Provides semantic search backbone.
Embedding models convert text chunks into high-dimensional vectors where similar texts are positioned closer together in the vector space. These embeddings enable semantic search and similarity matching, capturing meaning beyond keyword matching.
The quality of your embeddings directly affects retrieval accuracy and the system's ability to find conceptually related content. Better embeddings capture more nuanced semantic relationships and lead to more relevant search results.
embeddingCreation.configureModel
Vector embeddings are numerical representations of text that capture semantic meaning. They allow machines to understand similarities between different pieces of text based on their content rather than just matching keywords.
Embedding models convert text into high-dimensional vectors. These vectors typically have hundreds or thousands of dimensions, with each dimension representing some aspect of the text's meaning.
Regular embedding refreshes are crucial for RAG systems. When source documents change, their embeddings must be updated to ensure the retrieval system returns current information.
The quality of embeddings directly impacts retrieval performance. Better embeddings lead to more accurate semantic search results and ultimately better RAG outputs.
Our company's Q2 financial results exceeded expectations with a 15% revenue increase compared to the previous quarter. The board has approved a special dividend for shareholders.
embeddingCreation.2dProjection
embeddingCreation.semanticSearchDemo
embeddingCreation.searchExplanation
Building a robust Embedding Creation & Refresh solution is challenging. Respeak's Enterprise RAG Platform handles this complexity for you.