GPT-4o AI Agents: Easily Create Medical Research Agents (Praison AI)
Summary
TLDR在这个视频中,我们探索了如何利用低代码解决方案和人工智能(AI)创建医疗研究AI代理。首先,介绍了三个主要的AI代理:研究代理、写作代理和编辑代理,它们分别负责研究疾病、撰写文章和最终定稿。视频强调了如果研究代理无法访问PubMed或互联网资源,其输出质量将大打折扣。为了解决这个问题,我们介绍了如何通过Longchain工具为研究代理提供PubMed访问权限,从而提升研究质量。随后,视频详细演示了如何通过安装必要的包、导出API密钥、初始化代理、并利用Longchain工具集成PubMed,来创建和运行这些AI代理。此外,还展示了如何使用Longchain的Tav搜索API wrapper进行互联网搜索,以进一步提升AI代理的研究能力。整个流程不仅简化了手动研究的繁琐工作,还提高了研究的效率和质量。视频最后鼓励观众订阅YouTube频道,以便获取更多关于人工智能的更新。
Takeaways
- 🚀 利用GPT-4创建医疗研究AI代理,通过低代码解决方案集成Prais和AI。
- 🔍 第一个AI代理负责研究疾病,随后的代理负责写作和编辑,以完成整个研究过程。
- 📚 如果研究代理无法访问PubMed或互联网,输出的质量将大大降低。
- 🛠️ 通过提供工具如L链工具,研究代理可以利用这些工具进行研究,从而获得更好的输出。
- 🧰 通过集成Prais和AI与L链,可以简化工具的集成过程,提高研究效率。
- 📝 通过自动化,AI代理可以搜索PubMed数据库,找到最新的研究文章,并生成最终报告。
- 📈 使用L链工具,可以为研究代理提供高级访问权限,从而提高研究的质量和速度。
- 💻 通过命令行安装必要的包,如prais-ai、longchain-community-xml等,以支持研究代理的运行。
- 🔗 需要导出OpenAI模型名称和API密钥,以便研究代理能够使用所需的工具。
- 📑 创建的AI代理包括研究分析师、医疗作家和编辑代理,每个代理都有其特定的任务。
- 🔬 通过集成Pubmed和L链工具,研究代理可以访问Pubmed数据库,搜索相关文章。
- 🌐 除了Pubmed,还可以使用如Tav搜索引擎的API wrapper,为AI代理提供互联网搜索能力。
Q & A
什么是低代码解决方案?
-低代码解决方案是一种软件开发方法,它允许开发者通过图形界面和模型驱动的逻辑来创建应用程序,而不需要编写大量的代码。这种方法简化了开发流程,缩短了开发时间,并允许非专业开发者参与到应用开发中来。
如何使用GPT-4创建医疗研究AI代理?
-使用GPT-4创建医疗研究AI代理涉及到集成不同的AI模型来执行特定的任务,如研究、写作和编辑。首先,需要定义研究任务,然后利用GPT-4模型自动生成相应的代理,如研究分析师代理、医疗作家代理和编辑代理。
为什么研究代理需要访问PubMed?
-研究代理需要访问PubMed,因为PubMed是一个包含大量生物医学文献的数据库,可以为研究代理提供关于疾病原因和治疗方法的最新和最相关的信息。没有这样的访问权限,研究代理的输出质量将大大降低。
如何将L链工具集成到Prais和AI中?
-要将L链工具集成到Prais和AI中,首先需要安装必要的依赖,如XML到dict。然后,通过在Prais和AI的代理定义文件中添加工具代码,将L链工具的功能集成到代理中。这样,代理就可以使用这些工具来执行它们的任务。
如何为研究代理分配Pubmed L链工具?
-为研究代理分配Pubmed L链工具,需要在代理的代码中引入Pubmed查询工具的函数,并将其粘贴到代理的工具部分。这样,研究代理就可以通过L链工具访问Pubmed,执行相关的研究任务。
运行AI代理并获取最终输出的步骤是什么?
-运行AI代理并获取最终输出的步骤包括:1) 初始化Prais和AI;2) 定义研究任务并创建代理;3) 为代理添加所需的工具;4) 在终端运行Prais和AI命令来启动代理;5) 代理将依次执行研究、写作和编辑任务;6) 最终输出将是一个经过研究、撰写和编辑的详细报告。
如何使用L链工具进行更高级的研究?
-使用L链工具进行更高级的研究可以通过集成不同的L链工具来实现,如使用Pubmed工具进行医学文献搜索,或者使用搜索引擎API(如Google Search API或Bing Search API)进行网络搜索。这些工具可以通过L链的wrapper简化集成过程,使得AI代理能够访问和分析大量的数据。
为什么需要对来自utilities的工具进行包装(wrapper)?
-来自utilities的工具需要进行包装(wrapper),因为这些工具可能需要特定的初始化过程或者与L链工具不同的交互方式。通过创建一个wrapper,可以简化工具的使用,使其更容易地集成到Prais和AI中,并保持一致的接口。
如何使用Tav搜索引擎API进行AI代理的网络搜索?
-要使用Tav搜索引擎API进行AI代理的网络搜索,首先需要安装Tav Python库和Tav搜索API的wrapper。然后在工具文件中导入基础工具和Tav搜索API的wrapper,创建一个名为T工具的类,并定义一个方法来根据查询搜索相关信息。最后,将这个工具分配给AI代理,并提供Tav API的密钥。
为什么使用AI代理进行研究可以节省时间?
-使用AI代理进行研究可以节省时间,因为AI代理可以自动执行搜索、阅读和总结大量文献的任务。这避免了手动浏览和分析每一篇研究文章的耗时过程,使得研究工作更加高效。
如何获取Tav API的密钥?
-要获取Tav API的密钥,需要访问Tav的官方网站,并按照提供的指示进行注册和申请。一旦获得API密钥,就可以在AI代理中使用它来访问Tav搜索引擎的功能。
Outlines
🚀 利用GPT 4创建医疗研究AI代理
本段介绍了如何使用GPT 4和低代码解决方案来创建医疗研究AI代理。首先,介绍了AI代理的三个角色:研究代理、写作代理和编辑代理,它们协同工作以研究疾病、撰写相关文章并最终完成文章。接着,讨论了如果没有互联网研究资源,如PubMed的访问权限,研究代理的输出质量将大打折扣。为了解决这个问题,提出了使用Longchain工具来提供PubMed访问权限,以提高研究质量。最后,介绍了如何通过集成Prais和AI与Longchain,使用户能够更容易地创建和管理这些AI代理。
🔍 集成Longchain Wrapper进行高级研究
在本段中,继续讨论了Longchain工具的集成,特别是提到了'rapper'这一概念。介绍了如何使用工具和实用程序中的'wrapper'来实现更高级的功能。以Tav搜索引擎为例,说明了如何安装和使用Tav搜索API wrapper,以及如何创建一个名为'T tool'的类来搜索相关信息。此外,还展示了如何将Tav搜索集成到Prais和AI工具中,以及如何使用API密钥进行搜索。最后,通过展示如何使用Tav互联网搜索来简化研究过程,说明了如何将这些工具用于AI代理,以提高研究效率。
Mindmap
Keywords
💡医疗研究AI代理
💡GPT-4
💡低代码解决方案
💡PubMed
💡L链工具
💡API密钥
💡自然语言处理(NLP)
💡互联网搜索工具
💡编辑代理
💡工具集成
💡自动化研究
Highlights
使用GPT 4创建医疗研究AI代理,并通过低代码解决方案进行集成。
AI代理将执行研究、撰写和编辑工作,以生成关于疾病的详细报告。
如果研究代理无法访问PubMed或其他互联网资源,输出质量将受到限制。
通过提供PubMed访问权限,研究代理可以使用工具进行研究,从而获得更好的输出。
Longchain工具允许集成多种工具,以增强Prais和AI的功能。
演示了如何一步步创建医疗研究AI代理,并为其分配PubMed L链工具。
通过自动化过程,可以节省手动搜索、总结和准备最终报告的大量时间。
介绍了如何在YouTube频道上定期创建关于人工智能的视频,并邀请订阅。
展示了如何安装必要的软件包,如prais AI longchain和XML到dict。
说明了如何使用GPT 40模型,并初始化AI代理。
展示了如何定义研究任务,并自动创建相应的代理文件。
介绍了如何将L链PubMed工具集成到研究分析师代理中。
演示了如何运行代码并在终端中启动AI代理。
研究分析师代理使用PubMed工具搜索有关肺部疾病原因的相关文章。
医疗作家代理基于研究信息撰写详细文章,编辑代理负责最终的文章格式化。
展示了如何集成L链包装器,如Tav搜索API包装器,以提供互联网搜索访问。
说明了如何使用Tav搜索API包装器来搜索有关肺部疾病的信息。
演示了如何通过自动化简化研究过程,并为发布重新格式化研究答案。
表达了对创建更多类似视频的兴奋,并鼓励观众订阅、点赞和分享。
Transcripts
this is amazing now we are going to see
about medical research AI agents
creation using GPT 4 we are going to
integrate all these things in a low code
solution prais and AI imagine you have a
medical research AI agents so the first
agent is going to research about a
sickness then the writer agent is going
to write about that then finally the
editor agent is going to finalize
everything but imagine if the research
agent doesn't have access to pubit or
any internet research access the quality
of the output is going to be minimal
that's when we have tools if we provide
pubit access to the research agent the
research agent is going to use that tool
to do its research and finally get a
better output so how can we do this in a
more easy format that's when we
integrate prais and AI with L chain if
you go and see longchain tools there are
multiple tools for various different
toss you can integrate any of these
tools with praise and AI so how can you
do this that's exactly what we're going
to see today let's get
[Music]
started hi everyone I'm really excited
to show you about prais and AI
integration with the L chain in this we
going to create AI agents and give
Advanced access using tools so first we
are going to create medical research
agents then assign Pub mid L chain tool
to those agent and then finally run
those agents to get a final output
so these research agents going to search
the PubMed reper for the latest research
based on the provided topic and provide
that to those agents to run if you do it
manually this might take a lot of time
to go through every individual research
articles summarize that and prepare a
final report but this will make things
easier I'm going to take you through
step by step but before that I regularly
create videos in regards to Artificial
Intelligence on my YouTube channel so
subscribe and click the Bell icon to
stay tuned make sure you click the like
button so this video can be helpful for
many others like here so first step pip
install prais AI longchain Community XML
to dict the XML to dict is the package
used by pubit and then click enter first
export your open a API key like this and
then click enter next we are going to
use GPT 40 model so type export open AI
model name GPT 40 and then click enter
now we need to create agents to do that
type Pras AI hyphen hyphen init now we
can Define the task I want to research
about the courses of lung disease that's
it and click enter this will
automatically create your agents. yl
file and let's open this if you see that
it created these agents such as research
analyst agent medical writer agent and
editor agent each time you generate this
will vary so you can even customize this
based on your requirement so only VAR
creation which we are going to do is add
tools so if we go to the L chain pubit
tool here are the requirements it
requires XML to dict that's what we
installed initially then we need to use
this so if you see that it's coming from
tools pubit tool so anything with tools
we can use it directly just copy this so
here's our folder I'm going to create a
new file called tools. pii and then
click enter now inside the file I'm
going to paste the import which we just
copied that's it there's nothing else
more to do just copy this function pubit
query run and go to agents. Yo and paste
it in the tools section now we are ready
that's all it takes to integrate Pub mid
to the research analyst agent now we
have successfully created research
agents and then assigned the pubid L
chain tool to the agent now this
research agent will have access to pubit
using L chain tools now I'm going to run
this code to start running this in your
terminal type pris Ai and then click
enter that's it now first it going to
the research analyst agent the research
analyst agent is using the pubit tool
and it's searching for Relevant articles
about the reason for the lung disease as
you can see here lung searching for the
keyword lung and then retrieving all the
relevant articles which are most recent
after that using that information it's
passing on to the medical writer agent
and the writer agent writes a detailed
article document titled causes and risk
factors of the lung disease introduction
environment factors molecular mechanism
genetic factors infections occupation
hazards and much more then that is
passed to the editor agent to finalize
the article and we got everything
clearly written now we can directly use
this in this way you are able to
integrate any of the L chain tool with
prais
and do much more advanced research one
more thing I want to show you in Lang
chain there is something called rapper
so this is from utilities so if it is
from tools then you can use directly but
if it's from utilities with the wrapper
then you might need to slightly modify
the function so you got Google serer API
wrapper search API wrapper Sur API
wrapper similarly there are many tools
which also contain wrappers so how can
you use that here so in our case we are
going to use Tav search it is a search
engine specifically built for AI agents
so I'm going to use this so you need to
install like this and also we are going
to use tly search wrapper so in your
terminal install as instructed with Tav
python then in the tools file we are
going to import base tool from prais AI
tools and then tly search API wrapper
that is a class which we're going to use
then we are creating a class called T
tool naming it as T tool search relevant
information based on the query and this
is how you call it and then pass the
query like this that's it similarly for
Bing search wrapper you can see from the
documentation you can initialize that
first and then use search. results and
pause the query and the number of
results similarly for each rapper there
is a documentation which you need to
follow so for T there's a query and the
maximum number of results that's it now
we have integrated blank chain wrapper
in prais and AI tools now I'm copying
the name of the tool and replacing that
here with t tool now instead of pubit
access I'm giving internet search access
to those agents now I'm exporting T API
key like this and then click enter this
API key you can generate from t.com now
we're going to type praise and then
click enter that's it now you can see
it's using T internet search and it's
browsing web pages and getting
information about lung diseases so all
this data if we go and consume manually
it's really time consuming but this
simplifies the process and finally give
you the research answer same as before
it's going to the medical writer it's
writing in more detail next it goes to
the editor and the editor reformats it
for publishing as simple as that I'm
really excited about this I'm going to
create more videos similar to this so
stay tuned I hope you like this video do
like share and subscribe and thanks for
watching
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