Embeddings have become the backbone of many modern AI applications.From semantic search to retrieval-augmented generation (RAG) and intelligent recommendation systems, embedding models enable systems to understand the meaning behind text, code, or documents, not just the literal words. But generating embeddings comes with trade-offs.Using a hosted API for embedding generation often results in reduced data privacy, higher call costs, and time-consuming model regeneration.When your data is private or constantly evolving (think internal documentation, proprietary code, or customer support content), these limitations quickly become

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