import os import pytest os.environ.setdefault("OPENAI_API_KEY", "test-key") from yuxi.models.providers.builtin import BUILTIN_PROVIDERS from yuxi.models.providers.service import ( check_credential_status, _normalize_payload, _normalize_remote_model, fetch_remote_models, ) def test_normalize_payload_accepts_enabled_chat_model(): payload = _normalize_payload( { "provider_id": "openrouter-local", "display_name": "OpenRouter Local", "base_url": "https://openrouter.ai/api/v1", "enabled_models": [{"id": "anthropic/claude-sonnet-4.5", "type": "chat"}], } ) assert payload["provider_id"] == "openrouter-local" assert payload["provider_type"] == "openai" assert "models_endpoint" not in payload assert "embedding_models_endpoint" not in payload assert payload["enabled_models"][0]["display_name"] == "anthropic/claude-sonnet-4.5" def test_normalize_payload_accepts_anthropic_provider_type(): payload = _normalize_payload( { "provider_id": "xiaomi-token-plan", "display_name": "Xiaomi Token Plan", "provider_type": "anthropic", "base_url": "https://token-plan-cn.xiaomimimo.com/anthropic", "capabilities": ["chat"], "enabled_models": [{"id": "mimo-v2.5-pro", "type": "chat", "source": "manual"}], } ) assert payload["provider_type"] == "anthropic" assert payload["enabled_models"][0]["id"] == "mimo-v2.5-pro" def test_normalize_payload_rejects_unknown_enabled_model_type(): with pytest.raises(ValueError, match="type 必须是"): _normalize_payload( { "provider_id": "openrouter-local", "display_name": "OpenRouter Local", "base_url": "https://openrouter.ai/api/v1", "enabled_models": [{"id": "unknown-model", "type": "unknown"}], } ) def test_normalize_payload_allows_embedding_without_dimension(): """embedding 模型的 dimension 是可选字段，不提供也不会报错。""" payload = _normalize_payload( { "provider_id": "embedding-local", "display_name": "Embedding Local", "base_url": "https://example.com/v1", "capabilities": ["embedding"], "embedding_base_url": "https://example.com/v1/embeddings", "enabled_models": [{"id": "text-embedding", "type": "embedding"}], } ) assert payload["provider_id"] == "embedding-local" assert payload["enabled_models"][0].get("dimension") is None def test_normalize_remote_model_preserves_detailed_model_config(): model = _normalize_remote_model( { "id": "xiaomi/mimo-v2-omni", "name": "Xiaomi: MiMo-V2-Omni", "context_length": 262144, "architecture": { "input_modalities": ["text", "audio", "image", "video"], "output_modalities": ["text"], }, "top_provider": {"max_completion_tokens": 65536}, "supported_parameters": ["temperature", "tools"], } ) assert model["id"] == "xiaomi/mimo-v2-omni" assert model["display_name"] == "Xiaomi: MiMo-V2-Omni" assert model["type"] == "chat" assert model["input_modalities"] == ["text", "audio", "image", "video"] assert model["max_completion_tokens"] == 65536 assert model["raw_metadata"]["supported_parameters"] == ["temperature", "tools"] def test_normalize_remote_model_uses_endpoint_model_type(): model = _normalize_remote_model({"id": "BAAI/bge-m3", "object": "model"}, "embedding") assert model["id"] == "BAAI/bge-m3" assert model["type"] == "embedding" @pytest.mark.asyncio async def test_fetch_remote_models_loads_embedding_only_when_capability_enabled(monkeypatch): calls = [] async def fake_fetch(client, provider, headers, endpoint, model_type): calls.append((endpoint, model_type)) return [{"id": f"{model_type}-model", "type": model_type}] monkeypatch.setattr("yuxi.models.providers.service._fetch_models_from_endpoint", fake_fetch) class Provider: base_url = "https://example.com/v1" api_key = None api_key_env = None headers_json = {} capabilities = ["chat", "embedding", "rerank"] models_endpoint = "/models" embedding_models_endpoint = "/embeddings/models" rerank_models_endpoint = None models = await fetch_remote_models(Provider()) assert calls == [("/models", "chat"), ("/embeddings/models", "embedding")] assert [model["type"] for model in models] == ["chat", "embedding"] def test_normalize_payload_rejects_ollama_provider_type(): with pytest.raises(ValueError, match="provider_type 必须是"): _normalize_payload( { "provider_id": "ollama-local", "display_name": "Ollama Local", "provider_type": "ollama", "base_url": "http://localhost:11434", } ) def test_builtin_provider_templates_default_to_openai_provider_type(): assert len(BUILTIN_PROVIDERS) >= 16 provider_types = { _normalize_payload( { "provider_id": provider["provider_id"], "display_name": provider["display_name"], "base_url": provider["base_url"], "provider_type": provider.get("provider_type"), } )["provider_type"] for provider in BUILTIN_PROVIDERS } assert provider_types == {"openai"} assert all("ollama" not in provider["provider_id"] for provider in BUILTIN_PROVIDERS) def test_builtin_siliconflow_provider_includes_default_runnable_models(): provider = next(item for item in BUILTIN_PROVIDERS if item["provider_id"] == "siliconflow-cn") models = {model["id"]: model for model in provider["enabled_models"]} assert provider["capabilities"] == ["chat", "embedding", "rerank"] assert provider["embedding_base_url"] == "https://api.siliconflow.cn/v1/embeddings" assert provider["rerank_base_url"] == "https://api.siliconflow.cn/v1/rerank" assert models["Pro/BAAI/bge-m3"]["type"] == "embedding" assert models["Pro/BAAI/bge-m3"]["dimension"] == 1024 assert "base_url_override" not in models["Pro/BAAI/bge-m3"] assert models["Pro/BAAI/bge-reranker-v2-m3"]["type"] == "rerank" assert "base_url_override" not in models["Pro/BAAI/bge-reranker-v2-m3"] def test_builtin_dashscope_provider_includes_default_embedding_and_rerank_models(): provider = next(item for item in BUILTIN_PROVIDERS if item["provider_id"] == "alibaba") models = {model["id"]: model for model in provider["enabled_models"]} assert provider["capabilities"] == ["chat", "embedding", "rerank"] assert provider["embedding_base_url"] == "https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings" assert provider["rerank_base_url"] == "https://dashscope.aliyuncs.com/compatible-api/v1/reranks" assert "embedding_models_endpoint" not in provider assert "rerank_models_endpoint" not in provider assert models["text-embedding-v4"]["type"] == "embedding" assert models["text-embedding-v4"]["dimension"] == 1024 assert models["qwen3-rerank"]["type"] == "rerank" def testcheck_credential_status_disabled_provider_always_ok(): """未启用的 provider 无论凭证如何配置，状态始终为 ok。""" class Provider: is_enabled = False api_key = None api_key_env = None assert check_credential_status(Provider()) == "ok" def testcheck_credential_status_direct_api_key_ok(): """直接配置了 api_key 的启用 provider 状态为 ok。""" class Provider: is_enabled = True api_key = "sk-test" api_key_env = None assert check_credential_status(Provider()) == "ok" def testcheck_credential_status_env_key_exists_ok(monkeypatch): """api_key_env 对应的环境变量存在时状态为 ok。""" monkeypatch.setenv("TEST_API_KEY", "exists") class Provider: is_enabled = True api_key = None api_key_env = "TEST_API_KEY" assert check_credential_status(Provider()) == "ok" def testcheck_credential_status_env_key_missing_warning(monkeypatch): """api_key_env 对应的环境变量不存在时状态为 warning。""" monkeypatch.delenv("MISSING_KEY", raising=False) class Provider: is_enabled = True api_key = None api_key_env = "MISSING_KEY" assert check_credential_status(Provider()) == "warning" def testcheck_credential_status_both_empty_warning(): """api_key 和 api_key_env 都未配置时状态为 warning。""" class Provider: is_enabled = True api_key = None api_key_env = None assert check_credential_status(Provider()) == "warning" # ==================== 手动添加模型 / source 字段 ==================== def test_normalize_payload_default_model_source_is_remote(): """未显式指定 source 时，规范化后默认填入 remote，向后兼容旧数据。""" payload = _normalize_payload( { "provider_id": "openrouter-local", "display_name": "OpenRouter Local", "base_url": "https://openrouter.ai/api/v1", "enabled_models": [{"id": "anthropic/claude-sonnet-4.5", "type": "chat"}], } ) assert payload["enabled_models"][0]["source"] == "remote" def test_normalize_payload_accepts_manual_source(): """source=manual 表示管理员手动添加的模型，规范化保留该标签。""" payload = _normalize_payload( { "provider_id": "custom-local", "display_name": "Custom Local", "base_url": "https://example.com/v1", "capabilities": ["chat"], "enabled_models": [{"id": "my-chat-model", "type": "chat", "source": "manual"}], } ) assert payload["enabled_models"][0]["source"] == "manual" def test_normalize_payload_rejects_invalid_source(): """source 仅允许 manual 或 remote，其他取值视为非法。""" with pytest.raises(ValueError, match="source 必须是"): _normalize_payload( { "provider_id": "custom-local", "display_name": "Custom Local", "base_url": "https://example.com/v1", "enabled_models": [{"id": "x", "type": "chat", "source": "custom"}], } ) def test_normalize_payload_rejects_model_type_not_in_capabilities(): """provider 仅声明 chat 能力时，不允许写入 embedding 类型的模型。""" with pytest.raises(ValueError, match="不在 provider 能力"): _normalize_payload( { "provider_id": "chat-only", "display_name": "Chat Only", "base_url": "https://example.com/v1", "capabilities": ["chat"], "enabled_models": [{"id": "rogue-embedding", "type": "embedding", "dimension": 1024}], } ) def test_normalize_payload_allows_model_type_within_capabilities(): """provider 同时声明 chat + embedding 时，两类模型均可正常写入。""" payload = _normalize_payload( { "provider_id": "multi-cap", "display_name": "Multi Cap", "base_url": "https://example.com/v1", "capabilities": ["chat", "embedding"], "embedding_base_url": "https://example.com/v1/embeddings", "embedding_models_endpoint": "/embeddings/models", "enabled_models": [ {"id": "chat-1", "type": "chat", "source": "manual"}, { "id": "embed-1", "type": "embedding", "source": "manual", "dimension": 1024, }, ], } ) types = [model["type"] for model in payload["enabled_models"]] sources = [model["source"] for model in payload["enabled_models"]] assert types == ["chat", "embedding"] assert sources == ["manual", "manual"]