| """Query the SCUT-SS RAG library.
|
|
|
| python scripts/rag_query.py "软件测试的黑盒测试有哪些方法"
|
| python scripts/rag_query.py "数据库范式" -k 5 --course "数据库系统"
|
| python scripts/rag_query.py "解释一下 ATAM" --ask # needs LLM env vars
|
|
|
| LLM env vars (optional, any OpenAI-compatible endpoint):
|
| OPENAI_API_KEY / OPENAI_BASE_URL / OPENAI_MODEL
|
| """
|
|
|
| import argparse
|
| import json
|
| import sys
|
| import textwrap
|
| from pathlib import Path
|
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent))
|
| import llm
|
| from vectorstore import Index
|
|
|
| ROOT = Path(__file__).resolve().parent.parent
|
| DEFAULT_INDEX = ROOT / "vectorstore"
|
|
|
| PROMPT = """你是华南理工大学软件学院的学习助手。请只依据下面提供的资料回答问题；
|
| 资料里没有的内容请明确说明"资料中没有提到"，不要编造。回答用中文，尽量给出要点式结构，
|
| 并在关键结论后标注来源（用 [1] [2] 对应资料编号）。
|
|
|
| 资料：
|
| {context}
|
|
|
| 问题：{question}
|
| """
|
|
|
|
|
| def retrieve(index, query, k, course=None):
|
| hits = index.search(query, k=max(k * 6, 20))
|
| if course:
|
| hits = [h for h in hits if course in (h[0].get("course") or "")] or hits
|
| return hits[:k]
|
|
|
|
|
| def build_context(hits):
|
| parts = []
|
| for i, (chunk, score) in enumerate(hits, 1):
|
| parts.append(
|
| f"[{i}] 来源：{chunk['path']}｜{chunk.get('section', '')}\nURL: {chunk.get('url', '')}\n{chunk['text']}\n"
|
| )
|
| return "\n".join(parts)
|
|
|
|
|
| def ask_llm(prompt, provider=None, base_url=None, api_key=None, model=None):
|
| """Call a cloud model (any OpenAI-compatible endpoint). Returns (answer, conf) or (None, err)."""
|
| try:
|
| answer, conf = llm.chat(
|
| [
|
| {"role": "system", "content": "你是一个严谨的课程学习助手。"},
|
| {"role": "user", "content": prompt},
|
| ],
|
| provider=provider,
|
| base_url=base_url,
|
| api_key=api_key,
|
| model=model,
|
| )
|
| return answer, conf
|
| except Exception as exc:
|
| return None, llm.http_error_hint(exc)
|
|
|
|
|
| def main():
|
| ap = argparse.ArgumentParser()
|
| ap.add_argument("question")
|
| ap.add_argument("-k", type=int, default=5)
|
| ap.add_argument("--course", default=None, help="limit to one course, e.g. 软件测试")
|
| ap.add_argument("--index", default=str(DEFAULT_INDEX))
|
| ap.add_argument("--ask", action="store_true", help="call a cloud LLM with the retrieved context")
|
| ap.add_argument("--provider", default=None, help="one of: " + ", ".join(llm.available_presets()))
|
| ap.add_argument("--base-url", default=None)
|
| ap.add_argument("--model", default=None)
|
| ap.add_argument(
|
| "--save-context",
|
| default=None,
|
| metavar="FILE",
|
| help="dump question+context to a Markdown file so an external AI (IDE/chat) can read it",
|
| )
|
| ap.add_argument("--json", action="store_true")
|
| args = ap.parse_args()
|
|
|
| for _s in (sys.stdout, sys.stderr):
|
| try:
|
| _s.reconfigure(encoding="utf-8", errors="replace")
|
| except Exception:
|
| pass
|
|
|
| index = Index(args.index)
|
| hits = retrieve(index, args.question, args.k, args.course)
|
|
|
| if args.json:
|
| print(json.dumps([{"score": s, **c} for c, s in hits], ensure_ascii=False, indent=2))
|
| return
|
|
|
| context = build_context(hits)
|
|
|
| if args.save_context:
|
| out = Path(args.save_context)
|
| out.parent.mkdir(parents=True, exist_ok=True)
|
| out.write_text(
|
| "# 请基于以下资料回答问题\n\n"
|
| f"**问题**：{args.question}\n\n"
|
| "**要求**：只依据下列资料作答；资料未覆盖的内容请明确说明「资料中没有提到」；"
|
| "关键结论后标注来源编号 [1] [2]。\n\n"
|
| "## 检索到的资料\n\n" + context,
|
| encoding="utf-8",
|
| )
|
| print(f"上下文已写入 {out} —— 在 IDE / 聊天窗口里 @ 这个文件，让外部模型回答即可。")
|
|
|
| if args.ask:
|
| answer, conf = ask_llm(
|
| PROMPT.format(context=context, question=args.question),
|
| provider=args.provider,
|
| base_url=args.base_url,
|
| model=args.model,
|
| )
|
| if answer is None:
|
| print(f"调用云端模型失败：{conf}\n下面是检索到的资料，可用 --save-context 导出后交给外部模型：\n")
|
| else:
|
| print(f"[{conf.get('provider') or 'custom'} | {conf['model']} @ {conf['base_url']}]\n")
|
| print(answer)
|
| print("\n" + "=" * 60 + "\n")
|
|
|
| print(f"检索到 {len(hits)} 条资料（backend={index.meta['backend']}）：\n")
|
| for i, (chunk, score) in enumerate(hits, 1):
|
| head = f"[{i}] {chunk['path']}" + (f" · {chunk['section']}" if chunk.get("section") else "")
|
| print(head)
|
| print(f" score={score:.4f} {chunk.get('url','')}")
|
| print(textwrap.indent(textwrap.shorten(chunk["text"], 300, placeholder=" ..."), " "))
|
| print()
|
|
|
|
|
| if __name__ == "__main__":
|
| main()
|
|
|
