mp-ai-module-controller/SDG_BRIDGE_PLAN.md

# SDG Bridge Plan

## Bridge Script Filename

```
scripts/generate_training_data.py
```

This script reads the generated dictionary artifacts from this project, optionally runs a
NeMo Data Designer expansion pass on Mimir via SSH, and writes a timestamped raw training
dataset into the `workspace_Data` pipeline, then registers it in `manifest.llm.json` so the
Training Monitor picks it up automatically.

---

## Mimir — Data Designer Status

Mimir (`100.80.52.47`) has **Data Designer 0.8.0** installed and ready:

| Component | Location | Status |
|-----------|----------|--------|
| CLI | `/home/aaron-pressey/.venvs/data-designer/bin/data-designer` | ✅ installed |
| Backend adapter | `/home/aaron-pressey/.local/share/home-grown-llm-data/nemo_data_designer_backend.py` | ✅ present |
| Storage | `/mnt/storage/data-designer/` | ✅ mounted |
| Model configs | `/mnt/storage/data-designer/model_configs.yaml` | ✅ configured |
| Config env | `/home/aaron-pressey/.config/home-grown-llm-data/data-designer.env` | ✅ present |

**Current model provider:** NVIDIA API (`nvidia-text` alias → `nvidia/nemotron-3-nano-30b-a3b`).
Local llama.cpp on port 8081 is configured but not currently running — cloud provider is used
for generation unless a local inference server is started separately.

---

## What the Bridge Does

```
Step 1 (local — this project):
  dictionary/actions.index.json     ─┐
  dictionary/model.codebook.json     ├─► generate_training_data.py ─► seed JSONL
  dictionary/model.examples.jsonl   ─┘     (20 records/action, 260 total)
                                               │
Step 2 (remote — Mimir, optional):             │
  SCP seed JSONL + DD config to Mimir ◄────────┘
  SSH: data-designer create module_controller.yaml
         --num-records <N>
         --dataset-name module_controller_intents_<timestamp>
  SCP expanded dataset back
                                               │
Step 3 (local — workspace_Data):               │
  Write to data/module_controller_intents_<timestamp>/ ◄──────────────┘
  Patch manifest.llm.json with timestamped entry
  Training Monitor picks it up as a new Data Source
```

---

## Timestamp Tagging

Every dataset generated by this script is tagged with a **Unix timestamp at generation time**.
The timestamp is embedded in:

1. **Output directory name:** `data/module_controller_intents_<unix_ts>/`
2. **Manifest entry key:** `"module_controller_intents_<unix_ts>"`
3. **Manifest entry field:** `"generated_at": <unix_ts>`
4. **Every JSONL record's metadata:** `"generated_at": <unix_ts>`

This means you can run the bridge multiple times (after adding actions, changing examples,
or expanding via NeMo) and the Training Monitor will show each run as a distinct, traceable
Data Source. No clobbering, no ambiguity.

---

## Output Record Schema

Each output record uses the `instruction` schema profile:

```json
{
  "instruction": "Map the following natural language request to the correct action and payload.",
  "input": "hash hello with sha256",
  "output": "{\"action_id\": \"hash_string\", \"payload\": {\"text\": \"hello\", \"algorithm\": \"sha256\"}}",
  "metadata": {
    "action_id": "hash_string",
    "compact": "{\"v\":\"1.0.0\",\"c\":\"<codebook_checksum>\",\"a\":\"A007\",\"p\":{\"F010\":\"hello\",\"F001\":\"sha256\"}}",
    "source": "seed_intent_example",
    "split": "train",
    "generated_at": 1784660000
  }
}
```

---

## Generation Strategy

For each action in `actions.index.json`:

1. **Seed examples** — emit each `intent_example` directly (source: `seed_intent_example`)
2. **Alias variants** — one phrase per alias using the alias as the verb (source: `alias_variant`)
3. **Payload field variants** — for enum fields, one record per enum value; for numeric fields,
   low/mid/high values (source: `field_variant`)
4. **Template expansion** — 5 records per action from `synthetic_template` with filled placeholders
   (source: `template_expansion`)

**Minimum seed target:** ~20 records × 13 actions = 260 records.
**After NeMo expansion:** configurable via `--num-records` (default: 1000).

Split: 90% train / 10% validation, partitioned by action so all 13 actions appear in both splits.

---

## Mimir Data Designer Config (auto-generated by bridge)

The bridge writes this config to Mimir before running generation:

```yaml
# auto-generated by generate_training_data.py — do not edit by hand
# generated_at: <unix_ts>

model_config_path: /mnt/storage/data-designer/model_configs.yaml
model_providers_path: /mnt/storage/data-designer/model_providers.yaml

dataset:
  name: module_controller_intents_<unix_ts>
  schema_profile: instruction
  seed_file: /mnt/storage/data-designer/managed-assets/module_controller_seed_<unix_ts>.jsonl

columns:
  - name: input
    type: seed_passthrough

  - name: output
    type: llm_text
    model_alias: nvidia-text
    prompt: |
      You are generating training data for a local action dispatcher.
      Given the natural language request below, produce valid JSON with
      exactly two fields: "action_id" (string) and "payload" (object).
      The action_id must be one of: {action_ids}.
      Vary the phrasing of the input naturally but keep the output schema strict.

      Request: {{input}}
    output_schema:
      type: object
      required: [action_id, payload]
      additionalProperties: false
      properties:
        action_id:
          type: string
          enum: {action_ids}
        payload:
          type: object
```

---

## Manifest Entry (auto-patched by bridge)

```json
"module_controller_intents_<unix_ts>": {
  "name": "Module Controller Intents",
  "category": "synthetic_sft",
  "schema_profile": "instruction",
  "source_url": null,
  "acquisition_method": "local_synthetic_generator",
  "license_spdx": "UNLICENSED",
  "redistribution_allowed": false,
  "estimated_tokens": null,
  "status": "generated_local",
  "local_path": "data/module_controller_intents_<unix_ts>",
  "local_format": "jsonl",
  "generator": "mp-ai-module-controller/scripts/generate_training_data.py",
  "sdg_engine": "data-designer",
  "sdg_host": "mimir (100.80.52.47)",
  "sdg_model": "nvidia/nemotron-3-nano-30b-a3b",
  "codebook_version": "1.0.0",
  "codebook_checksum": "<from model.codebook.json>",
  "registry_checksum": "<from actions.index.json>",
  "generated_at": <unix_ts>,
  "doc": "data/module_controller_intents_<unix_ts>/README.md"
}
```

---

## CLI Usage

```bash
# From mp-ai-module-controller project root:

# Seed only (no NeMo expansion — fast, local, 260 records):
python3 scripts/generate_training_data.py \
  --dictionary-dir dictionary/ \
  --output-dir ../workspace_Data/data/ \
  --manifest ../workspace_Data/manifest.llm.json

# Full SDG run via Mimir (requires NVIDIA_API_KEY on Mimir):
python3 scripts/generate_training_data.py \
  --dictionary-dir dictionary/ \
  --output-dir ../workspace_Data/data/ \
  --manifest ../workspace_Data/manifest.llm.json \
  --sdg-host 100.80.52.47 \
  --sdg-user aaron-pressey \
  --sdg-key ~/.ssh/silma_orson_ed25519 \
  --num-records 1000

# Dry run (prints what would be generated, writes nothing):
python3 scripts/generate_training_data.py --dry-run
```

---

## Prerequisites

- Python 3.11+ (stdlib only: `json`, `pathlib`, `hashlib`, `time`, `argparse`, `subprocess`)
- SSH access to Mimir via `~/.ssh/silma_orson_ed25519` (for NeMo expansion pass)
- `NVIDIA_API_KEY` set in Mimir's environment (for cloud Nemotron generation)
- `dictionary/` files current — run `cargo run -- dictionary generate` first if registry changed

---

## Staleness Check

The manifest entry embeds both `codebook_checksum` and `generated_at`. If you add actions:

```bash
cargo run -- dictionary generate          # updates codebook_checksum
python3 scripts/generate_training_data.py # new timestamp → new manifest entry
```

The Training Monitor will show both the old and new dataset. You can deprecate the old one
by setting its `status` to `"superseded"` in the manifest.