pip install langchain openai python-dotenv

medical-report-generator/
├── .env            # 存储API密钥等敏感信息
├── main.py         # 主程序文件
└── requirements.txt # 依赖文件

OPENAI_API_KEY=your-api-key-here

import os
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain

# 加载环境变量
load_dotenv()

# 初始化模型 - 使用gpt-3.5-turbo以降低成本，实际应用中可考虑gpt-4提高质量
llm = ChatOpenAI(
    model_name="gpt-3.5-turbo",
    temperature=0.7,  # 控制创造性，医疗报告需要准确性，不宜过高
    openai_api_key=os.getenv("OPENAI_API_KEY")
)

# 定义报告生成模板 - 结构化提示词对结果质量至关重要
report_template = """
你是一位专业的{specialty}医生。请根据以下患者信息生成一份详细的医疗报告：

患者基本信息:
- 姓名: {name}
- 年龄: {age}
- 性别: {gender}
- 主诉: {complaint}

检查结果:
{exam_results}

请按照以下结构生成报告:
1. **临床诊断**: 
2. **诊断依据**: 
3. **治疗建议**: 
4. **注意事项**: 

使用专业但易懂的语言，避免过于晦涩的医学术语。
"""

prompt = ChatPromptTemplate.from_template(report_template)

# 创建处理链
report_chain = LLMChain(llm=llm, prompt=prompt)

def generate_medical_report(specialty, name, age, gender, complaint, exam_results):
    """生成医疗报告的封装函数"""
    return report_chain.run({
        "specialty": specialty,
        "name": name,
        "age": age,
        "gender": gender,
        "complaint": complaint,
        "exam_results": exam_results
    })

if __name__ == "__main__":
    # 示例使用 - 心血管科病例
    specialty = "心血管内科"
    patient_info = {
        "name": "张伟",
        "age": "58岁",
        "gender": "男",
        "complaint": "反复胸痛3个月，加重1周",
        "exam_results": """心电图显示ST段压低；超声心动图示左心室肥厚；
                        血脂检查：总胆固醇6.8mmol/L，LDL-C4.5mmol/L"""
    }

    print("正在生成医疗报告...\n")
    report = generate_medical_report(specialty, **patient_info)
    print(report)

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def safe_generate_report(params):
    try:
        return report_chain.run(params)
    except Exception as e:
        print(f"生成失败: {str(e)}")
        raise

# usage: report = safe_generate_report({...})

from langchain.cache import InMemoryCache
from langchain.globals import set_llm_cache

set_llm_cache(InMemoryCache())

from langchain.chains import SequentialChain

# Step1:初步诊断链 
diagnosis_template = """根据以下症状给出可能的诊断:{symptoms}"""
diagnosis_prompt = ChatPromptTemplate.from_template(diagnosis_template)
diagnosis_chain = LLMChain(llm=llm, prompt=diagnosis_prompt, output_key="diagnosis")

# Step2:详细报告链(使用上一步输出)
full_chain = SequentialChain(
    chains=[diagnosis_chain, report_chain],
    input_variables=["specialty", "name", "age", ...],
    output_variables=["text"]
)

pip install reportlab

from reportlab.lib.pagesizes import letter  
from reportlab.pdfgen import canvas  

def save_as_pdf(text, filename):
    c = canvas.Canvas(filename, pagesize=letter)
    width, height = letter

    # PDF样式设置(字号、行距等)
    textobject = c.beginText(40, height-40)
    textobject.setFont("Helvetica",12)

    for line in text.split('\n'):
        textobject.textLine(line.strip())

    c.drawText(textobject)
    c.save()

# usage: save_as_pdf(report_text,"medical_report.pdf")

正在生成医疗报告...

1.**临床诊断**: 
冠状动脉粥样硬化性心脏病 (冠心病) 
稳定性心绞痛 

2.**诊断依据**: 
-58岁男性患者，具有典型的心绞痛症状(反复胸痛3个月)  
-心电图显示ST段压低提示心肌缺血  
-超声心动图显示左心室肥厚  
-血脂异常(总胆固醇和LDL-C明显升高)

3.**治疗建议**: 
...
[完整输出省略]