dsh-plugin-rag
DeepSeek Harness (DSH) 插件：针对您所有聊天会话的自包含语义记忆（RAG）。通过 session/event 实时为消息建立索引，将嵌入向量存储在一个本地 JSON 文件中，并提供 rag_search 工具。
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:mervyn-teo/dsh-plugin-rag#fe3423967755823f8773b0f0da39034dde0c5472
Plugin Rag 以 dsh-plugin-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。
dsh-plugin-rag — semantic memory for your DSH sessions
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
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.
/embeddings endpoint; vectors live in one local JSON file. No native
modules, no database, no extra service.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.
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".
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
Reinstall and restart the harness so the profile re-resolves its dependencies and mounts the new bundle.
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.
rag_search toolOnce 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?")
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:
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 is just as clean as install — nothing in the harness was modified:
dsh-plugin-rag entry from cordis.patch.yml and from
dsh.profile.bundles.package.json dependencies.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.
| 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). |
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.
