feat: IMAP mail sync with thread-to-contact matching (PRD §6.1)
Real IMAP connection via imapflow library: - Connects to IMAP server (IMAPFlow, TLS on port 993) - Fetches recent messages (configurable limit, default 20) - Extracts: from, fromEmail, subject, date, snippet (200 chars) - Thread-to-contact matching (PRD §6.1): 1. Match sender email against persons table 2. If no person match, extract domain and match against company.website 3. Returns matchedCompany + matchedPerson for each email - Audit-logged (action: crm.mailsync:imap) - Zod validated (host, port, user, password, folder, limit) - 502 on IMAP connection failure with detail - 400 on validation failure Tested: invalid creds → 502 'IMAP connection failed', missing host → 400
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#!/usr/bin/env python3
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"""从学习资料生成知识点提取 prompt。
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用法：python3 generate_from_material.py <file_path> <document_id>
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输出：JSON 格式的 prompt（system_prompt + user_prompt），由 agent 发给 LLM 执行。
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LLM 返回知识点 JSON 后，agent 应调用 service.save_knowledge_points() 保存。
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"""
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from pathlib import Path
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import json
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import sys
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sys.path.append(str(Path(__file__).resolve().parents[1] / "src"))
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from quiz_mastery.file_parser import parse_file, build_extraction_prompt
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def main() -> None:
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if len(sys.argv) < 3:
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print("Usage: generate_from_material.py <file_path> <document_id>")
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print(" file_path: Path to study material (.md, .txt, .text)")
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print(" document_id: Identifier for this document")
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sys.exit(1)
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file_path = sys.argv[1]
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document_id = sys.argv[2]
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content = parse_file(file_path)
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prompts = build_extraction_prompt(content)
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output = {
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"action": "extract_knowledge_points",
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"document_id": document_id,
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"file_path": file_path,
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"prompts": prompts,
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"instructions": (
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"Send the system_prompt and user_prompt to an LLM. "
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"The LLM should return a JSON array of knowledge points. "
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"Then call save_knowledge_points(document_id, knowledge_points) to save."
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),
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}
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print(json.dumps(output, ensure_ascii=False, indent=2))
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if __name__ == "__main__":
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main()
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Executable
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#!/usr/bin/env python3
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"""从题目文件导入题目。
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用法：python3 import_quiz.py <file_path> <document_id> <user_id>
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输出：JSON 格式的 prompt（system_prompt + user_prompt），由 agent 发给 LLM 解析题目。
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LLM 返回题目 JSON 后，agent 应调用 service.import_questions() 导入。
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"""
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from pathlib import Path
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import json
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import sys
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sys.path.append(str(Path(__file__).resolve().parents[1] / "src"))
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from quiz_mastery.file_parser import parse_file
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from quiz_mastery.quiz_extractor import build_extraction_prompt
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def main() -> None:
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if len(sys.argv) < 4:
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print("Usage: import_quiz.py <file_path> <document_id> <user_id>")
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print(" file_path: Path to question file (.md, .txt, .text)")
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print(" document_id: Identifier for this document")
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print(" user_id: User identifier")
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sys.exit(1)
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file_path = sys.argv[1]
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document_id = sys.argv[2]
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user_id = sys.argv[3]
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content = parse_file(file_path)
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prompts = build_extraction_prompt(content)
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output = {
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"action": "import_questions",
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"document_id": document_id,
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"user_id": user_id,
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"file_path": file_path,
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"prompts": prompts,
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"instructions": (
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"Send the system_prompt and user_prompt to an LLM. "
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"The LLM should return a JSON array of questions. "
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"Then call service.import_questions(document_id, user_id, questions) to import."
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),
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}
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print(json.dumps(output, ensure_ascii=False, indent=2))
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if __name__ == "__main__":
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main()
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Executable
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#!/usr/bin/env python3
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"""生成出题 prompt。
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用法：python3 run_quiz.py <user_id> <document_id>
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根据已保存的知识点和用户掌握度记录，自动决定出题难度。
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- 从 mastery_records 读取每个知识点的 current_level
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- 首次出题的知识点强制 level=1
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- 输出 JSON 格式的 prompt（system_prompt + user_prompt），由 agent 发给 LLM 生成题目
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也支持指定知识点和难度：
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python3 run_quiz.py <user_id> <document_id> [level] [kp_id1,kp_id2,...]
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"""
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from pathlib import Path
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import json
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import sys
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sys.path.append(str(Path(__file__).resolve().parents[1] / "src"))
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from quiz_mastery import QuizMasteryService
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def main() -> None:
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if len(sys.argv) < 3:
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print("Usage: run_quiz.py <user_id> <document_id> [level] [kp_id1,kp_id2,...]")
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print(" user_id: User identifier")
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print(" document_id: Document identifier")
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print(" level: Optional difficulty level (1/2/3)")
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print(" kp_ids: Optional comma-separated knowledge point IDs")
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sys.exit(1)
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user_id = sys.argv[1]
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document_id = sys.argv[2]
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level = None
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kp_ids = None
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if len(sys.argv) >= 4:
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try:
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level = int(sys.argv[3])
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except ValueError:
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# Maybe it's kp_ids instead
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kp_ids = sys.argv[3].split(",")
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if len(sys.argv) >= 5:
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kp_ids = sys.argv[4].split(",")
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service = QuizMasteryService(
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base_dir=Path(__file__).resolve().parents[1] / "data"
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)
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result = service.generate_quiz_for_user(
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user_id=user_id,
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document_id=document_id,
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knowledge_point_ids=kp_ids,
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level=level,
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)
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print(json.dumps(result, ensure_ascii=False, indent=2))
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if __name__ == "__main__":
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main()
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#!/usr/bin/env python3
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"""提交测验答案并评分。
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用法：python3 submit_answers.py <user_id> <document_id> <session_id> <answers_json>
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参数：
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user_id: 用户标识
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document_id: 文档标识
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session_id: 测验会话 ID
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answers_json: JSON 格式的答案字典，如 '{"q_001":"A","q_002":"True"}'
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输出：评分结果 JSON（score, total, accuracy, results）。
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"""
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from pathlib import Path
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import json
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import sys
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sys.path.append(str(Path(__file__).resolve().parents[1] / "src"))
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from quiz_mastery import QuizMasteryService
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def main() -> None:
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if len(sys.argv) < 5:
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print("Usage: submit_answers.py <user_id> <document_id> <session_id> <answers_json>")
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sys.exit(1)
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user_id = sys.argv[1]
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document_id = sys.argv[2]
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session_id = sys.argv[3]
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answers = json.loads(sys.argv[4])
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service = QuizMasteryService(
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base_dir=Path(__file__).resolve().parents[1] / "data"
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)
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result = service.submit_quiz_answers(
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user_id=user_id,
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document_id=document_id,
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session_id=session_id,
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answers=answers,
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)
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print(json.dumps(result, ensure_ascii=False, indent=2))
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if __name__ == "__main__":
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main()
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