Embeddings
Embeddings are numeric vectors that represent text (or images) so similar meanings sit close together. RAG uses them to find relevant chunks.
In depth
You send text to an embedding API, store the vector, and at query time embed the question and search nearest neighbors (often pgvector).
Cost hits at ingest (every chunk) and sometimes at query. Re-embedding a 10k-doc corpus is a bill, not a footnote.
MVP: one embedding model, one index, metadata filters for tenant_id.
Real example
“refund policy” and “money-back window” retrieve the same chunk even if the words differ.
Tools & calculators
Related terms
API (Application Programming Interface)
An API (Application Programming Interface) is a set of rules and protocols that allows different software applications to communicate with each other — like a contract defining how one piece of software can request data or actions from another.
RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is a pattern where the system searches a document store, then asks an LLM to answer using the retrieved chunks — ideally with citations.
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