mervyn-teo/dsh-plugin-rag

安装

> 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:

| 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, or leave empty to read it from an environment variable. |
| **Key env var** | The environment variable read 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.

把 mervyn-teo/dsh-plugin-rag 加入你的 DSH 配置（web profile）即可启用。