dsh-plugin-rag

<p align="center"> <img src="assets/banner.png" alt="dsh-plugin-rag — semantic memory for your DSH sessions" width="720" /> </p>

<p align="center"> <strong>Semantic memory (RAG) over all your DeepSeek Harness chat sessions — automatic, self-contained, and non-destructive.</strong> </p>

<p align="center"> <a href="#install"><strong>Install</strong></a> · <a href="#how-it-works"><strong>How it works</strong></a> · <a href="#settings"><strong>Settings</strong></a> · <a href="#the-rag_search-tool"><strong>The <code>rag_search</code> tool</strong></a> · <a href="#uninstall"><strong>Uninstall</strong></a> </p>

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What it does

dsh-plugin-rag turns every conversation you have with the harness into a searchable memory. As you chat, the plugin increments the index with each new message and decrements it when compaction/pruning shadows old content, so retrieval always reflects the current surface of your sessions — never a stale dump.

• ✅ Automatic — no rebuild schedule, no manual export. It listens to the

session store and stays in sync as you work.

• ✅ Self-contained — embeddings come from any OpenAI-compatible

/embeddings endpoint; vectors live in one local JSON file. No native modules, no database, no extra service.

• ✅ Non-destructive — it listens to published session events. It never

patches the agent loop, and uninstalling restores the harness to its exact original state.

• ✅ Model-agnostic — choose a built-in preset or plug in your own

endpoint, model, and API key.

<p align="center"> <img src="assets/demo.gif" alt="dsh-plugin-rag demo" width="720" /> </p>

Install

A DSH plugin is a plain npm/Cordis package. Install it exactly like the terminal or qr-connect plugins: add it to your profile's dependencies, bundle list, and one cordis.patch.yml insert row.

1. Add the package to your profile's package.json (e.g. ~/.dsh/profiles/web/package.json):

``jsonc { "dependencies": { "dsh-plugin-rag": "github:mervyn-teo/dsh-plugin-rag" }, "dsh": { "profile": { "bundles": [ "@deepseek-ai/dsh-base", "@deepseek-ai/dsh-web-app", "dsh-plugin-rag" ] } } } ``

> Or install from a local clone: "dsh-plugin-rag": "file:/path/to/dsh-plugin-rag".

2. Add the insert row to your profile's cordis.patch.yml (create it if it doesn't exist):

``yaml - insert: - id: rag name: dsh-plugin-rag config: enabled: true provider: soclaas-bge-m3 model: bge-m3 endpoint: https://soclaas-api.comp.nus.edu.sg/v1 apiKey: "" apiKeyEnv: SOCLAAS_API_KEY topK: 5 dataDir: "" includeToolResults: true includeReasoning: false maxChunkChars: 4000 ``

3. Reinstall and restart the harness so the profile re-resolves its dependencies and mounts the new bundle.

Settings

Open Settings → Plugins → RAG Memory. The card exposes exactly the fields you need to point the indexer at any embeddings provider:

The card also shows a live index status (chunk count, session count, vector dimension, model, data dir) and a Reindex button.

> ⚠️ Changing the model or endpoint triggers a full rebuild, because > embedding vectors are not comparable across models or providers.

The rag_search tool

Once installed, the model gains a first-class rag_search tool. It embeds the query with your configured endpoint and returns the most relevant past messages — each with role, session title, and snippet — so the agent can recall prior work, decisions, code, and context across sessions.

rag_search("how did we set up the terminal plugin's WebSocket handshake?")

How it works

The plugin plugs into the harness the non-destructive way — by subscribing to events the session store already publishes:

Message extraction is deliberate about noise:

• only human user/message events (real prompts, not system-prompt or

runtime-context injections) are indexed;

• assistant/message contributes its final text blocks (not reasoning or

tool-call blocks — those are skipped unless you enable Index reasoning);

• tool/result contributes tool output (optional, and truncated by the

chunker).

Embeddings are written to ~/.dsh/rag/index.json (configurable via dataDir) using an atomic tmp+rename write. A per-session cursor tracks the last processed seq, so restarts are idempotent and only new content is embedded.

Uninstall

Uninstall is just as clean as install — nothing in the harness was modified:

1. Remove the dsh-plugin-rag entry from cordis.patch.yml and from dsh.profile.bundles. 2. Remove it from package.json dependencies. 3. Reinstall and restart.

Cordis disposes the plugin's scope (listeners, the rag_search tool, and the config route) automatically, leaving the harness byte-identical to before. The only residue is the index file itself; delete ~/.dsh/rag/ (or your dataDir) to purge the stored vectors.

Configuration reference

Privacy

Everything stays on your machine by default: the index is a local file, and the only outbound traffic is the embedding request to the endpoint you configure. API keys are never written into the index; they are read from the environment or kept in the plugin's runtime config.

License

[MIT](LICENSE)