11 points by cbrizz00 5 hours ago
I've been seeing a lot of buzz around GraphRAG and its potential, but many implementations seem to encounter issues, often with Neo4j. Has anyone managed to set up a reliable and cost-effective GraphRAG system in production? I'm curious about real-world experiences and practical solutions.
Do you find you really need that level of “resolution” with memories?
On our [1] chatbots we use one long memories text field per chatbot <-> user relationship.
Each bot response cycle suggests a new memory to add as part of its prompt (along with the message etc)
Then we take that new memory and the existing memories text and feed it to a separate “memory archivist” LLM prompt cycle that’s tasked with adding this new memory and resummarizing the whole thing, yielding a replacement for the stored memories, with this new memory added.
Maybe overly simplistic but easy to manage and pretty inexpensive. The archiving part is async and fast. The LLM seems pretty good sussing out what’s important and what isn’t.
I develop AI girlfriends. I've struggled a lot with achieving natural-feeling recall. I've gone through a few iterations, but I'm currently experimenting with a knowledge graph/vector hybrid that uses an LLM to extract facts to build the graph. Both the performance and $ cost really hurt, but it truly does breathe life into AI characters. I've seen a lot of the commercial products using the latest and most expensive models in the cloud to extract facts. Instead, I fine-tuned a local model on a gpt4-generated dataset, and it works surprisingly well. It will miss some connections but in practice I don't think it will be too noticeable.