Returns a 768-dimensional vector as an array that encodes the meaning of any given input text.
Semantic Search Engines: Implement the Embeddings API to power semantic search engines that retrieve results based on the semantic meaning and context of user queries. Users can find relevant documents, articles, or products more accurately, even when using natural language queries or ambiguous terms.
Content Recommendation Systems: Utilize the Embeddings API to enhance content recommendation systems by analyzing the semantic similarities between user preferences and available content. This allows for personalized recommendations tailored to each user's interests, leading to higher engagement and user satisfaction.
Text Classification and Categorization: Integrate the Embeddings API into text classification systems to automatically categorize and label text documents based on their semantic content. This can be applied in various domains such as sentiment analysis, topic modeling, spam detection, and customer support ticket routing.
Plagiarism Detection and Duplicate Content Identification: Deploy the Embeddings API to identify plagiarized or duplicated content by comparing the semantic similarities between documents. This is valuable for academic institutions, publishing platforms, and content creators to ensure originality and maintain quality standards.
Customer Support Chatbots: Enhance the capabilities of customer support chatbots by incorporating the Embeddings API to understand and respond to user queries more intelligently. By encoding user messages into semantic vectors, chatbots can provide more accurate and relevant responses, improving the overall customer experience.
Besides the number of API calls per plan, there are no other limitations.
Returns a 768-dimensional vector as an array that encodes the meaning of any given input text.
Embed - Endpoint Features
| Object | Description |
|---|---|
Request Body |
[Required] Json |
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0.057571105659008026, 0.03438039869070053, 0.01844623126089573],"_note":"Response truncated for documentation purposes"}
curl --location --request POST 'https://zylalabs.com/api/3562/embedding+api/3923/embed' --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 Embeddings API is a tool that encodes text into vector representations using advanced Natural Language Processing (NLP) machine learning models.
The API employs state-of-the-art NLP techniques to transform text input into dense vector embeddings that capture the semantic meaning and context of the text.
Vector embeddings are numerical representations of text that encode semantic information. They are useful because they enable comparison, similarity measurement, and analysis of textual data in mathematical space.
The API can be used in various applications such as semantic search engines, text similarity measurement, content recommendation systems, sentiment analysis, and text classification.
Yes, the API can process text in multiple languages and is designed to handle diverse linguistic patterns and structures.
The Embed 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 similarity measurement and semantic search.
The primary field in the response data is "embeddings," which contains an array of floating-point numbers representing the encoded vector. Each number corresponds to a dimension in the 768-dimensional space.
The response data is structured as a JSON object with a single key, "embeddings," which holds an array of numerical values. This format allows for easy parsing and integration into applications that require vector representations.
The Embed 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 embeddings.
Typical use cases include semantic search, content recommendation, text classification, and plagiarism detection. The vector embeddings can be used to measure similarity and enhance various NLP applications.
Users can leverage the returned vector embeddings for tasks such as comparing text similarity, clustering documents, or feeding into machine learning models for classification or recommendation tasks.
The Embeddings API utilizes state-of-the-art NLP models trained on diverse datasets, ensuring high accuracy in encoding text. Continuous model updates and evaluations help maintain data quality.
Users can expect embeddings to reflect semantic similarities, where similar texts yield closer vector representations in the 768-dimensional space. This allows for effective comparison and clustering of related content.
To obtain your API key, you first need to sign in to your account and subscribe to the API you want to use. Once subscribed, go to your Profile, open the Subscription section, and select the specific API. Your API key will be available there and can be used to authenticate your requests.
You can’t switch APIs during the free trial. If you subscribe to a different API, your trial will end and the new subscription will start as a paid plan.
If you don’t cancel before the 7th day, your free trial will end automatically and your subscription will switch to a paid plan under the same plan you originally subscribed to, meaning you will be charged and gain access to the API calls included in that plan.
The free trial ends when you reach 50 API requests or after 7 days, whichever comes first.
No, the free trial is available only once, so we recommend using it on the API that interests you the most. Most of our APIs offer a free trial, but some may not include this option.
Yes, we offer a 7-day free trial that allows you to make up to 50 API calls at no cost, so you can test our APIs without any commitment.
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.
Please have a look at our Refund Policy: https://zylalabs.com/terms#refund
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