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Comment by fudged71 | Hacker News Reader

Let’s say I want to ingest information from a series of interviews with multiple interviewees (multiple interviews per interviewee). It’s possible their opinions/facts change between interviews; but also each interviewee is going to have different opinions/facts.

Would it make most sense to capture this with multiple Graphiti graphs? Or would it be possible to do this in one graph?

At the end of the day the analysis would be finding insights across all interviewees and you want the cumulative knowledge…

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Hey, I'm one of the developers on Graphiti project

Adding to Daniel's reply, ingesting a series of interviews is definitely doable with one graph, please make sure to add the episodes from the interviews in their chronological order.

After all the episodes are processed by graphiti, you will be able to retrieve the "complete picture" for every participant in the interviews that reflects the possible change in their views/opinions.

You could achieve this with a single graph. Graphiti has a "message" EpisodeType that expects transcripts in a "<user>: <content>" format. When using this EpisodeType, Graphiti pays careful attention to "users," creating nodes for them and maintaining "fact" context for each user subgraph.

"Facts" shared across all users will also be updated universally. Alongside Graphiti's search, you'd be able to use cypher to query Neo4j to, for example, find hub nodes (aka highly-connected nodes), identifying common beliefs.

More here: https://help.getzep.com/graphiti/graphiti/adding-episodes

Oh that’s excellent! Thank you

I see that you mention Microsoft’s GraphRAG. My understanding is that a key part of their approach is hierarchical agglomeration of graph clusters to be able to answer wide questions from the graph. Is that in the works?

Yes, that is in the works and is a high priority for us. The major discussion point internally around implementing this feature has been on the retrieval portion. In general we want to provide many flexible search strategies that return a variety of different information. We want to organize search in such a way that it is flexible enough to meet a variety of demands, while also being ergonomic enough to be usable and understandable. We want to make sure that we update our retrieval approach at the same time as adding the community summaries so that it is easy to make use of this additional information.

Our implementation will likely involve us adding community nodes that will contain a summary of the nodes in that community. Did you have any perspective or opinions on best ways to implement the graphRAG style summarizations?

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