How can you use metadata in RAG to improve answers, and what is an advanced technique mentioned?

You can include each chunk's metadata in the template sent to the model, such as source={file_path};text={text}, so the answer can use facts like the document's year. An advanced technique for leveraging metadata is Self-query, which lets the retriever filter chunks at search time using metadata fields.

In RAG, a chunk's metadata can be as important as its text. For example, if a user asks about the latest market research, the model needs to know each document's year. To do this, you export both the text and the metadata from the chunk by changing the template in the Data to Message component, for instance using source={file_path};text={text} instead of just {text}. A more advanced approach is Self-query, in which the retriever uses metadata fields to filter chunks at search time, improving relevance before the model generates an answer.