Embeddings and Vector Search

1. What is the primary purpose of an embedding model?

To generate text from vectors To convert text into vectors representing meaning To store vectors in a database To measure the length of text strings

2. What does a vector database primarily do?

Stores raw text data Conducts keyword searches Performs nearest-neighbor searches on vectors Translates text into embeddings

3. What is a significant risk associated with vector search?

Retrieving too many irrelevant results Inability to store vectors High computational costs Missing the nearest vector

4. What critical design decision must be made regarding chunking?

Determining the length and structure of document chunks Choosing the embedding model Selecting the storage format for vectors Establishing a database schema

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