Semantic Search API refers to a type of search that uses Natural Language Processing (NLP) and Machine Learning algorithms to understand the meaning behind a user's query, rather than just searching for matching keywords. It will take into account the context, intent, and relationships between words to provide more relevant and accurate search results. In other words, it focuses on the user's intent rather than just the specific words used in the query.
You can use Semantic Search in numerous fields, here are some examples of common use cases:
These are just a few examples of Semantic Search API uses case, it can be applied in any field where there's a need to understand the meaning behind a user's query and provide more accurate and personalized results.
While comparing Semantic Search APIs, it is crucial to consider different aspects, among others, cost security and privacy. Semantic Search experts at Eden AI tested, compared, and used many Semantic Search APIs of the market. Here are some actors that perform well (in alphabetical order):
Cohere's Semantic Search API allows users to search through large datasets using natural language. The API is based on advanced Machine Learning models, offering high accuracy and fast response time. Cohere's API also allows for customization, enabling companies to tailor their search results to their specific needs.
Google Cloud's Semantic Search API utilizes advanced natural language processing and machine learning techniques to deliver more accurate and contextually relevant search results, enhancing the overall search experience for users.
NLP Cloud provides a Semantic Search API powered by advanced natural language processing models, ensuring precise and relevant search results even for complex queries. Moreover, the API is highly customizable, offering the flexibility to tailor the search results to specific industries or use cases. Users can also create their own semantic search engine by leveraging NLP Cloud's API and integrating their own business data to improve search accuracy and relevance.
Open AI’s Semantic Search API uses advanced language models and Machine Learning techniques to understand the meaning behind user queries and to match them with relevant results. Thanks to its extensive training on vast amounts of diverse data, the API delivers highly accurate and comprehensive search results, even for complex queries. Additionally, OpenAI's solution is customizable, enabling users to fine-tune their search results to specific domains or topics.
Sapling.ai's API leverages sophisticated NLP methods to facilitate natural language-based information retrieval. This technology boasts high precision and quick response times, enabling it to handle intricate queries. Furthermore, Sapling.ai accommodates an extensive array of data sources, including structured and unstructured data. User experience is further elevated through features such as synonym recognition and auto-complete suggestions.
For all companies who use Semantic Search APIs in their software: cost and performance are real concerns. The Semantic Search market is quite dense and all those providers have their benefits and weaknesses.
Performances of Semantic Search vary according to the specificity of data used by each AI engine for their model training. This means that some Semantic Search may perform great for some languages but won’t necessarily for others.
Semantic Search APIs perform differently depending on the language of the text. In fact, some providers are specialized in specific languages. Different specificities exist:
Some Semantic Search APIs trained their engine with specific data. This means that some Semantic Search APIs will perform better for educational articles, while others will perform better on technical documents.
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 Semantic Search 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, Question Answering, Data Anonymization, Speech Recognition, Face Detection and so forth.
We want our users to have access to multiple Semantic Search 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 Semantic Search integration project. This can be done by :