LangChain is definitely verbose, and personally I don't use it. That being said, they have some pretty interesting tools the author didn't cover: their example selectors e.g. the MaxMarginalRelevance selector [1] is interesting, useful, and similar example selector tools become something close to necessary for managing large LLM applications.
I wish the code quality was better, but poking around their docs does give pretty interesting ideas you can build yourself, even if you don't use LangChain. I think the release of OpenAI function calling has also just kinda sideswiped the need for large parts of these kind of frameworks — you don't need much help in coercing to JSON or parsing anymore if you use the function calling API.
I wish the code quality was better, but poking around their docs does give pretty interesting ideas you can build yourself, even if you don't use LangChain. I think the release of OpenAI function calling has also just kinda sideswiped the need for large parts of these kind of frameworks — you don't need much help in coercing to JSON or parsing anymore if you use the function calling API.
1: https://python.langchain.com/docs/modules/model_io/prompts/e...