有谁知道这三者之间的具体区别和功能,或者是否有人具有更多的功能/更灵活地用作开发人员?
有谁知道这三者之间的具体区别和功能,或者是否有人具有更多的功能/更灵活地用作开发人员?
Answers:
vs
vs
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║ wit.ai vs api.ai(Dialogflow) vs luis.ai ║
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║ S.No ║ Wit.ai ║ Api.ai(Dialogflow) ║ Luis.ai ║
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║ 1 ║ Wit.ai API is completely free ║ Api.ai Has a paid enterprise option ║ LUIS is in beta and free to use ║
║ ║ with no limitations on ║ which allows for this to be run on a ║ 10K transactions per month ║
║ ║ request rates. ║ private cloud internally and more ║ and up to 5 requests per second ║
║ ║ ║ from their services team., After google ║ for each account. ║
║ ║ ║ acquisition they are providing free ║ ║
║ ║ ║ services by integrating google cloud ║ ║
║ ║ ║ services. ║ ║
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║ 2 ║ Provides a nice combination ║ Speech to Text and Text to Speech ║ LUIS uses machine learning ║
║ ║ of both voice recognition and ║ capabilities, along with machine ║ based methods to analyze ║
║ ║ machine learning for developers. ║ learning. ║ sentences. To perform machine ║
║ ║ ║ ║ learning, LUIS breaks an ║
║ ║ ║ ║ utterance into "tokens". ║
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║ 3 ║ Has two main elements to it ║ Support of Intents, Entities, actions ║ Supports Intents, Entities ║
║ ║ that you set up within your ║ and one key focus area is its “Domains”. ║ and actions. ║
║ ║ app – intents and entities. ║ ║ ║
║ ║ Actions are separated to ║ ║ ║
║ ║ use as a combined operations. ║ ║ ║
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║ 4 ║ Has pre-build entities like ║ Has pre-build entities like @sys.date, ║ Has pre-build entities ║
║ ║ temperature, number, URLs, ║ @sys.color, @sys.unit-currency… etc. ║ builtin.intent.alarm, ║
║ ║ emails, duration… etc. ║ ║ builtin.intent.calendar, ║
║ ║ ║ ║ builtin.intent.email… etc. ║
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║ 5 ║ Doesn’t have integration module ║ Has integration module to connect ║ Has integration to Microsoft ║
║ ║ to directly communicating with ║ directly to Facebook messenger and ║ Azure and other services, can be ║
║ ║ Facebook messenger or other ║ other messenger api’s. Has support for ║ deployable in any supported ║
║ ║ messenger APIs. but has web ║ deploying in to heroku server, enterprise ║ servers. ║
║ ║ service api to hook services. ║ paid environment. ║ ║
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║ 6 ║ Early in 2015, joined Facebook ║ Created by a team who built personal ║ LUIS was introduced together with ║
║ ║ and opened up the entire platform ║ assistant app for major mobile platforms ║ Microsoft Bot Framework and Skype ║
║ ║ to be free for both public and ║ with speech and text enabled conversations. ║ Developer Platform which can be ║
║ ║ private instances. ║ acquired by google (sept 2016). ║ used to create Skype Bots. ║
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║ 7 ║ Wit.ai API for developers of iOS, ║ Api.ai has SDKs for Android, iOS, ║ LUIS allow building applications ║
║ ║ Android, Node.js, Raspberry Pi, ║ the Apple Watch, Node.js, Cordova, ║ by using the LUIS web interface. ║
║ ║ Ruby, Python, C, Rust and ║ Unity, C#, Xamarin, Windows Phone, ║ No coding needed other than the ║
║ ║ Windows Phone. It even ║ Python and JavaScript. It also can be ║ ability to interpret and use the ║
║ ║ has a JavaScript plugin for ║ integrated with Amazon’s Echo and ║ returned JSON in application. ║
║ ║ front end developers. ║ Microsoft’s Cortana. ║ It is also possible to use the ║
║ ║ ║ ║ LUIS REST API for ║
║ ║ ║ ║ automation of applications. ║
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更新: API.AI现在是Dialogflow。在这里了解更多。
该博客文章对Luis,Wit.ai,Api.ai,Amazon Alexa和IBM Watson服务进行了很好的分析和比较。它还具有很好的背景知识,说明您为什么首先要构建对话式机器人以及随之而来的一些挑战。它是由YumiBot(为您提供应用开发价格的机器人)背后的人编写的。
一般要点是,如果您正在尝试并且只想免费获得一些东西,Wit.ai和Luis是不错的选择。Api.ai具有出色的服务和用户体验,但并非免费。与IBM Watson相同,后者为企业工作定价更高。Alexa的API很棒,但只能与Alexa一起使用(但考虑到他们拥有庞大的用户群,这不是一件坏事)。
他们的建议是不要过多地依赖一个提供者:
我们建议您以结构化的方式将模型所需的所有数据存储在自己的代码存储库中。因此,以后您可以从头开始重新训练模型,或者在需要时甚至更改语言理解提供程序。您只是不想在公司关闭其服务而您完全没有准备的情况下遇到这种情况。你还记得解析吗?
我希望这能有所帮助!我认为做出选择的最好方法是尝试一下这些服务。鉴于其中许多仍在开发中,并且正在添加功能/更改定价模型,因此,您应该尝试以特定的用例来研究它们,并查看哪个可以使您最快地到达您的需求。
我们最近发布了对七个启用NLU API的服务的评估研究:API.ai,Amazon Lex,Microsoft LUIS,IBM Watson Conversation,wit.ai,Recast.ai和Snips.ai。
我们的发现的简要总结:
这个问题的一方面是这些工具在理解自然语言方面的效率如何。在我们刚刚发布的基准测试中(法国AI公司Snips),我们测试了Alexa(Amazon),SiriKit(Apple),Luis(Microsoft)和API.ai(Google)的内置自然语言引擎。
我们测试了他们对自然查询的理解能力,例如“为我找到可以参加午餐会的色拉吧”,“为6人订购出租车”以及326个其他查询。
总体结论是,所有解决方案都不完美。
更准确地说,它们的响应都具有相似的噪声水平(精度在60%到90%之间),但是它们所支持的语言广度却有很大差异。从这个角度来看,Luis的表现最差:在我们测试的每个用例中,它只了解不到14%的查询。API.ai的性能较好,尽管不是很可靠:它了解我们测试的查询的0%到80%,具体取决于用例。对于Alexa(42%和82%的召回率)和Siri(61%的召回率),召回率最高。
更多详细信息以及这些结果背后的原始数据可以在我们的博客文章“对自然语言理解系统进行基准测试”中找到
我将回答有关灵活性和作为开发人员的问题的最后一部分,IMO,这最终取决于您在这些平台上的期望。
如果您是使用NodeJS或.Net的开发人员,那么LUIS.ai拥有广泛的库和定义明确的代码段以及可以快速启动像样的bot的示例。与Google相比,其意图和实体识别略低于标准水平,但如果您是Microsoft Shop,则与O365,Teams,Skype,cortana等有很多一键式集成。LUIS.ai的缺点在于他们的服务非常不稳定,截止至撰写本文时,他们的LUIS.ai网站无法拒绝连接,并且已经有一个多星期了,Cortana集成无法正常工作。因此,该平台仍在开发中。
从纯粹的NLU角度来看,Api.ai优于Luis.ai,其后续意图非常容易设置,语音启动在很大程度上优于Luis.ai(即使在启动语音之后)。我要说的缺点是,建立机器人的可连接性和API比建立基于MSBot的聊天机器人要复杂得多。
另一个吸引人的平台开源平台是RASA NLU。https://rasa.com/。相对而言,大型数据集的实体识别和排名仍然有些粗略,但是它的开放源代码,如果您想动手,可以使用他们的github平台并对其进行改进。
从纯粹的开发角度来看,在MS平台上使用chatisbot(使用luis.ai或qnamaker.ai)更加容易,但是在稳定平台时要做好准备面对挑战。
-卡尔蒂克
我使用的是DialogFlow,但切换到LUIS。为什么?因为当您在DialogFlow中调用DetectIntent时,您会得到一个带有所选意图及其置信度的JSON,但是我需要获取一个意图列表,每个意图的置信度。wit.ai和api.ai也会发生相同的情况。
另一方面,LUIS为您提供了意图列表作为响应。这样,我就可以对自己进行进一步处理。
这是LUIS的一个示例,当您搜索“飞往开罗的机票”(LUIS示例的一部分)时:
{
"query": "Book me a flight to Cairo",
"topScoringIntent": {
"intent": "BookFlight",
"score": 0.9887482
},
"intents": [
{
"intent": "BookFlight",
"score": 0.9887482
},
{
"intent": "None",
"score": 0.04272597
},
{
"intent": "LocationFinder",
"score": 0.0125702191
},
{
"intent": "Reminder",
"score": 0.00375502417
},
{
"intent": "FoodOrder",
"score": 3.765154E-07
},
],
"entities": [
{
"entity": "cairo",
"type": "Location",
"startIndex": 20,
"endIndex": 24,
"score": 0.956781447
}
]
}
另一方面,用于DialogFlow的UI比通过LUIS获得的功能强大得多。