Very cool! I'm curious--I'd imagine that some long tail podcasts have transcripts that are too long to fit within a standard context window. Do you have some strategy for handling these?
I wouldn’t necessarily recommend _using_ LangChain, but their summarization docs might be of interest: https://python.langchain.com/en/latest/modules/chains/index_...
I don’t know if this is a spicy or a generally-agreed-upon take: my feeling is that, while LangChain was useful in that it helped the community codify some early intuitions about LLM invocation patterns, it’s basically a grab bag of partially complete somewhat disconnected utilities. It nods to composability but, in practice, its pieces often don’t fit together. On the Python side, it suffers from poor typing: when creating a chain, it’s often impossible to know what the full set of configuration options is without digging deep into LangChain’s code. It’s catch-as-can whether you can deeply configure specific sub-aspects of a chain.
There are other things I want in my own code at the moment, including keeping track of how many input/output tokens each of my actions takes, etc.
I dunno, maybe I’m the only one here. Curious what others think.