dsh-plugin-rag

Semantic memory (RAG) over all your DeepSeek Harness chat sessions — automatic, self-contained, and non-destructive.

Install · How it works · Settings · The rag_search tool · Uninstall

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.

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):

   {
     "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):

   - 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
           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, and the key is not plugin configuration — there is no setting for it and the card offers nowhere to type one. Each provider preset pins a credential reference (soclaas-bge-m3 → SOCLAAS_API_KEY, the OpenAI presets → OPENAI_API_KEY, custom → RAG_API_KEY), which is resolved through the harness credential store: the process environment, then ~/.dsh/.credentials.yaml (the same file the Models page writes), then a .env fallback. The Settings card reports only whether that reference currently resolves.

License

MIT