Text Similarity Checker API

Text Similarity Checker API

Unleash the power of precision with our Text Similarity Checker API. Harnessing cutting-edge NLP and 768-dimensional vector embeddings, it effortlessly calculates the cosmic dance of similarity between texts. Elevate your applications with seamless integration, unlocking a realm where linguistic connections thrive and understanding blossoms

API description

About the API:  

Embark on a linguistic odyssey with our Text Similarity Checker API, a beacon of brilliance in the realm of natural language processing. Immerse your applications in the finesse of advanced machine learning as it deftly calculates the harmony between two textual pieces. This API transforms text into 768-dimensional vectors, a feat achieved by our groundbreaking Embeddings API, and then orchestrates a ballet of understanding through cosine similarity.

Picture a symphony where words waltz in the vastness of semantic space, harmonizing in a cosmic dance of similarity. Our state-of-the-art NLP models imbue your projects with unparalleled depth, capturing nuanced connections and resonances that escape ordinary methods. Seamlessly integrate this API to unlock a world where text transcends its literal boundaries, fostering a new era of contextual comprehension.

Whether you're revolutionizing search algorithms, refining recommendation systems, or ensuring plagiarism detection with finesse, the Text Similarity Checker API is your avant-garde ally. Join us in shaping a future where understanding is not just processed but celebrated—an intricately woven tapestry of linguistic brilliance illuminated by the guiding light of innovation.



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

Pass the two texts that you want to check its similarity. 

 

What are the most common uses cases of this API?

  1. Document Similarity Analysis:

    • Enhance content management systems by integrating the Text Similarity Checker API to analyze and quantify the similarity between documents. This use case is invaluable for sorting, categorizing, and identifying duplicate or closely related content within extensive databases.
  2. Plagiarism Detection:

    • Safeguard academic integrity and intellectual property by incorporating the Text Similarity Checker API into educational platforms. Automatically detect and flag instances of plagiarism, ensuring a fair and honest academic environment.
  3. Search Engine Optimization (SEO):

    • Optimize search algorithms for precision and relevance by utilizing the API to assess the similarity between search queries and indexed content. Elevate user experience by delivering more accurate and contextually aligned search results.
  4. Content Recommendations:

    • Revolutionize recommendation systems in various platforms, such as e-commerce or streaming services, by employing the Text Similarity Checker API. Understand the preferences of users and recommend content based on the similarity of their past interactions or preferences.
  5. Legal Document Analysis:

    • Streamline legal research and document analysis by integrating the API into legal software. Evaluate the similarity between legal documents, contracts, or case briefs to identify relevant precedents, saving time and improving the efficiency of legal professionals.

 

Are there any limitations to your plans?

Besides the number of API calls, there are no other limitations.

API Documentation

Endpoints


Returns a similarity score between 0 and 1 (1 is similar and 0 is dissimilar) of two given texts.



                                                                            
POST https://www.zylalabs.com/api/3010/text+similarity+checker+api/3175/get+similarity
                                                                            
                                                                        

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

API EXAMPLE RESPONSE

       
                                                                                                        
                                                                                                                                                                                                                            {"similarity": 0.7571364641189575}
                                                                                                                                                                                                                    
                                                                                                    

Get Similarity - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/3010/text+similarity+checker+api/3175/get+similarity' --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 Text Similarity Checker API REST API, simply include your bearer token in the Authorization header.

Headers

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


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The API leverages state-of-the-art NLP machine learning models to embed the input texts into 768-dimensional vectors using our Embeddings API. It then computes the cosine similarity between these vectors to determine their similarity score.

While the API is designed to excel in English, its capabilities can extend to other languages. However, optimal performance is achieved for English, and users exploring other languages should consider language-specific APIs for precision.

The API does have input size limitations. For extensive texts, consider preprocessing or breaking down the content into smaller, meaningful segments to ensure efficient and accurate similarity computations.

Absolutely. The API is designed to provide real-time similarity scores. Its efficiency makes it suitable for applications demanding instantaneous responses, such as chatbots, content recommendation systems, and live data analysis.

The API considers context through its embedding process, capturing semantic nuances. While it excels in handling polysemy, users should be aware that context plays a crucial role, and results may vary based on the complexity of the language.

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 Service Level
100%
 Response Time
424ms

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