Retrieve relevant context with RAG in Duaer

What RAG is

RAG combines a language model with external material. On a question, relevant pieces are retrieved and handed to the model, instead of relying only on what the model saw in training. A vector store searches by semantic closeness, not simple keyword match.

Insert into the vector store

1. Fetch the source files or text with nodes.
2. Add a vector-store node and choose insert documents.
3. Connect an embedding model that turns text into vectors.
4. Split into chunks with a data loader. Add metadata to chunks when you need it.

Retrieve

Attach the same vector store as a tool on an Agent, using the same embedding model you used on insert, or query with the vector-store node directly. Set how many chunks to return, and include metadata when you need it.

Do not stuff a whole long document into every prompt. If you change the embedding model and do not rebuild the store, retrieval answers the wrong thing. Template notes link here.