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For example, for us, we are building an LLM chatbot that pulls in the data of a technical book publisher. They have 20 years of technical books, and 20 years of
by petervandijck 3y ago
For example, for us, we are building an LLM chatbot that pulls in the data of a technical book publisher. They have 20 years of technical books, and 20 years of videotaped conference talks.
Hard:
- We're using LangChain, which isn't always great
- The data pipeline was trickier than I had initially thought
- Indexing embeddings (in PostGres) is just hard (requires tons of ram)
But the hardest thing has been working on conversation quality. We've started to use LangSmith, which was a godsend for tracing and observability, and came out fairly recently. But it's not perfect and I wish there were better tools out there.
- jondwillis 3y agoWhat do you find lacking in LangSmith? I have been using it since the week it was in private beta, albeit a lot less recently, and thought it was good, though with some confusing UX and a handful of bugs.
- petervandijck 3y agoJust generally a lot of abstractions that seem sometimes overwrought, and seem to hide details.
- jondwillis 3y agoI would say that's langchain's ethos as a whole! haha
- catlover76 3y ago[dead]