You're building a system where a language model needs to find relevant text from a document collection before answering a user query. You want to use embeddings and a vector database so semantically similar content can be retrieved even when the wording differs.
How would you use embedding vector databases in an AI system?
Embedding generation for text chunks and queriesVector search and nearest-neighbor retrievalHow retrieval supports a RAG pipelineTrade-offs versus keyword search or fine-tuned QA