Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

LangChain adanwo

Explore LangChain composition, LangGraph stateful control flow, human interrupts, and LangSmith evaluation for LLM applications.

Jẹmọ itọsọna awọn ọnaLangchain
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Kini ero aarin lẹhin LangChain?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini ero aarin lẹhin LangChain?
    • Kikojọ ati orchestrating olona-igbese LLM-agbara ohun elo
    • Ikẹkọ awọn awoṣe ede nla lati ibere
    • Alejo GPUs fun itọkasi awoṣe
    • Ṣiṣẹda awọn aworan lati awọn itọ ọrọ
  2. Tani o ṣẹda LangChain ati ni aijọju nigbawo?
    • Sam Altman ni ọdun 2020
    • Harrison Chase ni ipari ọdun 2022
    • Lukas Biewald ni ọdun 2017
    • Jerry Liu ni ọdun 2023
  3. Kini LangGraph ti a lo fun laarin ilana ilolupo LangChain?
    • Awọn shatti iyaworan ti awọn metiriki ikẹkọ
    • Titẹ awọn itọka lati fi awọn ami pamọ
    • Ilé ti o ni ipinlẹ, awọn ṣiṣan iṣẹ aṣoju iṣakoso bi awọn aworan
    • Yiyipada koodu laarin awọn ede siseto
  4. Kini ipa akọkọ ti LangSmith?
    • Ti o npese sintetiki datasets
    • Nṣiṣẹ awọn oju-iwe wẹẹbu
    • Awọn bọtini API fifipamọ
    • Ṣiṣayẹwo, ṣiṣatunṣe, ati iṣayẹwo awọn ohun elo LLM
  5. Ni LangChain, kini sintasi paipu 'tan | awoṣe | parser' aṣoju?
    • Kikojọ irinše nipasẹ awọn pín Runnable ni wiwo
    • Aṣẹ ikarahun fun ṣiṣe awọn iwe afọwọkọ
    • Ibeere aaye data
    • Ọna kan lati pa awọn ẹwọn rẹ
  6. What happens when a LangGraph node invokes interrupt() with a checkpointer configured?
    • The graph discards its state and restarts training
    • The graph pauses with state saved, then can resume after external input
    • The graph converts the interrupt into a stateless chain
    • The graph changes the base model weights
  7. What kind of evaluation does LangSmith describe as monitoring application traces in production?
    • Offline evaluation on a fixed pre-deployment dataset only
    • A test that fine-tunes the application’s foundation model
    • Online evaluation that checks selected production runs for quality signals
    • An evaluation that reports only GPU training throughput
  8. What does an offline LangSmith evaluation test?
    • Only GPU utilization during model training
    • Every production user response with no reference examples
    • A guarantee of correctness on unseen inputs
    • An application against a curated dataset before deployment