52d57c3be7
- 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
105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
from __future__ import annotations
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from unittest.mock import patch
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import pytest
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from app.services.embedding_provider import EmbeddingProvider, _runtime_model_config_from_dict
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from app.services.knowledge_rag_runtime import KnowledgeRagError, RuntimeModelConfig
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def _config(provider: str = "GLM") -> RuntimeModelConfig:
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return RuntimeModelConfig(
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slot="embedding",
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provider=provider,
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model="Embedding-3",
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endpoint="https://open.bigmodel.cn/api/paas/v4/",
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api_key="k",
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capability="embedding",
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)
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def test_runtime_model_config_from_dict_maps_fields() -> None:
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cfg = _runtime_model_config_from_dict(
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{
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"slot": "embedding",
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"provider": "GLM",
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"model": "Embedding-3",
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"endpoint": "https://e",
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"apiKey": "secret",
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"capability": "embedding",
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}
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)
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assert cfg.api_key == "secret"
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assert cfg.model == "Embedding-3"
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def test_embed_empty_texts_returns_empty() -> None:
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provider = EmbeddingProvider(_config())
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assert provider.embed([]) == []
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def test_embed_returns_vectors_and_caches_dimension() -> None:
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provider = EmbeddingProvider(_config())
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with patch(
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"app.services.embedding_provider._request_embeddings_public",
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return_value=[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
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) as mock_req:
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vectors = provider.embed(["a", "b"])
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assert vectors == [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
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assert provider.dimension() == 3
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calls_after_first_dimension = mock_req.call_count
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# 第二次 dimension 不应再次请求
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assert provider.dimension() == 3
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assert mock_req.call_count == calls_after_first_dimension
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def test_dimension_raises_on_invalid_vectors() -> None:
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provider = EmbeddingProvider(_config())
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with patch(
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"app.services.embedding_provider._request_embeddings_public",
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return_value=[],
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):
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with pytest.raises(KnowledgeRagError):
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provider.dimension()
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def test_request_embeddings_public_glm_branch() -> None:
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cfg = _config("GLM")
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with patch(
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"app.services.embedding_provider._send_json_request",
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return_value=(200, {"data": [{"embedding": [0.1, 0.2]}]}),
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) as mock_send:
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from app.services.embedding_provider import _request_embeddings_public
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vectors = _request_embeddings_public(cfg, ["x"])
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assert vectors == [[0.1, 0.2]]
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called_url = mock_send.call_args.args[1]
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assert called_url.endswith("/embeddings")
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def test_request_embeddings_public_ollama_branch() -> None:
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cfg = _config("Ollama")
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with patch(
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"app.services.embedding_provider._send_json_request",
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return_value=(200, {"embeddings": [[0.5, 0.6]]}),
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) as mock_send:
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from app.services.embedding_provider import _request_embeddings_public
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vectors = _request_embeddings_public(cfg, ["x"])
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assert vectors == [[0.5, 0.6]]
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called_url = mock_send.call_args.args[1]
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assert called_url.endswith("/api/embed")
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def test_request_embeddings_public_raises_on_http_error() -> None:
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cfg = _config("GLM")
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with patch(
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"app.services.embedding_provider._send_json_request",
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return_value=(500, {"message": "boom"}),
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):
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from app.services.embedding_provider import _request_embeddings_public
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with pytest.raises(KnowledgeRagError):
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_request_embeddings_public(cfg, ["x"])
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