Quickly build a vector database from stored files with Telnyx Embeddings API. Our intuitive platform makes it easy to embed documents for context-rich inference.
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Embeddings are numerical representations of data essential for AI's contextual understanding, powering applications like chatbots and fraud detection. But the costs and computational resources required for scalable embeddings are a challenge for businesses trying to harness the full potential of AI.
Telnyx Embeddings API allows users to embed data stored in Telnyx Cloud Storage with the click of a button. Dedicated infrastructure powers scalable embeddings via API so businesses can create vector databases that can offer additional context for Inference, boosting the intelligence of AI applications.
At over 90% cheaper than competitors, Telnyx empowers our users to embed and store vast amounts of data, enhancing the effectiveness of GenAI applications.
Prime data for fast inference with an intuitive and cost-effective embeddings API.
Quickly perform embeddings on stored documents, at scale. Our dedicated infrastructure and OpenAI-compatible APIs power fast embedding for all your files.
Whether testing in the portal or deploying to prod via API, our intuitive platform means you don’t have to waste time on MLOps.
At up to 50% cheaper than competitors, Telnyx allows you to build a vector data base without breaking the bank.
Instantly summarize internal documents to extract the most important information or condense for sharing with stakeholders
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Store unlimited vectors in Cloud Storage for fast, contextualized inference.
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Take a look at our helpful tools to get started
Quickly embed your storage buckets with the click of a button in the portal.
We’re looking for companies that are building AI products and applications to test our new Sources and Inference products while they're in beta. If you're interested, get in touch!
Find tips, best practices, and guides for Embeddings Api
Tutorial for AI Playground Quickstart. Start building on Telnyx today.
Perform embedding on a Telnyx Storage Bucket using the a embedding model.
This endpoint returns a list of Open Source and OpenAI models that are available for use.
Perform a similarity search on a Telnyx Storage Bucket, returning the most similar num_docs
document chunks to the query.
Check the status of a current embedding task. Will be one of the following:
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