中期成果提交：PPT自动生成 Agent（感知-规划-行动-记忆闭环 + aura-ppt 渲染引擎 + Web 交互界面 + 测试用例 + AI 使用日志）

tests/e2e_llm.py

@@ -0,0 +1,153 @@
+# -*- coding: utf-8 -*-
+"""LLM 接入集成测试：规划/标题/意图/禁止降级/长文档两级规划。
+
+用法：python tests/e2e_llm.py
+"""
+import os
+import sys
+import time
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+os.environ["PLAN_CHUNK_CHARS"] = "2500"  # 压低阈值，确保长文档用例真正走分段通读
+
+from src.agent import llm  # noqa: E402
+from src.agent.perception import GenerateConfig  # noqa: E402
+from src.agent import planning as P  # noqa: E402
+from src.agent.planning import build_plan, PlanningError  # noqa: E402
+from src.agent.chat import parse_intent_llm, parse_intent  # noqa: E402
+
+SAMPLE = """# 项目背景
+为提升知识周转效率启动平台建设，前期调研两个月
+# 本期成果
+部署完成基础服务，导入文档120份
+混合检索准确率较基线提升明显
+完成三个部门试点接入
+# 下一步
+扩大试点范围，建立运营机制
+"""
+
+failures = []
+
+
+def check(name, cond, extra=""):
+    print(f"  [{'OK' if cond else 'NG'}] {name} {extra}")
+    if not cond:
+        failures.append(name)
+
+
+def make_long_doc(sections=40):
+    """构造约 1.5-2 万字符的多节长文档（含数字，验证分段通读与页数硬约束）。"""
+    parts = []
+    for i in range(1, sections + 1):
+        parts.append(f"# 第{i}章节 阶段性工作汇报")
+        parts.append(f"本阶段完成任务{i}的设计与开发，投入人力3人，周期2周")
+        parts.append(f"完成度达到{i*2}%，质量抽检合格率98%")
+        parts.append("- 关键交付物已通过评审")
+        parts.append("- 遗留问题已登记跟踪")
+        parts.append("下一步将推进与业务系统的对接联调")
+    return "\n".join(parts)
+
+
+def main():
+    print("== 0. 禁止降级：LLM 失败必须报错而非照搬 ==")
+    cfg_nf = GenerateConfig(title="降级测试", user_id=1, content=SAMPLE,
+                            page_min=5, page_max=8)
+    orig_chat = llm.chat_json
+    try:
+        llm.chat_json = lambda *a, **k: None
+        try:
+            build_plan(cfg_nf)
+            check("no silent fallback", False, "未抛出 PlanningError")
+        except PlanningError as e:
+            check("no silent fallback", True, str(e)[:50])
+    finally:
+        llm.chat_json = orig_chat
+
+    ok = llm.ensure_backend()
+    check("llm backend", ok)
+
+    print("== 1. LLM 内容规划（短文档直通） ==")
+    t = time.time()
+    cfg = GenerateConfig(title="知识平台周报", user_id=1, content=SAMPLE,
+                         scene="report", language="zh", page_min=6, page_max=9)
+    r = build_plan(cfg)
+    plan = r.plan
+    slides = plan["slides"]
+    print(f"  mode={r.mode} pages={len(slides)} 耗时={time.time()-t:.0f}s")
+    check("llm mode used", r.mode == "llm")
+    check("page count in range", 6 <= len(slides) <= 9, f"n={len(slides)}")
+    check("cover first / end last", slides[0]["type"] == "cover" and slides[-1]["type"] == "end")
+    notes_n = sum(1 for s in slides if s.get("notes"))
+    check("per-slide notes", notes_n >= len(slides) - 2, f"notes={notes_n}")
+    generic = sum(1 for s in slides if s["title"] in ("",) or s["title"].startswith("要点"))
+    check("no generic titles", generic == 0)
+
+    def avg_len(sl):
+        bl = [len(b) for x in sl if x["type"] == "content" for b in x.get("content", [])]
+        return sum(bl) / max(len(bl), 1)
+    check("full-sentence bullets (short doc)", avg_len(slides) >= 12,
+          f"avg={avg_len(slides):.0f}")
+    for s in slides:
+        print(f"    - [{s['type']:8s}] {s['title'][:24]} notes={'Y' if s.get('notes') else '-'}")
+
+    print("== 2. 长文档两级规划（分段通读→汇总大纲） ==")
+    long_doc = make_long_doc()
+    t = time.time()
+    cfg2 = GenerateConfig(title="季度工作总结", user_id=1, content=long_doc,
+                          scene="report", language="zh", page_min=10, page_max=15)
+    r2 = build_plan(cfg2)
+    slides2 = r2.plan["slides"]
+    n2 = len(slides2)
+    print(f"  mode={r2.mode} pages={n2} 源={len(long_doc)}字 耗时={time.time()-t:.0f}s")
+    check("long doc page hard cap", n2 <= 15 and n2 >= 4, f"n={n2}")
+    notes2 = sum(1 for s in slides2 if s.get("notes"))
+    check("long doc has notes", notes2 >= n2 - 2, f"notes={notes2}")
+    covered = sum(1 for i in range(1, 41) if str(i) in "".join(
+        s["title"] + "".join(s["content"]) for s in slides2))
+    check("late sections represented", covered >= 3, f"covered_sections={covered}")
+    check("full-sentence bullets (long doc)", avg_len(slides2) >= 12,
+          f"avg={avg_len(slides2):.0f}")
+
+    print("== 3. 智能标题 ==")
+    t = time.time()
+    data = llm.chat_json(
+        "根据以下 PPT 内容拟 3 个标题（每个不超过 20 字）。"
+        '输出 JSON {"titles": ["..."]}。\n\n' + SAMPLE)
+    titles = [str(x).strip() for x in (data or {}).get("titles", []) if str(x).strip()]
+    print(f"  titles={titles} 耗时={time.time()-t:.0f}s")
+    check("title suggest", len(titles) >= 1)
+
+    print("== 4. LLM 意图解析（规则无法处理的句式） ==")
+    msg = '第2页末尾添加一条要点：风险与依赖已同步全组'
+    rule = parse_intent(msg, {})
+    llm_it = parse_intent_llm(msg, plan)
+    check("rule cannot parse (no quotes)", rule["type"] == "unknown", str(rule))
+    check("llm parses add", llm_it and llm_it.get("type") == "edit_page"
+          and llm_it.get("op") == "add" and llm_it.get("page_no") == 2,
+          str(llm_it))
+    msg2 = "把标题改成 AI 知识平台建设汇报"
+    it2 = parse_intent_llm(msg2, plan)
+    check("llm parses title", it2 and it2.get("type") == "change_title"
+          and "知识平台" in it2.get("title", ""), str(it2))
+
+    print("== 5. 渲染 LLM plan（含 notes） ==")
+    import json
+    import tempfile
+    from src.engine_bridge import render_plan, verify_output
+    tmp = Path(tempfile.mkdtemp(prefix="llm_plan_"))
+    p = tmp / "plan.json"
+    p.write_text(json.dumps(plan, ensure_ascii=False, indent=2), encoding="utf-8")
+    out = tmp / "out.pptx"
+    render_plan(p, out)
+    vok, detail = verify_output(out, p)
+    check("render+verify llm plan", vok, detail[:60])
+
+    print("\n" + ("LLM E2E ALL PASSED" if not failures else "FAILURES: " + "; ".join(failures)))
+    sys.exit(1 if failures else 0)
+
+
+if __name__ == "__main__":
+    main()