What is the first step in the RAG flow implementation described in the transcript?	Chunking the text, often by section.
What process is performed on each text chunk after it has been created?	Generating an embedding for the chunk.
In the vector store setup, why is it necessary to store the original chunk text alongside its embedding?	To ensure that when the database returns the most similar embeddings, the developer receives the actual meaningful text associated with that embedding.
What is the purpose of the loop used when inserting data into the vector store?	To pair each chunk with its corresponding embedding and insert that pair into the store.
What is the process used when a user submits a question in the RAG workflow?	Generating a user embedding for the question.
How is the retrieval of relevant documents performed in the vector store?	By searching the store using the generated user embedding.
When searching the vector store, what specific action is taken to retrieve multiple relevant chunks?	Passing an argument to the search function to specify the desired number of results (e.g., finding the top two).
What two key pieces of information are returned by the vector store search results?	The content of the relevant document chunk and its distance from the query embedding.