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Cherry Studio-style knowledge base system for DeepSeek Harness (DSH): bases, documents, chunking, embeddings (OpenAI-compatible / Ollama / local / lexical fallback), retrieval, model-facing tools, and a browser management panel

Markdown 노트를 위한 이중 지식 그래프 RAG: 선행 조건 + 유사도 그래프(P/S-Agent 순회), 단락 청킹, n-gram/임베딩 하이브리드 검색, 명시적 가중치 피드백 루프 — MCP를 통해 제공

Turns local markdown and text documents into a queryable knowledge base with hybrid semantic and keyword search, citation verification, and source injection.

Cross-session plaintext memory: deterministic BM25 keyword recall (no embeddings), memory_save/list/search/confirm/forget tools behind a human-confirm gate, and global + project-scoped JSON stores that follow your git repo.

Layered memory pipeline for DSH: auto-distills conversations into atomic facts, scene summaries and a persona profile (L0–L3), with hybrid BM25 + vector retrieval, chat/work family separation, and context injection before each model step.

Tree-sitter로 워크스페이스 심볼을 인덱싱하고 어휘 기반 또는 임베딩 보조 코드 검색을 제공(선택 사항)