mirror of
https://github.com/open-jarvis/OpenJarvis.git
synced 2026-07-27 21:05:34 +00:00
Andrew Park
f3d9705961
Tools: - Bridge 4 more real OpenJarvis tools into the orchestrator catalog (think, apply_patch, pdf_extract, db_query); catalog is now 6 models + 11 basic tools. - Fix the dispatch path so confirmation-gated tools actually run: the ToolExecutor was built with no confirm callback, so shell_exec (and any requires_confirmation tool) returned a "requires confirmation" error instead of executing -- which silently broke TerminalBench. Headless eval/rollouts now auto-approve. - Drop dead-end candidates: git_* (need the unbuilt openjarvis_rust extension and just duplicate shell_exec), browser_* (needs Playwright), and knowledge_search/sql/retrieval (personal-data RAG, empty on the academic benchmarks). SFT data pipeline: - Reasoning-task loaders (GeneralThought-430K + OpenThoughts3) with an 8K cold-start set and a 30K GRPO prompt pool. - Domain-dispatched verifier (math/code checkers + Gemini judge fallback, since the OpenAI key is dead). - Rejection-sampling generator + unified <tool_call> serializer matching the GRPO rollout format; base self-sampling driver. - Drop the old paradigm/tier scaffolding (adp_loader, build, paradigms, select, serialize, tiers) replaced by the unified path. Training/eval: - SFT + GRPO configs for Qwen3.5-9B and gemma-4-12B-it. - OrchestratorBackend + eval harness over GAIA/TerminalBench/TauBench/ MMLU-Pro/SuperGPQA. - Cost-aware GRPO reward. Ignore generated data/ artifacts.
104 lines
3.6 KiB
Python
104 lines
3.6 KiB
Python
#!/usr/bin/env python
|
|
"""Build ToolOrchestra SFT data by rejection sampling over ToolScale.
|
|
|
|
For each ToolScale task, a teacher orchestrator is rolled out N times over the
|
|
faithful unified tool catalog (one tool per model); passing trajectories are
|
|
serialized into the conversations JSONL the SFT trainer consumes.
|
|
|
|
Needs API access for the teacher + expert models (set the usual env keys).
|
|
|
|
Example:
|
|
uv run python scripts/orchestrator/build_unified_sft.py \
|
|
--out data/orchestrator_unified_sft.jsonl \
|
|
--teacher-model gpt-5 --max-tasks 200 --samples-per-task 4 \
|
|
--local-model qwen3:8b --local-endpoint http://localhost:8001/v1
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import argparse
|
|
import json
|
|
import logging
|
|
import os
|
|
from typing import Optional
|
|
|
|
from openjarvis.agents.hybrid.expert_registry import default_catalog
|
|
from openjarvis.agents.hybrid.toolorchestra.unified import (
|
|
make_call_orchestrator,
|
|
make_dispatch,
|
|
)
|
|
from openjarvis.agents.hybrid.toolorchestra.rollout import run_unified_rollout
|
|
from openjarvis.learning.intelligence.orchestrator.sft_data.reject_sample import (
|
|
generate_sft_dataset,
|
|
gold_coverage_verify,
|
|
)
|
|
from openjarvis.learning.intelligence.orchestrator.sft_data.toolscale import (
|
|
load_toolscale,
|
|
)
|
|
|
|
|
|
def main(argv: Optional[list[str]] = None) -> int:
|
|
p = argparse.ArgumentParser(description=__doc__)
|
|
p.add_argument("--out", default="data/orchestrator_unified_sft.jsonl")
|
|
p.add_argument("--teacher-model", default="claude-sonnet-4-6")
|
|
p.add_argument("--teacher-base-url", default="https://api.anthropic.com/v1/",
|
|
help="OpenAI-compatible base URL; pass '' for OpenAI cloud.")
|
|
p.add_argument("--max-tasks", type=int, default=200)
|
|
p.add_argument("--samples-per-task", type=int, default=4)
|
|
p.add_argument("--max-keep-per-task", type=int, default=1)
|
|
p.add_argument("--max-turns", type=int, default=50)
|
|
p.add_argument("--temperature", type=float, default=1.0)
|
|
p.add_argument("--local-model", default=None)
|
|
p.add_argument("--local-endpoint", default=None)
|
|
args = p.parse_args(argv)
|
|
|
|
logging.basicConfig(level=logging.INFO, format="%(message)s")
|
|
|
|
tools = default_catalog(
|
|
local_model=args.local_model, local_endpoint=args.local_endpoint,
|
|
)
|
|
logging.info("Tool catalog (%d): %s", len(tools), [t.name for t in tools])
|
|
|
|
base_url = args.teacher_base_url or None
|
|
api_key = (
|
|
os.environ.get("ANTHROPIC_API_KEY")
|
|
if base_url and "anthropic" in base_url
|
|
else os.environ.get("OPENAI_API_KEY")
|
|
)
|
|
call_orch = make_call_orchestrator(
|
|
args.teacher_model,
|
|
base_url=base_url,
|
|
api_key=api_key,
|
|
temperature=args.temperature,
|
|
)
|
|
dispatch = make_dispatch({})
|
|
|
|
def rollout_fn(task):
|
|
try:
|
|
return run_unified_rollout(
|
|
task.instruction, tools,
|
|
call_orchestrator=call_orch, dispatch=dispatch,
|
|
max_turns=args.max_turns,
|
|
)
|
|
except Exception as exc: # network/key failures shouldn't kill the run
|
|
logging.warning("rollout failed for %s: %s", task.task_id, exc)
|
|
return None
|
|
|
|
tasks = load_toolscale(max_tasks=args.max_tasks)
|
|
stats = generate_sft_dataset(
|
|
args.out,
|
|
tasks=tasks,
|
|
tools=tools,
|
|
rollout_fn=rollout_fn,
|
|
verify_fn=gold_coverage_verify,
|
|
samples_per_task=args.samples_per_task,
|
|
max_keep_per_task=args.max_keep_per_task,
|
|
reward_fn=lambda r: -r.cost_usd, # cheapest-correct gets highest reward
|
|
)
|
|
print(json.dumps(stats, indent=2))
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
raise SystemExit(main())
|