Adding Graph Retrieval to a RAG Pipeline With Python

The majority of retrieval-augmented generation (RAG) examples start as follows: document segmentation; create embedding representations for segmented document sections (“chunks”); store those representations in a vector database and use them to “retrieve” the “best matching” document section(s) for any user-inputted search query. That is a good starting point. However, there is one significant limitation to the process described above: not all questions relate solely to similarity.