Returns a 768-dimensional vector as an array that encodes the meaning of any given input text.
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.
Besides the number of API calls, there is no other limitation.
Returns a 768-dimensional vector as an array that encodes the meaning of any given input text.
Generate - Endpoint Features
| Object | Description |
|---|---|
Request Body |
[Required] Json |
{"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, 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-0.017957786098122597, -0.012000484392046928, 0.0008964621811173856, -0.014768925495445728, 0.04969446733593941, 0.006979559548199177, 0.02643679454922676, 0.046177417039871216, 0.032043423503637314, -0.03660064935684204, -0.0050842552445828915, 0.0688665509223938, 0.05680043622851372, -0.014677781611680984, -0.04784739762544632, 0.012187196873128414, -0.025042042136192322, 0.03124428726732731, -0.017944026738405228, -0.030582627281546593, 0.001717127626761794, 0.07021261751651764, 0.05673833191394806, -0.01793675683438778, 0.024400077760219574, -0.028652558103203773, -0.011586768552660942, -0.02704085037112236, 0.039513107389211655, 0.042995698750019073, 0.029097286984324455, 0.02808394283056259, -0.04627488553524017, -0.004282901529222727, 0.011990219354629517, -0.012022530660033226, -0.009469372220337391, 0.023506544530391693, -0.030062736943364143, -0.0169608686119318, -0.0015973638510331511, -0.013061029836535454, 0.05358842387795448, 0.02537827007472515, 0.026025012135505676, 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0.032316043972969055, 0.0657534971833229],"_note":"Response truncated for documentation purposes"}
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." }'
| Header | Description |
|---|---|
Authorization
|
[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
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.
The Generate endpoint returns a 768-dimensional vector as an array, which encodes the semantic meaning of the input text. This vector representation allows for various applications, such as semantic search and text comparison.
The primary field in the response data is "embeddings," which contains an array of numerical values representing the encoded vector of the input text. Each value contributes to the overall semantic representation.
The response data is structured as a JSON object with a single key, "embeddings," which maps to an array of floating-point numbers. This array represents the 768-dimensional vector derived from the input text.
The Generate endpoint accepts a single parameter: the input text to be encoded. Users can customize their requests by providing different text inputs to generate corresponding vector representations.
Users can leverage the returned vector data for various applications, such as comparing text similarity, clustering documents, or enhancing recommendation systems by analyzing the semantic relationships encoded in the vectors.
Typical use cases include powering semantic search engines, document classification, content recommendation systems, and sentiment analysis. The vector representations enable nuanced understanding and processing of textual data.
Data accuracy is maintained through the use of pre-trained NLP models that have been trained on extensive datasets. These models capture semantic relationships effectively, ensuring reliable vector representations for diverse text inputs.
Quality checks include validation against benchmark datasets and performance evaluations on specific NLP tasks. This ensures that the vectors generated maintain high accuracy and relevance for various applications.
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