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...
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?