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Structured Output with LangChain and Llamafile

blog.brakmic.com

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Re: Structured Output with LangChain and Llamafile

#5

Post by pcwelder »

```

try:

    answer = chain.invoke(question)

    # print(answer) # raw JSON output

    display_answer(answer)
except Exception as e:

    print(f"An error occurred: {e}")

    chain_no_parser = prompt | llm

    raw_output = chain_no_parser.invoke(question)

    print(f"Raw output:\n\n{raw_output}")
```

Wait, are you calling LLM again if parsing fails just to get what LLM has sent to you already?

The whole thing is not difficult to do if you directly call API without Lang chain, it'd also help you avoid such inefficiency.

Re: Structured Output with LangChain and Llamafile

#7

Post by anshumankmr »

dcreater wrote: post #2

People still use langchain?

Its good for quickly developing something but for production, I do not think so.We used it for a RAG application I built last year with a client, ended up removing it piece by piece, and found our app responded faster.

But orgs think its some sort of flagbearer of LLMs.As I am interviewing for other roles now, HRs from other companies still ask for how many years of exp I have with Langchain and Agentic AI.

Re: Structured Output with LangChain and Llamafile

#10

Post by Hugsun »

zingababba wrote: post #8
dcreater wrote: post #2

People still use langchain?

What should be used instead?

I gave up after it didn't let me see the prompt that went into the LLM, without using their proprietary service. I'd recommend just using the API directly. They're very simple. There might be some simpler wrapper library if you want all the providers and can't be bothered to implement the support for each. Vercel's ai-sdk seems decent for JS.
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