Coherence RAG supports document ingestion and vector search via one or more _document stores_. Each document store can be configured independently, and can use different embedding model, index type, document parser, and chunking strategy.

Document store represents a _document ingestion boundary_, but not necessarily a _vector search boundary_. In other words, documents are always ingested into a specific store, but vector search can be performed both within a single store, and in parallel, across all the stores.

This allows users to create individual stores for different documentation sets, and to limit the scope of the search to a specific store, when the context of the search is known, while still allowing search across all index content when the necessary context is missing. For example, users could index documentation for each individual product (or a project) into a separate document store, allowing them to significantly narrow down the size of the vector space that needs to be searched when they are interested in a specific product, while still being able to search across all the stores to answer questions that are not product-specific.