Question Answering APIs use Machine Learning and Natural Language Processing (NLP) techniques to analyze the text of a question and provide a relevant and accurate answer.
This API is designed to understand the context and meaning of the question being asked and identify the most relevant information from a large corpus of data, such as a database or knowledge base. This allows users to quickly and easily find answers to their questions without the need for manual searching or browsing through large amounts of information.
You can use Question Answering APIs in numerous fields, here are some examples of common use cases:
These are just a few examples of Question Answering API uses case, it can be applied in many different fields to improve efficiency, accuracy, and customer satisfaction by providing quick and accurate answers to common questions while releasing human resources for more complex tasks.
While comparing Question Answering APIs, it is crucial to consider different aspects, among others, cost security and privacy. Question Answering experts at Eden AI tested, compared, and used many Question Answering APIs of the market. Here are some actors that perform well (in alphabetical order):
Hugging Face provides a state-of-the-art Question Answering API that uses advanced NLP models to answer questions in a conversational manner. With high accuracy and the ability to handle complex questions, the API is customizable and can be fine-tuned for specific use cases or industries.
Microsoft Azure's Question Answering API uses Machine Learning to provide contextually relevant answers. Their technology also supports multiple languages with high accuracy and fast response time. In addition, the API can be customized for specific industries.
NLP Cloud provides a powerful and flexible Question Answering solution based on the latest natural language processing models. The API offers a fast response time for the pre-trained Roberta model and impressive accuracy for the GPT-J model. Additionally, users have the option to either leverage the pre-existing models or create and upload their own custom models
OpenAI uses state-of-the-art Deep Learning models for their Question Answering API. It is trained on a vast amount of data and continually updated to ensure that it can provide accurate answers to a wide range of questions. In addition, OpenAI's technology can handle complex questions that require a certain understanding of context and relationships between concepts. The API is also designed to be scalable for handling large volumes of queries in real-time.
Tenstorrent's AI Q&A API uses machine learning to provide contextually relevant answers. Their technology is capable of supporting a variety of languages with high accuracy and rapid response time. Furthermore, the system can be tailored to adapt to different sectors.
For all companies who use Question Answering API in their software: cost and performance are real concerns. The Question Answering market is quite dense and all those providers have their benefits and weaknesses.
Performances of Question Answering vary according to the specificity of data used by each AI engine for their model training. This means that some APIs may perform well at providing information while others could generate human-like responses.
Some Question Answering APIs trained their engine with specific data. Some APIs may perform well for medical questions while others may perform better for financial questions.
Question Answering APIs perform differently depending on the language of the text. In fact, some providers are specialized in specific languages. Different specificities exist:
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate Question Answering tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform amongst several technologies: Text-to-Speech, Language Detection, Sentiment analysis API, Summarization, Face Detection, Data Anonymization, Speech recognition, and so forth.
We want our users to have access to multiple Question Answering engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple APIs :
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies. Eden AI allows you to call multiple AI APIs.
You can see Eden AI documentation here.
The Eden AI team can help you with your Question Answering integration project. This can be done by :