Text to Vector API

The Text to Vector API transforms text into vectors using cutting-edge NLP machine learning models. It fuels semantic search, text comparison tools, recommendation engines, and more. Explore its potential alongside our Text Similarity API for enhanced text analysis and application across diverse domains.

About the API:  

 

The Text to Vector API employs advanced NLP machine learning models to encode text into vectors, facilitating semantic search, text comparison, and recommendation systems. Seamlessly integrating with our Text Similarity API, it enhances text analysis across various applications. Whether powering intelligent search engines, refining content recommendations, or enabling precise text comparisons, this API offers unparalleled accuracy and efficiency. Developers can leverage its capabilities to unlock insights from textual data, drive personalized user experiences, and streamline information retrieval processes. With its robust feature set and versatility, the Text to Vector API is a cornerstone for applications demanding sophisticated text processing and analysis

 

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

Returns a 768-dimensional vector as an array that encodes the meaning of any given input text.

 

What are the most common uses cases of this API?

 

  • Semantic Search Engines: Implement the Text to Vector API to power semantic search engines capable of understanding the context and meaning of user queries. By encoding text into vectors, the API facilitates more accurate and relevant search results, enhancing user satisfaction and engagement.

  • Document Clustering and Classification: Utilize the API to cluster and classify large collections of documents based on their semantic similarities. By converting text into vectors, the API enables efficient organization and categorization of documents, streamlining information retrieval and analysis processes.

  • Content Recommendation Systems: Enhance content recommendation systems by leveraging the Text to Vector API to understand the semantic relationships between different pieces of content. By encoding text into vectors, the API enables the identification of relevant content for users based on their preferences and interests.

  • Sentiment Analysis and Opinion Mining: Employ the API to perform sentiment analysis and opinion mining tasks on textual data. By transforming text into vectors, the API facilitates the analysis of sentiment and emotion expressed in user reviews, social media posts, and other forms of textual content.

  • Personalized Product Recommendations: Integrate the API into e-commerce platforms to deliver personalized product recommendations to users. By encoding product descriptions and user preferences into vectors, the API enables the generation of tailored recommendations that match the semantic similarities between products and user preferences.

 

 

 

Are there any limitations to your plans?

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

API Documentation

Endpoints


Returns a 768-dimensional vector as an array that encodes the meaning of any given input text.

 


                                                                            
POST https://zylalabs.com/api/4247/text+to+vector+api/5178/generate
                                                                            
                                                                        

Generate - Endpoint Features

Object Description
Request Body [Required] Json
Test Endpoint

API EXAMPLE RESPONSE

       
                                                                                                        
                                                                                                                                                                                                                            {"embeddings": [0.022502584382891655, -0.07829175889492035, -0.023030739277601242, -0.0051000090315938, -0.08034040033817291, 0.03913218155503273, 0.011342836543917656, 0.0034648010041564703, -0.029457414522767067, -0.018893009051680565, 0.09474341571331024, 0.029274793341755867, 0.03948595002293587, -0.04631657898426056, 0.02542460709810257, -0.03219998627901077, 0.062192872166633606, 0.015559187158942223, -0.04677954688668251, 0.05039012059569359, 0.01461136806756258, 0.023141343146562576, 0.012206641025841236, 0.025069614872336388, 0.002936502918601036, -0.041982151567935944, -0.004010356497019529, -0.022784406319260597, -0.0076859560795128345, -0.03310907632112503, 0.03221183270215988, -0.02099923975765705, 0.011673103086650372, -0.09850738942623138, 1.7793261122278636e-06, -0.022993190214037895, -0.0131140798330307, -0.028022274374961853, -0.06999702751636505, 0.02593141607940197, -0.028950177133083344, 0.08763360232114792, -0.012091899290680885, 0.03986050561070442, -0.03313817083835602, 0.03591080382466316, 0.034609925001859665, 0.06497839093208313, -0.030081775039434433, 0.06981883943080902, -0.003995161037892103, -0.001015992253087461, -0.03501847758889198, -0.04365672916173935, 0.050802573561668396, 0.04687576740980148, 0.05396635830402374, -0.04030090197920799, 0.0032014083117246628, 0.013661866076290607, 0.03821883723139763, -0.003238445147871971, -0.0007845857180655003, -0.01711883209645748, 0.006904410198330879, -0.010923718102276325, 0.00863308273255825, -0.018235811963677406, 0.018793178722262383, 0.015499051660299301, 0.010215045884251595, -0.002483787015080452, 0.010315326042473316, 0.06248869001865387, 0.003603207878768444, -0.006266260053962469, -0.02034061774611473, -0.006723427679389715, -0.035477105528116226, 0.03435384854674339, 0.06722819060087204, 0.09068730473518372, 0.013244078494608402, 0.020659221336245537, -0.02786853536963463, 0.0429694764316082, -0.04668600484728813, 0.0150116216391325, -0.06622843444347382, -0.022759350016713142, -0.062499068677425385, -0.025845518335700035, 0.000731295149307698, 0.011465327814221382, 0.05663831904530525, 0.0020624427124857903, -0.04092491418123245, -0.045505113899707794, 0.016695775091648102, -0.08315562456846237, 0.0020906678400933743, -0.008709234185516834, 0.00010764430044218898, 0.033744506537914276, 0.0056034293957054615, -0.016698049381375313, 0.044791001826524734, 0.0063180578872561455, -0.06459043174982071, 0.052910320460796356, 0.01930190622806549, -0.006201551295816898, -0.11876000463962555, 0.0355963408946991, -0.022886380553245544, -0.015187290497124195, -0.0059265936724841595, -0.0001571462635183707, 0.010706988163292408, 0.0038608312606811523, -0.06870150566101074, -0.016975291073322296, -0.027972958981990814, 0.028048157691955566, 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-0.04774652421474457, 0.008559761568903923, 0.02219409868121147, 0.14923103153705597, -0.019177203997969627, 0.014347637072205544, 0.04399494081735611, -0.0022775910329073668, 0.001381106791086495, 0.032316043972969055, 0.0657534971833229, 0.02269971929490566...
                                                                                                                                                                                                                    
                                                                                                    

Generate - CODE SNIPPETS


curl --location --request POST 'https://zylalabs.com/api/4247/text+to+vector+api/5178/generate' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{ "text": "This is an 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 to Vector 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 Text to Vector API is designed to encode textual data into numerical vectors using advanced Natural Language Processing (NLP) techniques, enabling applications to analyze and understand the semantic relationships between texts.

The API utilizes state-of-the-art NLP machine learning models to convert input text into high-dimensional numerical vectors. These vectors represent the semantic meaning and context of the text, allowing for efficient processing and analysis.

The API employs a variety of NLP models, including Word Embedding models like Word2Vec, GloVe, and FastText, as well as Transformer-based models like BERT, RoBERTa, and GPT, depending on the specific use case and requirements.

Key features include text embedding capabilities, semantic similarity computation, support for text classification and clustering, and compatibility with downstream NLP tasks such as sentiment analysis and named entity recognition.

The API leverages pre-trained NLP models trained on large datasets to capture semantic relationships between words and phrases accurately. Additionally, it may employ techniques like fine-tuning on domain-specific data to enhance performance for specific tasks.

Zyla API Hub is like a big store for APIs, where you can find thousands of them all in one place. We also offer dedicated support and real-time monitoring of all APIs. Once you sign up, you can pick and choose which APIs you want to use. Just remember, each API needs its own subscription. But if you subscribe to multiple ones, you'll use the same key for all of them, making things easier for you.

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 Service Level
100%
 Response Time
759ms

Category:

NLP

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