feat(agent-core): 增加智能编排与模型工具基础

tests/test_llm_provider.py

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+from agent_core.llm_provider import (
+    EmbeddingConfigurationError,
+    LLMConfigurationError,
+    create_embedding_provider,
+    create_llm_provider,
+)
+
+
+def test_create_llm_provider_requires_api_key_for_openai_compatible():
+    provider = create_llm_provider(
+        {
+            "LLM_API_KEY": "",
+            "LLM_BASE_URL": "https://api.openai.com/v1",
+            "LLM_MODEL": "gpt-4.1-mini",
+            "LLM_PROVIDER": "openai_compatible",
+        }
+    )
+
+    response = provider.generate([{"role": "user", "content": "hello"}])
+
+    assert response.success is False
+    assert isinstance(response.error, LLMConfigurationError)
+    assert "LLM_API_KEY" in str(response.error)
+
+
+def test_mock_provider_returns_deterministic_content():
+    provider = create_llm_provider({"LLM_PROVIDER": "mock", "LLM_MODEL": "demo-model"})
+
+    response = provider.generate([{"role": "user", "content": "hello"}])
+
+    assert response.success is True
+    assert response.model_name == "demo-model"
+    assert "hello" in response.content
+
+
+def test_openai_compatible_provider_posts_chat_completion(monkeypatch):
+    captured = {}
+
+    class FakeResponse:
+        def __enter__(self):
+            return self
+
+        def __exit__(self, exc_type, exc, traceback):
+            return False
+
+        def read(self):
+            return b'{"choices":[{"message":{"content":"ok"}}],"model":"demo-model"}'
+
+    def fake_urlopen(request, timeout):
+        captured["url"] = request.full_url
+        captured["headers"] = dict(request.header_items())
+        captured["body"] = request.data.decode("utf-8")
+        return FakeResponse()
+
+    monkeypatch.setattr("agent_core.llm_provider.urlopen", fake_urlopen)
+    provider = create_llm_provider(
+        {
+            "LLM_PROVIDER": "openai_compatible",
+            "LLM_API_KEY": "sk-test",
+            "LLM_BASE_URL": "https://api.siliconflow.cn/v1",
+            "LLM_MODEL": "demo-model",
+        }
+    )
+
+    response = provider.generate([{"role": "user", "content": "hello"}])
+
+    assert response.success is True
+    assert response.content == "ok"
+    assert captured["url"] == "https://api.siliconflow.cn/v1/chat/completions"
+    assert '"model": "demo-model"' in captured["body"]
+    assert captured["headers"]["Authorization"] == "Bearer sk-test"
+
+
+def test_embedding_provider_uses_openai_compatible_embeddings(monkeypatch):
+    class FakeResponse:
+        def __enter__(self):
+            return self
+
+        def __exit__(self, exc_type, exc, traceback):
+            return False
+
+        def read(self):
+            return b'{"data":[{"embedding":[0.1,0.2]},{"embedding":[0.3,0.4]}]}'
+
+    monkeypatch.setattr("agent_core.llm_provider.urlopen", lambda request, timeout: FakeResponse())
+    provider = create_embedding_provider(
+        {
+            "EMBEDDING_API_KEY": "sk-test",
+            "EMBEDDING_BASE_URL": "https://api.siliconflow.cn/v1",
+            "EMBEDDING_MODEL": "demo-embedding",
+        }
+    )
+
+    assert provider.embed_texts(["a", "b"]) == [[0.1, 0.2], [0.3, 0.4]]
+
+
+def test_embedding_provider_requires_api_key():
+    provider = create_embedding_provider(
+        {
+            "EMBEDDING_API_KEY": "",
+            "EMBEDDING_BASE_URL": "https://api.siliconflow.cn/v1",
+            "EMBEDDING_MODEL": "demo-embedding",
+        }
+    )
+
+    try:
+        provider.embed_texts(["a"])
+    except EmbeddingConfigurationError as exc:
+        assert "EMBEDDING_API_KEY" in str(exc)
+    else:
+        raise AssertionError("expected EmbeddingConfigurationError")