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• In clinical activities such as ward rounds and consultations, medical staff need to frequently query patients' test results and progress.
• Searching for information from electronic medical records involves reasoning processes such as time conditions, logical conditions, and terminology standardization. Simple structured queries are difficult to meet the requirements.
• Large language models have changed the task logic of natural language processing and can explore application opportunities under new ideas.

• Establish a workflow framework for electronic medical records and large language model inference.
• Implement an interactive question-and-answer interface for electronic medical record information.
• Explore the best practices of using large model inference in different scenarios and domains.

• Load Data:
  load_data.py:
  - digest_doc: Extract essential information such as ID and timestamps from plain text based on the minimal information model.
  - load_std_data: Load data in standard fields.
• Retrieve Medical Records:
  query_record.py: Find medical records that meet the requirements.
• Answer Questions:
  answer_question.py: Answer query questions based on medical record entries.
• Example:
  Seeexample.py