@kai232/dsh-rag
为 DeepSeek Harness Web 提供明确且有来源支持的本地知识库检索。
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:imkelt/DSH-RAG#f22d649f5d4f739f4e8615a2dfd80280431712c2Rag 以 @kai232/dsh-rag 发布，当前版本为 0.1.2。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
点击下方 Badge 复制 Markdown，粘贴到 README 即可。
验证 package.json 声明的 GitHub 仓库，即可管理这个公开页面。认领后，Hub 会优先安排当前版本的安全扫描，并在通过后公开展示结果。
认领这个 Plugin →English | 简体中文
DSH-RAG is a local knowledge-base plugin for DeepSeek Harness Web. It turns explicitly authorized folders into named, incrementally synchronized knowledge bases. When you run /rag <question>, it retrieves evidence before the model answers and shows verifiable sources with excerpts, exact locations, and open-file actions. Regular chat never accesses local documents.
@knowledge-base selectors for a single-turn override. Regular messages never search or inject local documents.^22.19.0 or >=24.0.0PATH (dsh plugin uses pnpm to manage profile packages)0.1.1-rc.2DeepSeek Harness is currently a developer preview. 0.1.1-rc.2 is the version DSH-RAG has verified through package installation, startup, and removal checks; it does not mean the plugin can run only on that version. Other DSH versions may work, but are not claimed as compatible until they pass the same checks.
Choose one of the following methods. Both install DSH-RAG into the same web profile, so you do not need to run both.
Use this method if you have not installed the dsh command. Run the following commands from any directory:
npx @deepseek-ai/dsh plugin --profile web add @kai232/dsh-rag
npx @deepseek-ai/dsh web
Keep the second command running, then open the Web address printed in the terminal.
If dsh --version works in your terminal, no directory change is required:
dsh plugin --profile web add @kai232/dsh-rag
dsh web
If you prefer to run DSH from source, clone the repository and use its pnpm dsh script:
git clone https://github.com/deepseek-ai/deepseek-harness.git
cd deepseek-harness
pnpm install
pnpm run build
pnpm dsh plugin --profile web add @kai232/dsh-rag
pnpm dsh web
If you already have a local checkout, skip git clone, enter its root directory, and continue with pnpm install. The pnpm run build step prepares the source artifacts used by pnpm dsh web.
To update DSH-RAG, stop the running Web process, repeat the corresponding plugin add command, and start Web again.
SILICONFLOW_API_KEY before starting DSH; the plugin never falls back to a plaintext key file. Connection status is checked automatically, and Retry appears only after a failure./rag and choose one knowledge base in the first command menu.Knowledge-base names and file names are user data and are not translated when the interface language changes.
Command format:
/rag <question>
Typing /rag shows the command and one-turn variants for each knowledge base in the first menu. After entering the command, /rag @ also shows knowledge bases above the general file, folder, and Session candidates; choosing one inserts its plain-text selector. Knowledge bases never appear in unrelated @ menus or after the question has started. Pressing Space without choosing starts a question that uses the saved Session scope. Running /rag alone shows knowledge-base statistics and usage without calling the model.
Run an explicit retrieval turn using the knowledge bases selected for the current Session:
/rag What does the project note say about the launch window?
The plugin completes retrieval before it asks the model to answer, so retrieval never depends on whether the model chooses to call a tool. The command searches only the knowledge bases selected for the current Session. If none are selected, it stops with guidance instead of searching every knowledge base.
To override the scope for one turn, select a knowledge base from the first /rag command menu. The plugin inserts the shortest unambiguous selector automatically without changing the saved Session selection:
/rag @"Project notes" Which Python version does the virtual environment use?
Names without whitespace use the shorter form, such as /rag @Notes <question>; quoted selectors remain available for names with spaces. The next regular message returns to zero retrieval.
Results identify the knowledge base, file, structural context, excerpt, and exact source location. The open action accepts an opaque source ID; the Host resolves and validates the path again before asking the operating system to open it.
With SiliconFlow configured, the first cloud vector index sends every text chunk extracted from the authorized documents. Later syncs send only new or changed chunks for Embedding. Each explicit query sends the query and a limited candidate set for Reranking. Original PDF, Word, or PowerPoint files are not uploaded, but their extracted text is sent to SiliconFlow.
If SiliconFlow or the network is unavailable, DSH-RAG reports the fallback and continues with local BM25. Existing local indexes remain available. The v0.1 release does not expose AI-generated document or section context because that experiment did not improve the frozen retrieval evaluation.
Derived data is stored under $DSH_HOME/dsh-rag/ by default, independent of the directory where DSH is started. Settings → Knowledge → Data location shows and opens the actual folder. Set DSH_RAG_DATA_DIR to an absolute path before starting DSH only when you need a custom location.
When the default location is empty and legacy data exists in the old launch-directory .dsh-rag folder, the plugin copies, validates, and promotes it without deleting the old folder. If both locations contain data, it leaves both unchanged and reports the conflict instead of merging automatically.
The Delete knowledge base action uses a second confirmation and removes only the knowledge-base settings and reproducible catalogs, checkpoints, and indexes. It never modifies or deletes files in the authorized source folders.
To back up derived data, stop DSH and copy the complete data directory. Indexes are reproducible and can be rebuilt from the original folders.
realpath; symbolic-link, junction, UNC, and path-escape cases are rejected.MIT