Chapter 2, RAG Embedding Vector Stores with Deep Lake and OpenAI
- Do embeddings convert text into high-dimensional vectors for faster retrieval in RAG?
Yes, embeddings create vectors that capture the semantic meaning of text.
- Are keyword searches more effective than embeddings in retrieving detailed semantic content?
No, embeddings are more context-aware than rigid keyword searches.
- Is it recommended to separate RAG pipelines into independent components?
Yes, this allows parallel development and easier maintenance.
- Does the RAG pipeline consist of only two main components?
No, the pipeline consists of three components – data collection, embedding, and generation.
- Can Activeloop Deep Lake handle both embedding and vector storage?
Yes, it stores embeddings efficiently for quick retrieval.
- Is the text-embedding-3-small model from OpenAI used to generate embeddings in this...
