用聊天记录做个人记忆检索，提升大模型问答精准度。

As an initial step toward personal memory retrieval-augmented generation (RAG) for large language models (LLMs), this study presents a retrieval-only case study over one user's LINE conversation history. We segmented 358,896 messages into 22,329 temporally coherent chunks and constructed three search representations: raw_text, a generated summary, and embedding_text, which combines a summary with a raw-text excerpt and other fixed text. We compared BM25, dense vector retrieval, and linear hybrid retrieval on 100 evaluation questions verified by a single annotator. Among individual retrievers, embedding_text_bm25 achieved the highest point estimate, with Recall@5 of 0.584. We then explored six retriever pairings and 21 weights, for 126 configurations on the same evaluation set. The selected combination of embedding_text_bm25 and embedding_text_vector at beta = 0.45 achieved Recall@5 = 0.697, MRR@5 = 0.595, and nDCG@5 = 0.575. Its Recall@5 exceeded that of embedding_text_bm25 by 0.113, with a question-level paired percentile-bootstrap 95% confidence interval of [0.048, 0.184]. This interval is conditional on fixing the configuration selected on the same 100 questions and does not account for uncertainty from configuration selection or weight search. The difference from a summary-based hybrid at beta = 0.50 was 0.050, with a 95% confidence interval of [-0.013, 0.115], so no clear difference could be established. The 17 aggregate questions also yielded lower point estimates than the other question types, suggesting that flat chunk-level retrieval struggles when evidence is distributed across multiple times and conversations. This evaluation is an exploratory single-user, single-annotator study conducted on the same question set used for configuration search; it does not evaluate final answer generation or generalization to unseen questions.