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Earlier quoted context omitted.
GPT-4 has good json support now. It’s nice to be able to give it a a schematic get structured json back.
But why tho? At the start of the chain there's a human and its natural language questions. At the end of the chain there's a human waiting natural language answers. In between you may want some form of data storage, and natural language can be that as well, as it makes retrieval trivial for LLM.
My primary use of langchain has been to turn text into a vector database with chroma and then query the vector database to return source materials to an LLM to use. I’d much prefer to move away from it because like the author I find LC’s adherence to system prompts is really bad. I can handle all of the agent side of things, I just want relevant source materials from the vector DB as strings — anyone have a better mo…
My personal preference is Llama Index by Jerry Liu. It excels at clearer docs + better abstraction.
HelloWorldPrint(Baseprint): @validators def input_variables...
bros really just need llm.call(), I ended up rewriting my own tools which goes 10x faster for me personally.
If I want to do something I have to rely on other sources to use it. That's just an overkill.