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 |
Continue with Google
Continue with Github
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0.057571105659008026, 0.03438039869070053, 0.01844623126089573, -0...
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 commitments. One click upgrade/downgrade or cancellation. No questions asked.
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.
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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