This is a great README! It clearly breaks down some approaches to RAG. I also approciate how you strive to de-mystify what’s going on under the hood, which is in many ways VERY simple.
This seems very similar to LangSmith’s trace monitoring, which I have been leaning on heavily for observability. You also mention LlamaIndex— how do you see your project fitting into the ecosystem?
I don’t think I would able to use this yet because it is serial. Is it possible to non-serially issue independent sub-question queries?
In my experimental agent system, waggledance.ai[1], I have been working on a pre-agent step of picking and synthesizing the right context and tools[2] for a given subtask of a larger goal, and it seems to be boosting results. It looks like now I have to try sub-question answering in the mix as well.
This seems very similar to LangSmith’s trace monitoring, which I have been leaning on heavily for observability. You also mention LlamaIndex— how do you see your project fitting into the ecosystem?
I don’t think I would able to use this yet because it is serial. Is it possible to non-serially issue independent sub-question queries?
In my experimental agent system, waggledance.ai[1], I have been working on a pre-agent step of picking and synthesizing the right context and tools[2] for a given subtask of a larger goal, and it seems to be boosting results. It looks like now I have to try sub-question answering in the mix as well.
[1] demo - https://waggledance.ai
[2] relevant code sample - https://github.com/agi-merge/waggle-dance/blob/1b14163c24fd2...