this is precisely the problem i encountered and tried to solve with Edgechains. we think Generative AI is a config management problem (like Terraform or Kubernetes).
>None of this stuff is reusable. Langchain is attempting to set up abstractions to reuse everything. But what we end up with a mediocre DAG framework where all the instructions/data passing through is just garbage. The longer the chain, the more garbage you find at the output.
chains X prompts X LLMs == pods X services X nodes in Terraform.
So we model it on top of config management grammar that is proven to work in large production config - jsonnet.
>None of this stuff is reusable. Langchain is attempting to set up abstractions to reuse everything. But what we end up with a mediocre DAG framework where all the instructions/data passing through is just garbage. The longer the chain, the more garbage you find at the output.
chains X prompts X LLMs == pods X services X nodes in Terraform.
So we model it on top of config management grammar that is proven to work in large production config - jsonnet.
A trivial example is this - https://github.com/arakoodev/EdgeChains/blob/main/Examples/r...
Would love to get an example of complex chains (even if u have an ARxiv paper) that you think we could solve in Edgechains-jsonnet ?