < max_count:
selected_memories.append(memory)
logger.info(f"🧠 LLM selected {len(selected_memories)} out of {len(candidate_memories)} candidates")
@@ -777,7 +775,6 @@ CANDIDATE MEMORIES:
duration_text = f" in {duration:.2f}s" if duration >= 0.01 else ""
retrieval_method = "LLM" if should_use_llm else "Semantic"
await self.memory_system._emit_status(emitter, f"🎯 {retrieval_method} Memory Retrieval Complete{duration_text}", done=True)
logger.info(f"🎯 {retrieval_method} Memory Retrieval Complete{duration_text}")
return selected_memories, analysis_info