Semantic vector generation: turns batches of text (up to 64 per request) into dense 768-dimension embeddings ready to feed into a vector database or similarity search index. Runs without any external API key or subscription, pay only for the call. Well suited for building RAG retrieval layers, clustering related content, or scoring how similar two passages are.

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