Embeddings are way to represent data in a way that makes it easy to find the "relatedness" between 2 or more data points. They're powerful because they allow you to store unstructured data (aka things that don't fit tightly into JSON) and create searchable systems out of them.
Below, you'll find examples of what you can build with embeddings.
Embeddings are a powerful way to represent data. They can be used to create recommendation engines, search engines, and Q&A engines. Embedbase gives you and sdk & an api to create these systems without any ML knowledge.
Embedbase is fully open-source and is designed to accompany you from your first line of code to shipping your apps to millions of users.
The most common use case at the moment is to create "ChatGPT for" experiences. Embedbase make it easy to overcome prompt size limitations.
Embeddings are a tool that can be used to solve a wide variety of problems. Below are some examples of what you can build with them:
- Semantic Search (where results are ranked by relevance to a query string)
- Clustering (where text strings are grouped by similarity)
- Recommendations (where items with related text strings are recommended)
- Anomaly detection (where outliers with little relatedness are identified)
- Diversity measurement (where similarity distributions are analyzed)
- Classification (where text strings are classified by their most similar label)