Text Similarity Analysis API

The Text Similarity Analysis API calculates the similarity score between two texts using advanced NLP machine learning models. By embedding texts into 768-dimensional vectors and computing cosine similarity, it provides precise and reliable similarity metrics.

About the API:  

The Text Similarity Analysis API offers a robust solution for measuring the similarity between two pieces of text. Leveraging state-of-the-art natural language processing (NLP) machine learning models, this API first transforms the texts into 768-dimensional vectors. These embeddings capture the semantic meaning of the texts, ensuring a high degree of accuracy. The API then calculates the cosine similarity between the two vectors, providing a precise similarity score. This process is essential for various applications, such as plagiarism detection, content recommendation, and clustering similar documents. With its advanced embedding techniques and reliable similarity computations, the Text Similarity Analysis API is a powerful tool for developers and businesses looking to enhance their text analysis capabilities. Whether you are working on improving search engine results, refining customer service chatbots, or developing intelligent content categorization systems, this API ensures high performance and accuracy.

 

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

Pass two text strings and receive the similarity percentage between them. 

 

What are the most common uses cases of this API?

  • Plagiarism Detection: Educational institutions and content publishers can use the API to compare student submissions or published articles with existing documents. By detecting high similarity scores, it can identify potential cases of plagiarism or duplicate content.

  • Content Recommendation: E-commerce platforms, streaming services, and news websites can enhance user experience by recommending similar products, movies, or articles. The API can analyze user preferences and suggest items with high similarity scores to their previous selections.

  • Customer Service Chatbots: Customer support systems can use the API to improve chatbot responses. By analyzing the similarity between customer queries and a database of FAQs or past interactions, the chatbot can provide more accurate and relevant responses.

  • Document Clustering and Organization: Organizations dealing with large volumes of documents, such as legal firms or research institutions, can use the API to cluster and categorize documents based on their content similarity. This aids in efficient document management and retrieval.

  • Search Engine Enhancement: Search engines can leverage the API to improve search results. By comparing the similarity between user queries and indexed pages, the API can rank and display the most relevant results, enhancing the overall search experience for users.

 

Are there any limitations to your plans?

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

API Documentation

Endpoints


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

 

text_1 (required) - first input text. Maximum 5000 characters.

text_2 (required) - second input text. Maximum 5000 characters.



                                                                            
POST https://zylalabs.com/api/4392/text+similarity+analysis+api/5399/check+similarity
                                                                            
                                                                        

Check Similarity - Endpoint Features

Object Description
Request Body [Required] Json
Test Endpoint

API EXAMPLE RESPONSE

       
                                                                                                        
                                                                                                                                                                                                                            {"similarity": 0.8126154541969299}
                                                                                                                                                                                                                    
                                                                                                    

Check Similarity - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/4392/text+similarity+analysis+api/5399/check+similarity' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"text_1": "This is an example sentence.",
 "text_2": "This is just another example sentence."}'

    

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 Analysis 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 accepts plain text inputs in UTF-8 format. Both short phrases and long paragraphs can be processed, allowing for flexible use in various applications.

The API uses advanced NLP models to embed texts into 768-dimensional vectors and then computes the cosine similarity between these vectors to produce a similarity score ranging from -1 to 1, where 1 indicates identical texts.

The API utilizes state-of-the-art transformer-based models, such as BERT or its variants, for embedding texts. These models are pre-trained on large corpora and fine-tuned for high accuracy in capturing semantic meaning.

The API is optimized for low latency, typically responding within a few hundred milliseconds for standard requests. Performance may vary depending on the length and complexity of the input texts.

Yes, the API supports multiple languages, provided the underlying embedding model has been trained on multilingual data. This allows for accurate similarity calculations across different languages.

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 Service Level
100%
 Response Time
821ms

Category:

NLP

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