Initial commit

skills/quiz-mastery/scripts/generate_from_material.py

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+#!/usr/bin/env python3
+"""从学习资料生成知识点提取 prompt。
+
+用法：python3 generate_from_material.py <file_path> <document_id>
+
+输出：JSON 格式的 prompt（system_prompt + user_prompt），由 agent 发给 LLM 执行。
+LLM 返回知识点 JSON 后，agent 应调用 service.save_knowledge_points() 保存。
+"""
+from pathlib import Path
+import json
+import sys
+
+sys.path.append(str(Path(__file__).resolve().parents[1] / "src"))
+
+from quiz_mastery.file_parser import parse_file, build_extraction_prompt
+
+
+def main() -> None:
+    if len(sys.argv) < 3:
+        print("Usage: generate_from_material.py <file_path> <document_id>")
+        print("  file_path:   Path to study material (.md, .txt, .text)")
+        print("  document_id: Identifier for this document")
+        sys.exit(1)
+
+    file_path = sys.argv[1]
+    document_id = sys.argv[2]
+
+    content = parse_file(file_path)
+    prompts = build_extraction_prompt(content)
+
+    output = {
+        "action": "extract_knowledge_points",
+        "document_id": document_id,
+        "file_path": file_path,
+        "prompts": prompts,
+        "instructions": (
+            "Send the system_prompt and user_prompt to an LLM. "
+            "The LLM should return a JSON array of knowledge points. "
+            "Then call save_knowledge_points(document_id, knowledge_points) to save."
+        ),
+    }
+
+    print(json.dumps(output, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()