Faiss is for similarity search over vectors via k-NN. GraphRAG is, well, a graph. More precisely, GraphRAG has more in common with old school knowledge graph techniques involving named entity extraction and the various forms of black magic used to identify relationships between entities. If you remember RDF and the semantic web it's sort of along those lines. One of the uses of Faiss is in a k-NN graph but the edges between nodes in that graph are (similarity) distance based.

Looking at an example prompt from GraphRAG will make things clear https://github.com/microsoft/graphrag/blob/main/graphrag/pro...

especially these lines:

Return output in English as a single list of all the entities and relationships identified in steps 1 and 2.

Format each relationship as a JSON entry with the following format:

{{"source": <source_entity>, "target": <target_entity>, "relationship": <relationship_description>, "relationship_strength": <relationship_strength>}}

Excuse me, how is it not?