I'd argue that building your app based on a quickly evolving library is not a wise idea, esp. if the library is not well documented.
+1 on this experience. Langchain is great at wrapping simpler tasks, but once you start to decouple components you start to run into issues.
Outside a prototype what’s the benefit? The steps I envision are: 1) turn prompt to embeddings 2) return examples that match from vector db 3) load as examples 4) prompt LLM with examples.
Am I missing something?
Why on earth would you want to import multiple dependencies for this?
Like I'd be blown away if someone had a production app where they were able to swap LLM providers (and nothing else) due to langchain. And if that expectation is too high then why not just code against the openai API?