Python Retrieval Architecture for Private Knowledge Managers: Relevance Tuning in 4 Stages

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AI Fusion Summary

A privacy‑focused personal knowledge manager uses staged retrieval with separate sparse and dense embeddings, fusing ranked results before tuning chunking and reranking against a private evaluation set. The approach keeps index size low, preserves relevance, and allows inspection of each pipeline stage. By treating crawl boundaries as product policy and adding freshness fields, the system can limit exposure to private data while maintaining up‑to‑date answers.
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