embeddings

To create vector embeddings for semantic search, you can use a unified API that supports OpenAI, Voyage, and local transformers.js backends to index and query text data. This requires configuring your chosen provider and setting up a local database for vector storage.

Yes, you can use local transformers.js as a backend for semantic search without relying on external APIs. This local embedding approach allows you to generate vectors, index documents, and perform retrieval entirely within your own infrastructure.

Yes, you need a local database for vector storage, indexing, and cache management to retrieve vector embeddings. This database handles the persistent storage layer required to perform fast semantic search queries across your indexed documents and chat contexts.

The best way to index chat histories and memories for semantic retrieval is using a provider-agnostic embeddings API that converts text into vectors and stores them in a local database. This enables fast, scalable semantic search across your conversational data.