92 lines
4.0 KiB
Python
92 lines
4.0 KiB
Python
"""Explicit opt-in: actual pinned model -> HTTP -> PostgreSQL -> HTTP process restart."""
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import os
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import pytest
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from sqlalchemy import text
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from test_runtime import server
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from app.services.embedding import SMALL_ZH
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@pytest.mark.skipif(
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os.environ.get("RUN_LOCAL_EMBEDDING_TEST") != "1",
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reason="requires optional CPU runtime and offline pinned model cache",
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)
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def test_real_local_model_vectors_retrieval_and_http_restart(migrated_database):
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options = {
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"EMBEDDING_ENABLED": "true",
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"EMBEDDING_MODEL": "small",
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"EMBEDDING_CACHE_DIR": os.environ["EMBEDDING_CACHE_DIR"],
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"HF_HUB_OFFLINE": "1",
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"HF_HUB_DISABLE_TELEMETRY": "1",
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"HF_HUB_DISABLE_IMPLICIT_TOKEN": "1",
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"TOKENIZERS_PARALLELISM": "false",
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}
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query = {"query": "合成测试中交通安排应查阅哪份资料？"}
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with server(migrated_database, environment_overrides=options) as client:
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ids = []
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for topic, body in [
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("交通", "【合成测试】占位交通资料介绍测试公交线路与班次的查询入口。"),
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("住宿", "【合成测试】占位住宿资料介绍测试房型与入住须知。"),
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("审核", "【合成测试】占位审核资料说明测试出处核验流程。"),
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]:
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item = client.post(
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"/api/knowledge",
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json={
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"title": f"{topic}合成测试",
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"content": body,
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"source_url": "https://example.com/",
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"collected_at": "2026-10-16T00:00:00Z",
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},
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).json()
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response = client.post(
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f"/api/knowledge/{item['id']}/review",
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json={
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"expected_version": item["version"],
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"verification_status": "verified",
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"reviewer": "test-reviewer",
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"verification_notes": "仅为模型/HTTP/数据库持久化测试，不是事实资料",
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},
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)
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assert response.status_code == 200
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ids.append(item["id"])
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text_ = client.post("/api/indexes", json={"chunk_size": 200, "overlap": 20}).json()
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assert (
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client.post(
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f"/api/indexes/{text_['id']}/publish", json={"expected_generation": 0}
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).status_code
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== 200
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)
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built = client.post(
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"/api/vector-indexes", json={"text_index_version_id": text_["id"]}, timeout=60
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)
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assert built.status_code == 201, built.text
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version = built.json()
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assert version["space_id"] == SMALL_ZH.space_id and version["dimension"] == 512
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published = client.post(
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f"/api/vector-indexes/{version['id']}/publish", json={"expected_generation": 0}
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)
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assert published.status_code == 200
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active = published.json()
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response = client.post("/api/retrieval/vector", json=query, timeout=60)
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assert response.status_code == 200, response.text
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first = response.json()
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assert {h["knowledge_id"] for h in first["results"]} == set(ids)
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assert all(-1 <= h["score"] <= 1 for h in first["results"])
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assert first["config"]["space"]["spec"]["revision"] == SMALL_ZH.revision
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with migrated_database.connect() as c:
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assert (
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c.scalar(
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text("SELECT count(*) FROM chunk_vectors WHERE public.vector_dims(embedding)=512")
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)
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== 3
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)
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assert c.scalar(text("SELECT count(*) FROM vector_publications")) == 1
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with server(migrated_database, environment_overrides=options) as client:
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assert client.get("/api/vector-indexes/active").json() == active
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assert client.get(f"/api/retrieval/runs/{first['run_id']}").json() == first
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response = client.post("/api/retrieval/vector", json=query, timeout=60)
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assert response.status_code == 200, response.text
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assert response.json()["results"] == first["results"]
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