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RAG Cost Estimates: Token Counts, Embeddings, and Node.js Semantic Search
Short answer: For an ask-your-docs semantic search app, batch document indexing, estimate token spend before rollout, and send chat only the top retrieved chunks; that keeps RAG cost under deliberate control.
I build RAG and agent features in Python, even when the serving app is Node.js, because I want the eval harness close to the retrieval experiments. The language boundary is rarely the budget problem. Prompt shape is. Start by treating every uploaded document as an ingestion job: split i...









