Embeddings Models and Vector Stores: Basics
Leveraging language models for document analysis faces a challenge. Large documents easily surpass model processing capacity. The model can only inspect a few thousand words at a time. Combining embeddings and vector databases is a solution.
Embeddings:
Embeddings are numerical representations for text fragments. They capture semantic meaning. Similar content produces similar vectors, enabling easy comparison in the vector space. This helps identify text segments for language model input in question answering.
Vector Databases:
A vector database efficiently stores numerical representations, populated with text chunks from incoming documents. Large documents are broken into smaller, manageable chunks to ensure only relevant sections go to the language model.
