feat(agent-core): 增加智能编排与模型工具基础

tests/test_agent_core.py

@@ -0,0 +1,122 @@
+from agent_core.orchestrator import run_agent
+from agent_core.rag.ingest import ingest_document
+from agent_core.rag.retriever import retrieve
+
+
+def test_run_agent_returns_structured_mock_result():
+    scenario = {
+        "id": "knowledge_qa",
+        "name": "知识库问答助手",
+        "rag": {"enabled": True, "collection": "knowledge_qa", "top_k": 3},
+        "tools": ["generate_action_items"],
+        "output": {"type": "general_answer"},
+    }
+
+    result = run_agent(scenario, "如何处理异常？")
+
+    assert result.status == "success"
+    assert result.answer
+    assert result.structured_output["output_type"] == "general_answer"
+    assert isinstance(result.references, list)
+    assert result.tool_calls[0]["tool_name"] == "generate_action_items"
+
+
+def test_rag_ingest_and_retrieve_filters_by_scenario_and_query(tmp_path):
+    store_path = tmp_path / "rag_store.json"
+    text = "设备点检需要先断电挂牌。质量异常需要记录批次、工位和缺陷现象。"
+
+    result = ingest_document(
+        scenario_id="quality_analysis",
+        source_file="quality.md",
+        text=text,
+        collection="quality_analysis",
+        store_path=store_path,
+    )
+    ingest_document(
+        scenario_id="risk_audit",
+        source_file="risk.md",
+        text="报销审核需要检查发票、金额和审批链。",
+        collection="risk_audit",
+        store_path=store_path,
+    )
+
+    chunks = retrieve(
+        scenario_id="quality_analysis",
+        query="质量异常批次",
+        collection="quality_analysis",
+        top_k=3,
+        store_path=store_path,
+    )
+
+    assert result.success is True
+    assert result.chunks_count >= 1
+    assert chunks
+    assert chunks[0]["source"] == "quality.md"
+    assert "质量异常" in chunks[0]["content"]
+    assert all(chunk["scenario_id"] == "quality_analysis" for chunk in chunks)
+
+
+def test_rag_reingest_replaces_same_document_and_retrieve_filters_document_ids(tmp_path):
+    store_path = tmp_path / "rag_store.json"
+
+    ingest_document(
+        document_id=1,
+        scenario_id="knowledge_qa",
+        source_file="old.md",
+        text="旧制度要求人工登记。",
+        collection="knowledge_qa",
+        store_path=store_path,
+    )
+    ingest_document(
+        document_id=1,
+        scenario_id="knowledge_qa",
+        source_file="new.md",
+        text="新制度要求系统自动登记。",
+        collection="knowledge_qa",
+        store_path=store_path,
+    )
+    ingest_document(
+        document_id=2,
+        scenario_id="knowledge_qa",
+        source_file="other.md",
+        text="系统自动登记后需要生成审计记录。",
+        collection="knowledge_qa",
+        store_path=store_path,
+    )
+
+    chunks = retrieve(
+        scenario_id="knowledge_qa",
+        query="系统自动登记",
+        collection="knowledge_qa",
+        top_k=5,
+        document_ids=[1],
+        store_path=store_path,
+    )
+
+    assert chunks
+    assert {chunk["document_id"] for chunk in chunks} == {1}
+    assert all(chunk["source"] == "new.md" for chunk in chunks)
+    assert all("旧制度" not in chunk["content"] for chunk in chunks)
+
+
+def test_run_agent_uses_retrieved_document_chunks(tmp_path):
+    store_path = tmp_path / "rag_store.json"
+    ingest_document(
+        scenario_id="knowledge_qa",
+        source_file="sop.md",
+        text="异常处理 SOP：先隔离现场，再通知负责人。",
+        collection="knowledge_qa",
+        store_path=store_path,
+    )
+    scenario = {
+        "id": "knowledge_qa",
+        "name": "知识库问答助手",
+        "rag": {"enabled": True, "collection": "knowledge_qa", "top_k": 3},
+        "tools": [],
+        "output": {"type": "general_answer"},
+    }
+
+    result = run_agent(scenario, "异常处理怎么做？", options={"rag_store_path": store_path})
+
+    assert result.references[0]["source"] == "sop.md"
+    assert "隔离现场" in result.references[0]["content"]

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")