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

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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.

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:

Field	Purpose
Enable indexing	Toggle the indexer and the rag_search tool.
Embedding model	Pick an existing preset — BGE-M3 (SoCLaaS), OpenAI text-embedding-3-small/large, or Ollama nomic-embed-text — or Custom… to supply your own.
Endpoint URL	Base URL of any OpenAI-compatible embeddings endpoint.
Model name	The model string sent to the endpoint.
API key	Paste a key directly. Saving it persists it to the harness settings (settings.yaml) and mirrors it into the .env file under Key env var, so it survives a restart. Leave empty to read from settings, then .env, then the process environment.
Key env var	The environment variable name the key is read from / written to in the .env file when the API key field is empty.
Results	Default number of hits returned by rag_search.
Index tool results	Also index tool output (on by default).
Index reasoning	Also index model reasoning blocks (off: noise + privacy).
Max chars per chunk	Chunk size for long messages.

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:

Event	Effect
session/created	Replays the (new or resumed) session's log from the stored cursor forward.
session/event	Increment/decrement — indexes new user/message, assistant/message, and tool/result surface events; un-indexes entries shadowed by a replace (compaction / tool-result pruning).
session/flush	Awaited durability checkpoint; drains the pending embed batch.

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

Key	Default	Contract
enabled	true	Whether indexing and the rag_search tool are active.
provider	soclaas-bge-m3	soclaas-bge-m3 · openai-3-small · openai-3-large · ollama-nomic · custom
model	bge-m3	Model string sent to the endpoint (overrides the preset's model).
endpoint	https://soclaas-api.comp.nus.edu.sg/v1	OpenAI-compatible embeddings base URL.
topK	5	Default result count (1–50).
dataDir	""	Index directory; empty means ~/.dsh/rag.
includeToolResults	true	Index tool results.
includeReasoning	false	Index reasoning blocks.
maxChunkChars	4000	Max characters per chunk (256–16000).

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