Questions
Answer the following questions with Yes or No:
- Do embeddings convert text into high-dimensional vectors for faster retrieval in RAG?
- Are keyword searches more effective than embeddings in retrieving detailed semantic content?
- Is it recommended to separate RAG pipelines into independent components?
- Does the RAG pipeline consist of only two main components?
- Can Activeloop Deep Lake handle both embedding and vector storage?
- Is the text-embedding-3-small model from OpenAI used to generate embeddings in this chapter?
- Are data embeddings visible and directly traceable in an RAG-driven system?
- Can a RAG pipeline run smoothly without splitting into separate components?
- Is chunking large texts into smaller parts necessary for embedding and storage?
- Are cosine similarity metrics used to evaluate the relevance of retrieved information?
