What is an embedding model?
Imagine you have a library full of books, and you want to find the ones that talk about climate change. You could search for the exact words, but that wouldn't help if someone wrote about global warming or environmental impact. An embedding model is like a smart librarian who understands the meaning behind words, not just the words themselves. It takes words or sentences and turns them into a list of numbers—like a secret code that represents the idea.
What is it?
Embedding models are a type of artificial intelligence (AI) tool that helps computers understand language. They are used in AI search engines like Perplexity to find the most relevant information when you ask a question. When you type a query, the embedding model turns it into a set of numbers, and then compares that to other sets of numbers representing documents or web pages. The closer the numbers are, the more similar the ideas are.
How does it work?
Think of embedding models like a map. Every word or sentence is a point on the map. Words that mean similar things are placed close together. For example, car and automobile are close, while car and banana are far apart. This map is built using a lot of text from the internet, so the model learns how words relate to each other.
When you ask a question like, "What causes climate change?", the model creates a number code for that question. Then, it searches through its database of number codes (representing web pages) to find the ones that are closest to your question. These are the most relevant pages.
Why does it matter?
Good embedding models make AI search more accurate. If you ask a question, you want the best answers, not just any answers. The faster and more accurate the model can match your question to the right information, the better the search results will be.
Perplexity has built a special system to make these embedding models run fast on GPUs (Graphics Processing Units). These are powerful computer chips originally used for video games, but now also used for AI tasks. They can process many calculations at once, making the search faster.
Perplexity's system uses three parts:
- Ivy: This part prepares the data for processing.
- Tulip: This part handles the actual calculations to create the number codes (embeddings).
- ROSE: This part stores and retrieves the embeddings quickly.
Together, these three parts make Perplexity's AI search engine smart and fast.
Key takeaways
- Embedding models help AI understand the meaning behind words, not just the words themselves.
- They turn words or sentences into numbers, like a secret code, to compare ideas.
- Perplexity uses special computer parts (GPUs) and three tools (Ivy, Tulip, and ROSE) to make searches faster and more accurate.
- Fast and good embedding models are key to high-quality AI search engines.
So, when you ask a question to an AI search engine, embedding models are the smart helpers that make sure you get the best answers.
