Semantic Similarity API

Semantic Similarity API

Semantic Similarity API assesses and quantifies likeness between text, leveraging advanced NLP for accurate semantic understanding.

About the API:  

The Semantic Similarity API is a robust and versatile tool designed to evaluate and quantify the similarity between two or more fragments of text. Its main objective is to analyze the textual content, providing valuable information on the degree of overlap, similarity or equivalence in the information transmitted.

Basically, the Semantic Similarity API is designed to meet the growing demand for automated text analysis and comparison in various industries. Whether applied in educational environments for plagiarism detection, content management systems for detecting duplicate content, or information retrieval systems for improving search relevance, this API emerges as an advanced solution for discerning textual similarities.

A key strength of the Semantic Similarity API lies in its ability to understand the contextual and semantic complexities of language. Traditional text matching methods often rely on simplistic metrics such as word overlap or string matching, which risks inaccuracies, especially when faced with nuanced language use. In contrast, the Semantic Similarity API uses advanced NLP-based algorithms and models to capture the meaning of words, phrases, and sentences, ensuring more accurate and context-aware similarity assessment.

As the volume of digital information continues to emerge, the Semantic Similarity API takes on a critical role in automating otherwise challenging and time-consuming tasks. By providing an efficient means of measuring textual similarity, the API enables companies and institutions to refine decision-making processes, improve content management practices, and optimize operations. Its integration across multiple domains underscores its importance as a fundamental tool for modern text analysis, reflecting the continued evolution of natural language processing technologies to meet the demands of a text-rich digital landscape.


What this API receives and what your API provides (input/output)?

It will receive parameters and provide you with a JSON.


What are the most common uses cases of this API?

  1. Plagiarism Detection: Detect and prevent plagiarism by comparing submitted content against existing databases for similarities.

  2. Content Deduplication: Identify and remove redundant information within databases or content management systems.

  3. Document Comparison: Compare legal documents, contracts, or policies to highlight similarities or differences.

  4. E-Learning Assessments: Evaluate student submissions for originality in educational settings.

  5. Search Engine Optimization (SEO): Improve search relevance by identifying and addressing duplicate content on websites.

Are there any limitations to your plans?

Besides the number of API calls, there is no other limitation.

API Documentation


To use this endpoint you must indicate text in the parameters.


Text Similarity - Endpoint Features
Object Description
Request Body [Required] Json
Test Endpoint


                                                                                                                                                                                                                            {"similarity": 0.7571364641189575}

Text Similarity - CODE SNIPPETS

curl --location --request POST '' --header 'Authorization: Bearer YOUR_API_KEY' 


API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Semantic Similarity API REST API, simply include your bearer token in the Authorization header.


Header Description
Authorization [Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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To use this API, users must indicate 2 texts to obtain a text similarity comparison.

Semantic Similarity API is a powerful tool designed to evaluate and quantify similarity between fragments of text, emphasizing a nuanced understanding of language.

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