jerryandClaude Fable 5
1e94de7464
- BFF 薄适配层（FastAPI，8090）：QA 代理/SSE 中继/Neo4j 直连/抽取代理/会话持久化 - Vue3 前端：五相位检索流水线、文献来源与证据链、知识图谱、提取任务四 tab - 四档响应式（桌面/笔记本/平板/手机）、深浅主题 - 81 个 FR 全部实现，测试追溯矩阵 81/81，后端 121 测/前端 156 测 - 真实凭据已脱敏，运行时配置在 backend/.env（不入 git） Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
206 lines
7.7 KiB
Python
206 lines
7.7 KiB
Python
"""services/test_normalize.py —— 归一化器单测。
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输入用 backend/mock/fixtures/upstream_real_shape_sample.json（真实上游形态样本，
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DISCOVERY.md §4.1），断言 API.md §7/§8/§9 的归一化输出关键字段。
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fixture 是单一事实源（ARCHITECTURE.md §6），本测试同时守护 mock/BFF 契约一致。
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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from app.services.normalize import (
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extract_chains,
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normalize_graph,
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normalize_hop,
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normalize_qa_response,
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normalize_source,
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pick,
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)
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FIXTURE = (
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Path(__file__).resolve().parents[2]
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/ "mock" / "fixtures" / "upstream_real_shape_sample.json"
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)
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def load_raw() -> dict:
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return json.loads(FIXTURE.read_text(encoding="utf-8"))
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# ---------------------------------------------------------------------------
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# pick
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# ---------------------------------------------------------------------------
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def test_pick_first_existing_key():
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obj = {"b": 2, "c": 3}
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assert pick(obj, "a", "b", "c") == 2
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assert pick(obj, "a") is None
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assert pick(obj, "a", default="x") == "x"
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assert pick(obj, "n", "b", default="y") == 2
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def test_pick_skips_none_values():
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assert pick({"a": None, "b": 1}, "a", "b") == 1
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# ---------------------------------------------------------------------------
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# §8 SourceItem
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# ---------------------------------------------------------------------------
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def test_normalize_source_all_keys_present():
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src = normalize_source(load_raw()["sources"][0])
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expected_keys = {
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"id", "title", "paper_id", "chunk_id", "pmid", "pmcid", "doi", "url",
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"snippet", "score", "locator", "section", "support_count", "type", "metadata",
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}
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assert expected_keys <= set(src.keys()) # 所有字段键必须存在（缺失为 null）
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assert src["id"] == "S1" # evidence_id → id
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assert src["title"] == "Genome-wide analysis of the SPL gene family in pecan"
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assert src["paper_id"] == "PMC5488377_12864_2017_Article_3882"
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assert src["pmid"] == "28655295"
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assert src["doi"] == "10.1186/s12864-017-3882-4"
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assert src["score"] == 0.982 # final_score → score
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assert src["snippet"].startswith("## Phylogenetic")
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assert src["url"] == "https://doi.org/10.1186/s12864-017-3882-4"
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# 上游 item 的 source 是断言主体实体名（非论文标题），落 metadata.source_entity
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assert src["metadata"]["source_entity"] == "SPL gene family"
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assert src["metadata"]["assertion_id"] == "cass_d12f62f9abffbd903b97"
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assert src["metadata"]["vector_score"] == 0.714
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assert src["metadata"]["rerank_score"] == 0.989
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assert src["metadata"]["output_layer"] == "main_kg"
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def test_normalize_source_title_preferred_over_source():
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src = normalize_source({"title": "Real Title", "source": "Entity Name"})
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assert src["title"] == "Real Title"
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assert src["metadata"]["source_entity"] == "Entity Name"
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def test_normalize_source_only_doi_derives_url():
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src = normalize_source({"doi": "10.1000/xyz"})
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assert src["url"] == "https://doi.org/10.1000/xyz"
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assert src["title"] is None
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assert src["metadata"] == {}
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def test_normalize_source_alias_chains():
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src = normalize_source({
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"content": "snippet text",
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"doc_id": "doc-1",
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"confidence": 0.5,
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"assertion_id": "cass_x",
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})
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assert src["snippet"] == "snippet text"
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assert src["paper_id"] == "doc-1"
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assert src["score"] == 0.5
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assert src["metadata"]["assertion_id"] == "cass_x"
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# ---------------------------------------------------------------------------
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# §9 图谱
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# ---------------------------------------------------------------------------
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def test_normalize_hop_aliases():
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hop = normalize_hop({
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"from_name": "A", "from_id": "cent_a", "source_type": "gene",
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"to_name": "B", "to_id": "cent_b", "target_type": "species",
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"relation": "characterized_in", "paper_id": "P1", "pmid": "1",
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"pmcid": None, "doi": "10.1/x",
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})
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assert hop["source"] == "A"
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assert hop["target"] == "B"
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assert hop["source_id"] == "cent_a"
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assert hop["target_id"] == "cent_b"
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assert hop["source_type"] == "gene"
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assert hop["target_type"] == "species"
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assert hop["relation"] == "characterized_in"
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assert hop["paper_id"] == "P1"
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def test_extract_chains_both_shapes():
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raw = load_raw()
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chains = extract_chains(raw) # 优先 native_graph_chains（列表形态）
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assert len(chains) == 1
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assert chains[0]["chain_id"] == "G1"
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assert len(chains[0]["hops"]) == 1
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# dict 形态（graph_knowledge_chains）→ 取 .chains
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chains_dict = extract_chains({"graph_knowledge_chains": raw["graph_knowledge_chains"]})
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assert len(chains_dict) == 1
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assert chains_dict[0]["chain_id"] == "G1"
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assert len(chains_dict[0]["hops"]) == 2
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def test_normalize_graph_nodes_edges_chains():
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g = normalize_graph(load_raw())
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assert isinstance(g, dict) and set(g.keys()) == {"nodes", "edges", "chains"}
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assert len(g["chains"]) == 1
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assert g["chains"][0]["chain_id"] == "G1"
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assert len(g["chains"][0]["hops"]) == 1 # native_graph_chains 里只有 1 跳
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# 节点：按 id 去重，label 取名字、type 取 source_type/target_type
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ids = [n["id"] for n in g["nodes"]]
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assert "cent_spl" in ids and "cent_oryza" in ids
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assert len(ids) == len(set(ids))
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by_id = {n["id"]: n for n in g["nodes"]}
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assert by_id["cent_spl"]["label"] == "SPL gene"
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assert by_id["cent_spl"]["type"] == "gene_family"
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assert by_id["cent_oryza"]["type"] == "species"
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# 边：带 chain_id / hop / paper_id
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assert len(g["edges"]) == 1
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edge = g["edges"][0]
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assert edge["source"] == "cent_spl"
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assert edge["target"] == "cent_oryza"
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assert edge["relation"] == "characterized_in"
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assert edge["label"] == "characterized_in"
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assert edge["chain_id"] == "G1"
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assert edge["hop"] == 0
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assert edge["paper_id"] == "PMC5488377_12864_2017_Article_3882"
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# ---------------------------------------------------------------------------
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# §7 归一化 QA 响应
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# ---------------------------------------------------------------------------
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def test_normalize_qa_response():
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out = normalize_qa_response(load_raw())
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assert out["query_id"] == "20260825_000715_49f801a9"
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assert out["version"] == "1.1.73"
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assert out["answer"] == "" # search 模式空
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assert out["success"] is True
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assert out["source_count"] == 1
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assert isinstance(out["sources"], list) and len(out["sources"]) == 1
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assert out["sources"][0]["id"] == "S1"
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assert out["used_sources"] == []
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# timing：真实键 timings → timing
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assert out["timing"]["total"] == 91.9
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# citation_validation → citation_map
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assert out["citation_map"]["valid"] is True
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# query_plan → retrieval_plan
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assert "agentic_plan" in out["retrieval_plan"]
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# graph 与 chains 同源
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assert len(out["graph"]["chains"]) == 1
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assert out["chains"][0]["chain_id"] == "G1"
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# raw 保存原始完整响应
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assert out["raw"]["question"] == "What is the SPL gene family?"
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def test_normalize_qa_response_alt_aliases():
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out = normalize_qa_response({
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"response": "answer text",
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"citations": [{"evidence_id": "E1", "paper_title": "T", "final_score": 0.9}],
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"used_sources": [],
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"agentic_plan": {"anchor": "x"},
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"timing": {"total_s": 1.0},
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"citation_map": {"valid": False},
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"notes": "n",
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})
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assert out["answer"] == "answer text"
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assert out["sources"][0]["title"] == "T"
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assert out["retrieval_plan"] == {"anchor": "x"}
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assert out["timing"] == {"total_s": 1.0}
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assert out["citation_map"] == {"valid": False}
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assert out["retrieval_notes"] == "n"
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