Unlock the potential of your data with vector databases! In my latest article, I dive into the potential of vector databases, focusing on their application in similarity search and Retrieval Augmented Generation (hashtag#RAG). Discover practical use cases and step-by-step examples to harness this cutting-edge technology for natural language processing, image recognition, and more.
#vectordatabase #vectorindex #vectordb #rag #similaritysearch https://www.linkedin.com/pulse/vector-databases-indexes-similarity-search-andrag-andreas-buckenhofer-fsyde/

"List some example questions that the provided content answers."

Did you know that you can easily use #chatbots to generate phrases for a #VectorIndex? #Embeddings of example questions match way better to embeddings of user queries than whole document embeddings or keyword embeddings.

They aren't mutually exclusive though – it makes sense to use all those embeddings for a single document, and obviously multiple embeddings of each type (except for the whole document embedding obviously).

And once more, never use embeddings significantly larger than 128 dimensions.