embeddings

To generate vector embeddings for semantic search, you process text through configurable providers like OpenAI or local models. The system creates vector representations that capture semantic meaning, supporting batch processing and caching results in a database for efficient retrieval.

Yes, semantic search performs similarity searches against stored vector embeddings to find relevant information. This allows you to retrieve documents based on semantic meaning, finding related content even when exact phrases are not present in the text.

Yes, semantic search and similarity analysis work with configurable providers including OpenAI and local models. You can select your preferred provider to generate vector embeddings and perform similarity scoring between texts based on your specific infrastructure needs.

The best way to manage large collections of vector embeddings is by integrating with a database for caching and storage. This supports batch processing during embedding generation and enables efficient semantic search queries against the stored collections for similarity analysis.

To calculate similarity scores between texts, you generate vector embeddings for each document and compare their vector representations. The system performs similarity scoring between the texts to quantify how closely related they are based on their semantic meaning.