46 lines
1.8 KiB
Plaintext
46 lines
1.8 KiB
Plaintext
# models.dspy — 按能力分类列出可用模型（对齐 llmage v1/models 的分类方式）
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#
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# URL: /pipeline_llm/api/v1/models?catelogid=t2t
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# catelogid 可选：t2t / t2i / t2v / embedding / rerank / tts / asr / i2t ...
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# （能力类型标准 = 模型治理 appcodes llm_capability，新增走评审禁止野生标签）
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# 不传 = 全部能力
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#
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# 鉴权：Authorization: Bearer *** token>（与 chat/completions 一致）。
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# 机构隔离：只列本机构治理链可用的模型（无策略 = 空列表）。
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auth = ''
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try:
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auth = request.headers.get('Authorization', '') or ''
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except Exception:
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auth = ''
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if not auth:
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auth = (params_kw or {}).get('api_key', '') or ''
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if not auth:
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return json.dumps({"error": {"message": "缺少 Authorization Bearer token",
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"type": "invalid_request_error", "code": "missing_token"}},
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ensure_ascii=False)
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ok, info = await verify_llm_token(auth)
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if not ok:
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return json.dumps({"error": {"message": str(info),
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"type": "invalid_request_error", "code": "invalid_token"}},
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ensure_ascii=False)
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catelogid = (params_kw or {}).get('catelogid', '') or ''
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rows = await llm_list_models(info.get('org_id', '') or '', catelogid=catelogid)
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# OpenAI 兼容输出 + 分类字段（catelogid 照 llmage 叫法，值 = 能力类型标准）
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out = {"object": "list", "data": []}
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for r in rows:
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out["data"].append({
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"id": r.get('model_id', '') or r.get('name', ''),
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"object": "model",
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"name": r.get('name', ''),
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"catelogid": r.get('capability', ''),
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"vendor": r.get('vendor', ''),
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"price_input": r.get('price_input', 0),
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"price_output": r.get('price_output', 0),
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})
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return json.dumps(out, ensure_ascii=False)
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