Looks like the test-setup confuses knowledge graphs with graph databases. The code just creates a neo4j database from a document, not a knowledge graph (basically uses neo4j as vector database). A knowledge graph would be created by a LLM as a preprocessing step (and queried similary by an LLM). This is a different approach than was tested, an approach that trades preprocessing time and domain knowledge for accuracy. Reference: https://python.langchain.com/v0.1/docs/use_cases/graph/const...
Yeah, I think the dataset is flawed. GraphRAG appears to be aimed at navigating the Microsoft 365 document and people graph that you get in an organization setting, not doing a homogenous search.
Looks and feels a lot like AWS Amplify (which is not a bad thing). I assume the difference is that this runs on the frontend server (node) where Amplify tranformation happens on AppSync. Definetly going to test this.
What is bit unclear what is the interface with the frontend server to tailcall,like how do I pass the JWT tokens from node app to @upstream headers?
I am not sure if I understand what you by the "frontend server"? I think Tailcall is closer to AppSync than Amplify.
Second part of your question: You can define a set of "allowed headers" that will be automatically forwarded upstream. In your case, you can set the allowed headers to `Authorization`.
