•✅Prompter：用于构建 prompt（支持内置或自定义模板）•✅LLM 接口：支持 OpenAI、PaLM、Hugging Face 等多种模型•✅Pipeline：自动将文本输入 → prompt → LLM → 输出打通
无论是关键词提取、还是生成读书问题，Promptify都能轻松搞定。
使用 pip 安装：
pip3installpromptify
或安装最新 GitHub 版本：
pip3installgit+https://github.com/promptslab/Promptify.git
使用前需要准备 LLM 的 API Key（如 OpenAI、DeepSeek等）。
from promptify importPrompter,OpenAI,Pipelinesentence ="""The patient is a 93-year-old female with a medical history of chronic right hip pain, osteoporosis, hypertension, depression, and chronic atrial fibrillation admitted for evaluation and management of severe nausea and vomiting and urinary tract infection"""model =OpenAI("your_api_key_here")prompter =Prompter('ner.jinja')pipe =Pipeline(prompter, model)result = pipe.fit(sentence, domain="medical", labels=None)print(result)
输出结果如下：
[{"E":"93-year-old","T":"Age"},{"E":"chronicrighthippain","T":"MedicalCondition"},{"E":"osteoporosis","T":"MedicalCondition"},{"E":"hypertension","T":"MedicalCondition"},{"E":"depression","T":"MedicalCondition"},{"E":"chronicatrialfibrillation","T":"MedicalCondition"},{"E":"severenauseaandvomiting","T":"Symptom"},{"E":"urinarytractinfection","T":"MedicalCondition"},{"Branch":"InternalMedicine","Group":"Geriatrics"}]👉 整理干净的结构化输出，准确分类，还能归属到临床科室。节省大量人工标注时间。
from promptify importOpenAI,Promptersentence ="""The patient is a 93-year-old female with a medical history of chronic right hip pain, osteoporosis, hypertension, depression, and chronic atrial fibrillation admitted for evaluation and management of severe nausea and vomiting and urinary tract infection"""model =OpenAI("your_api_key_here")nlp_prompter =Prompter(model)result = nlp_prompter.fit('multilabel_classification.jinja', domain='medical', text_input=sentence)print(result)
输出如下：
[{'1':'Medicine','2':'Osteoporosis','3':'Hypertension','4':'Depression','5':'Atrialfibrillation','6':'Nauseaandvomiting','7':'Urinarytractinfection','branch':'Health','group':'Clinicalmedicine','mainclass':'Health'}]👉 非常适合做医疗知识图谱、自动病例标签系统等。
from promptify importOpenAI,Promptersentence ="""The rabbit-hole went straight on like a tunnel for some way, and then dipped suddenly down, so suddenly that Alice had not a moment to think about stopping herself before she found herself falling down a very deep well."""model =OpenAI("your_api_key_here")nlp_prompter =Prompter(model)result = nlp_prompter.fit('qa_gen.jinja', domain='story_writing', text_input=sentence)print(result)
输出结果：
[{'A':'Alicefoundherselffallingdownaverydeepwell.','Q':'WhathappenedwhenAlicewentdowntherabbit-hole?'},{'A':'Verydeep.','Q':'Howdeepwasthewell?'},{'A':'No,shedidnothaveamomenttothink.','Q':'DidAlicehavetimetothinkaboutstoppingherself?'},{'A':'Itwentstraightonlikeatunnel.','Q':'Whatdirectiondidtherabbit-holego?'},{'A':'No,shedidnotexpectit.','Q':'DidAliceexpecttofalldownawell?'}]👉 这些问答题可以直接拿去做英文阅读理解测验，省心又省力！
作为一个在资深程序员（已经写了十多年的代码），我对工具的要求是“节省脑细胞 + 效果可控”。
Promptify做到了这几点：
✅ 节省脑力：告别 prompt 拼命调试
✅ 模板灵活：支持自定义任务模版
✅ 多模型兼容：OpenAI、Hugging Face 都能跑
✅ 任务多样：从医学到创意写作都能驾驭
✅ 快速上手：几行代码就能跑结果
Promptify 是所有在 NLP 路上摸索的开发者的贴心助手。无论你做的是实体提取、文本分类、还是生成问题，它都能一站式搞定。
推荐给：
•数据科学家•医疗 NLP 工程师•教育/创作者•AI 初学者
小伙伴们，可以尝试下，按照你工作、生活中所使用的提示词，把这些都管理起来，作为自己的资源。
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