mirror of
https://github.com/deepseek-ai/deepseek-harness.git
synced 2026-08-29 04:26:38 +00:00
Tianyi Cui
e2920f0109
Stop narrowing the standalone DeepSeek adapter to a DSH_MODEL-derived one-entry catalog. The direct adapter already accepts model ids outside its advisory catalog, so retain only the DSH_CONTEXT_WINDOW fallback and let the JSON-RPC initialize model be the single runtime selection. Remove model mirroring from minimal.py and the packaged smoke. The keyless process now initializes deepseek-v4-pro without DSH_MODEL, while the packaged scenario continues to use its unlisted smoke-model; together they prove both cataloged and arbitrary SDK model arguments reach the adapter directly. Update the bundle, tutorial, SDK/example references, and owning Agent Notes to keep DSH_MODEL only as minimal.py's optional default input, never as a second value callers must synchronize.
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
#!/usr/bin/env python3
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"""Run one minimal-agent turn through the bundled Python SDK runtime."""
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from __future__ import annotations
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import argparse
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import os
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from pathlib import Path
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from deepseek_harness import DeepSeekHarness
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def main() -> None:
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"""Parse one task and print the agent's final response."""
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parser = argparse.ArgumentParser()
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configured_home = os.environ.get("DSH_HOME", "")
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parser.add_argument("prompt", help="Task for the minimal agent")
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parser.add_argument("--workspace", type=Path, default=Path.cwd())
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parser.add_argument(
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"--dsh-home",
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type=Path,
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default=Path(configured_home) if configured_home.strip() else None,
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)
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parser.add_argument("--profile", default="sdk-minimal")
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parser.add_argument("--session-id")
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parser.add_argument("--provider", default="deepseek-official")
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parser.add_argument("--model", default=os.environ.get("DSH_MODEL", "deepseek-v4-flash"))
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parser.add_argument("--max-tokens", type=int)
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args = parser.parse_args()
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if args.dsh_home is None:
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parser.error("--dsh-home or a non-empty DSH_HOME is required")
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workspace = args.workspace.resolve()
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dsh_home = args.dsh_home.resolve()
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with DeepSeekHarness(
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provider=args.provider,
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model=args.model,
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max_tokens=args.max_tokens,
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cwd=str(workspace),
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dsh_home=str(dsh_home),
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profile=args.profile,
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) as harness:
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result = harness.run(args.prompt, session_id=args.session_id)
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print(result.final_response)
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if __name__ == "__main__":
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main()
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