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You want to build a chatbot that shows user responses token-by-token as they are generated. Which combination of LangChain features should you use in production?

hard📝 Application Q15 of 15
LangChain - Production Deployment
You want to build a chatbot that shows user responses token-by-token as they are generated. Which combination of LangChain features should you use in production?
AUse <code>streaming=True</code> with callbacks, but disable token printing to improve speed.
BUse <code>streaming=True</code> with a callback handler implementing <code>on_llm_new_token</code> to display tokens live.
CUse <code>streaming=True</code> but no callbacks, then print the final output after completion.
DUse <code>streaming=False</code> and collect all tokens before displaying the full response.
Step-by-Step Solution
Solution:

  1. Step 1: Identify streaming usage for live token display

    Streaming must be enabled to get tokens as they generate, not after full response.

  2. Step 2: Use callback handler to process tokens live

    Implementing on_llm_new_token in a callback lets you display tokens immediately.

  3. Step 3: Confirm best practice for production chatbot

    Combining streaming=True with a callback that prints tokens live is the correct approach.

  4. Final Answer:

    Use streaming=True with a callback handler implementing on_llm_new_token to display tokens live. -> Option B

  5. Quick Check:

    Streaming + on_llm_new_token = live chatbot tokens [OK]
Quick Trick: Streaming plus on_llm_new_token callback shows tokens live [OK]
Common Mistakes:
MISTAKES
  • Disabling streaming and expecting live tokens
  • Not using callbacks to handle tokens
  • Printing tokens only after full response

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