Similarity Search
Similarity search is the operation of finding the k nearest vectors to a query embedding in a vector database, returning the most semantically similar stored passages.
What is Similarity Search?
Similarity search is the operation of finding the k nearest vectors to a query embedding in a vector database, returning the most semantically similar stored passages.
Similarity search is the operation of finding the k nearest vectors to a query embedding in a vector database, returning the most semantically similar stored passages.
Where is it used?
Every RAG pipeline performs similarity search before generation; FAISS `IndexFlatIP.search`, Qdrant `.search()`, and pgvector `<=>` operator implement this under the hood.
How to build it
Build a FAISS `IndexFlatL2(d)`, call `.add(vectors)` and `.search(query_vec, k=5)` to get indices and distances, then map indices back to original chunk text for the prompt.