| |
| """Laya decision model — simple runner (torch-free). |
| |
| pip install coreai-core transformers numpy |
| |
| python3 run.py --task guardrail --state "rm -rf /home/user/projects" \ |
| --question '{"disposition":{"type":"choice","instructions":"...", |
| "criteria":{"allow":"...","block":"..."}}}' |
| python3 run.py --task lang_route --state "wake me at nine" \ |
| --question '{"lang":{"type":"choice","instructions":"...", |
| "criteria":{"en":"...","de":"..."}}}' |
| |
| Prints one JSON verdict: chain, choice, confidence, probs, acted. |
| Questions with no "criteria" render as binary noul (yes/no). --unit |
| gpu|cpu|ne (gpu recommended; unpinned ANE loads can SIGABRT). One process, |
| one agent — reuse it for many decide() calls in your own code via |
| CombinedAgent directly (see src/laya_port/combined_agent.py). |
| """ |
| import argparse, json, os, sys |
|
|
| HERE = os.path.dirname(os.path.abspath(__file__)) |
| sys.path.insert(0, os.path.join(HERE, "src")) |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser(description="Laya combined decision model") |
| ap.add_argument("--task", required=True, |
| help="triage|lang_route|guardrail|act_escalate|tool_route|" |
| "skill_route|mail_sort|supervise|choose|compact|rerank") |
| ap.add_argument("--state", required=True, help="agent state text") |
| ap.add_argument("--question", required=True, |
| help='typed question JSON: {"<name>": {type, instructions, criteria}}') |
| ap.add_argument("--unit", default="gpu", choices=["gpu", "cpu", "ne"]) |
| ap.add_argument("--pad", action="store_true", |
| help="pad every call to asset L_max (pays re-specialization " |
| "once at startup; ~16 ms/call warm vs seconds per shape)") |
| a = ap.parse_args() |
|
|
| from laya_port.combined_agent import CombinedAgent |
| ag = CombinedAgent(os.path.join(HERE, "laya-combined-f16.aimodel"), |
| os.path.join(HERE, "configs"), unit=a.unit) |
| q = json.loads(a.question) |
| shape = ag.prov["shape"]["L_max"] if a.pad else None |
| print(json.dumps(ag.decide(a.task, a.state, q, pad_to=shape), indent=1)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
