wehub-resource-sync
61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
"""Real smoke test against a live LM Studio server.
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Not part of the pytest suite — requires LM Studio running on localhost:1234
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with Gemma-4-31B-it loaded. Run manually:
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pipenv run python skills/autoskill/tests/smoke_lmstudio.py
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"""
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import json
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import sys
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from pathlib import Path
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THIS_DIR = Path(__file__).resolve().parent
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sys.path.insert(0, str(THIS_DIR.parent / "scripts"))
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from backends import LocalBackend
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from match_skills import load_skill_descriptions, top_k_matches
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from synthesize import synthesize
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def main() -> int:
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backend = LocalBackend(endpoint="http://localhost:1234/v1", model="gemma-4-31b-it")
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repo_skills_dir = THIS_DIR.parent.parent
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all_skills = load_skill_descriptions(repo_skills_dir)
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print(f"loaded {len(all_skills)} skills from {repo_skills_dir}")
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# Real sentence-transformers embedder.
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print("loading sentence-transformers/all-MiniLM-L6-v2 ...")
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
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def embedder(text: str):
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return list(map(float, model.encode(text)))
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cluster = {
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"apps": ["Chrome", "Zotero"],
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"session_count": 4,
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"total_duration_seconds": 7200,
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"example_titles": ["PubMed search: tumor microenvironment", "bioRxiv preprint"],
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}
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query = ("apps: " + ", ".join(cluster["apps"]) +
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" | titles: " + "; ".join(cluster["example_titles"]))
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top_k = top_k_matches(query, all_skills, embedder=embedder, k=5)
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print("real top-5 matches from embedding search:")
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for s in top_k:
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print(f" {s['score']:.3f} {s['name']}")
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result = synthesize(cluster, top_k, backend=backend)
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print("\n--- VERDICT ---")
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print(json.dumps(result, indent=2)[:1000])
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assert result["verdict"] in {"reuse", "compose", "novel"}, result
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print("\nOK: real sentence-transformers top-k + real Gemma-4-31B-it produced a valid verdict.")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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