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.
{"similarity": 0.8126154541969299}
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."}'
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 simply include your bearer token in the Authorization header.
| Header | Description |
|---|---|
Authorization
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Required
Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.
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Pass two text strings and receive the similarity percentage between them.
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.
Besides the number of API calls, there is no other limitation.
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.
The Check Similarity endpoint returns a JSON object containing a similarity score between two texts. The score ranges from 0 to 1, where 1 indicates the texts are identical and 0 indicates they are completely dissimilar.
The primary field in the response data is "similarity," which provides the computed similarity score between the two input texts. This score is a floating-point number.
Users can customize their requests by providing two text parameters, `text_1` and `text_2`, each with a maximum length of 5000 characters. This allows for flexibility in comparing various text lengths and types.
The response data is structured in JSON format, with a single key-value pair. The key is "similarity," and the value is the computed score, making it easy to parse and utilize in applications.
Typical use cases include plagiarism detection in educational settings, content recommendation for e-commerce, enhancing customer service chatbots, and improving search engine results by ranking relevant content based on similarity.
Data accuracy is maintained through the use of advanced NLP models that are pre-trained on extensive datasets. Continuous updates and fine-tuning ensure that the models remain effective in capturing semantic meaning.
If one or both input texts are empty, the API will likely return a similarity score of 0, indicating no similarity can be computed. Users should ensure both texts are provided for meaningful results.
The API accepts plain text inputs in UTF-8 format. This allows for a wide range of text types, including plain text documents, code snippets, and user-generated content.
To obtain your API key, first sign in to your account and navigate to the API you want to use. From the API's Pricing section, choose a plan and complete the subscription process. Once subscribed, return to the API page and you will see your API Access Key displayed at the top of the documentation page. You can use this key to authenticate your requests.
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