test(flywheel): 补 few-shot 飞轮单测并沉淀开发文档

- embedding_provider：GLM/Ollama 分支、维度缓存、HTTP 错误降级
- few_shot_ingestion：confirmed/false_positive 入库、ignored 跳过、幂等去重、
  create_feedback hook 触发、feature flag、吞异常
- few_shot_retrieval：去重、token 预算、超长截断；prompt 注入合并 examples + 向后兼容
- 容器内新增测试 20 passed；回归测试 35 passed（RAG/risk_observations/rule_generation）
- 沉淀 document/development/2026-07-03/feature/ai-data-flywheel 概念文档与 TODO，
  飞轮 1 已勾选证据，飞轮 2-6 待后续迭代

server/tests/test_embedding_provider.py

@@ -0,0 +1,104 @@
+from __future__ import annotations
+
+from unittest.mock import patch
+
+import pytest
+
+from app.services.embedding_provider import EmbeddingProvider, _runtime_model_config_from_dict
+from app.services.knowledge_rag_runtime import KnowledgeRagError, RuntimeModelConfig
+
+
+def _config(provider: str = "GLM") -> RuntimeModelConfig:
+    return RuntimeModelConfig(
+        slot="embedding",
+        provider=provider,
+        model="Embedding-3",
+        endpoint="https://open.bigmodel.cn/api/paas/v4/",
+        api_key="k",
+        capability="embedding",
+    )
+
+
+def test_runtime_model_config_from_dict_maps_fields() -> None:
+    cfg = _runtime_model_config_from_dict(
+        {
+            "slot": "embedding",
+            "provider": "GLM",
+            "model": "Embedding-3",
+            "endpoint": "https://e",
+            "apiKey": "secret",
+            "capability": "embedding",
+        }
+    )
+    assert cfg.api_key == "secret"
+    assert cfg.model == "Embedding-3"
+
+
+def test_embed_empty_texts_returns_empty() -> None:
+    provider = EmbeddingProvider(_config())
+    assert provider.embed([]) == []
+
+
+def test_embed_returns_vectors_and_caches_dimension() -> None:
+    provider = EmbeddingProvider(_config())
+    with patch(
+        "app.services.embedding_provider._request_embeddings_public",
+        return_value=[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
+    ) as mock_req:
+        vectors = provider.embed(["a", "b"])
+        assert vectors == [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
+        assert provider.dimension() == 3
+        calls_after_first_dimension = mock_req.call_count
+        # 第二次 dimension 不应再次请求
+        assert provider.dimension() == 3
+        assert mock_req.call_count == calls_after_first_dimension
+
+
+def test_dimension_raises_on_invalid_vectors() -> None:
+    provider = EmbeddingProvider(_config())
+    with patch(
+        "app.services.embedding_provider._request_embeddings_public",
+        return_value=[],
+    ):
+        with pytest.raises(KnowledgeRagError):
+            provider.dimension()
+
+
+def test_request_embeddings_public_glm_branch() -> None:
+    cfg = _config("GLM")
+    with patch(
+        "app.services.embedding_provider._send_json_request",
+        return_value=(200, {"data": [{"embedding": [0.1, 0.2]}]}),
+    ) as mock_send:
+        from app.services.embedding_provider import _request_embeddings_public
+
+        vectors = _request_embeddings_public(cfg, ["x"])
+        assert vectors == [[0.1, 0.2]]
+        called_url = mock_send.call_args.args[1]
+        assert called_url.endswith("/embeddings")
+
+
+def test_request_embeddings_public_ollama_branch() -> None:
+    cfg = _config("Ollama")
+    with patch(
+        "app.services.embedding_provider._send_json_request",
+        return_value=(200, {"embeddings": [[0.5, 0.6]]}),
+    ) as mock_send:
+        from app.services.embedding_provider import _request_embeddings_public
+
+        vectors = _request_embeddings_public(cfg, ["x"])
+        assert vectors == [[0.5, 0.6]]
+        called_url = mock_send.call_args.args[1]
+        assert called_url.endswith("/api/embed")
+
+
+def test_request_embeddings_public_raises_on_http_error() -> None:
+    cfg = _config("GLM")
+    with patch(
+        "app.services.embedding_provider._send_json_request",
+        return_value=(500, {"message": "boom"}),
+    ):
+        from app.services.embedding_provider import _request_embeddings_public
+
+        with pytest.raises(KnowledgeRagError):
+            _request_embeddings_public(cfg, ["x"])