generated from kgod/ai-review-template
hypandClaude
ae2d9b128d
自内部仓库剥离深度优化与 RAG 知识库后的交付版本： - Builder 对话式简历生成（FSM + 意图路由 LLM 兜底增强） - 条目级轻度优化：事实覆盖门禁 + STAR/bullet 修复链，功能/简介/成果与技术栈同级保护 - 简历导入：DOCX/PDF 解析、结构归一、手机号脱敏 - PostgreSQL 运行时 + Alembic 迁移链 Co-Authored-By: Claude <noreply@anthropic.com>
30 lines
807 B
Python
30 lines
807 B
Python
from __future__ import annotations
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import os
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from sqlalchemy import create_engine, text
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def test_langgraph_runtime_is_available() -> None:
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from langgraph.graph import START, StateGraph
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graph = StateGraph(dict)
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graph.add_node("finish", lambda state: state)
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graph.add_edge(START, "finish")
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assert graph.compile().invoke({}) == {}
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def test_postgres_test_database_has_pgvector() -> None:
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database_url = os.environ["RESUME_AGENT_TEST_DATABASE_URL"]
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engine = create_engine(database_url)
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try:
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with engine.connect() as connection:
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extension = connection.execute(
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text("SELECT extname FROM pg_extension WHERE extname = 'vector'")
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).scalar_one_or_none()
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finally:
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engine.dispose()
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assert extension == "vector"
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