Superlinked
The Vector Computer
Superlinked is a compute framework for your information retrieval and feature engineering systems, focused on turning complex data into vector embeddings. Vectors power most of what you already do online - hailing a cab, finding a funny video, getting a date, scrolling through a feed or paying with a tap. And yet, building production systems powered by vectors is still too hard! Our goal is to help enterprises put vectors at the center of their data & compute infrastructure, to build smarter and more reliable software.
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Features
- Multi-modal Vectors
- Multi-objective Queries
- Infrastructure as Code
- Semantic Search
- Recommendations
- Analytics
Advantages
- Combine text, images and structured metadata into multi-modal vectors that fully describe your entities in their complex context.
- Smoothly navigate the trade-off between multiple competing objectives like relevance, freshness and popularity.
- Manage the compute layer that sits between your data infrastructure and your vector database through a simple python SDK.
- Combine semantic relevance and user feedback to reliably retrieve the optimal document chunks in your Retrieval Augmented Generation system.
- Combine semantic relevance and document freshness in your search system, because more recent results tend to be more accurate.
Disadvantages
- Can be complex to set up and configure.
- May require specialized expertise to use effectively.
- Can be expensive to implement and maintain.
Frequently Asked Questions
-
Q:What is Superlinked?
A:Superlinked is a compute framework for your information retrieval and feature engineering systems, focused on turning complex data into vector embeddings. -
Q:What are the benefits of using Superlinked?
A:Superlinked can help you build smarter and more reliable software by putting vectors at the center of your data & compute infrastructure. -
Q:How do I get started with Superlinked?
A:You can get started with Superlinked by getting a demo or reading the documentation.
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