Kuphatikiza RAG ndi LLMs kwa Enhanced Software Development Workflows

** Kuphatikiza RAG ndi ma LLM a Enhanced Software Development Workflows**

Chiyambi

Mawonekedwe a chitukuko cha mapulogalamu akusintha mwachangu, motsogozedwa ndi kupita patsogolo kwa nzeru zamakono (AI), kuphunzira pamakina (ML), ndi kukonza zilankhulo zachilengedwe (NLP). Matekinoloje awiri ofunika kwambiri omwe akusintha momwe timapangira mapulogalamu ndi Retrieval-Augmented Generation (RAG) ndi Large Language Models (LLMs). M'nkhaniyi, tiwona kuphatikiza kwa RAG ndi ma LLM kuti tithandizire njira zamaukadaulo zamapulogalamu, kukonza kupanga ma code, komanso kupititsa patsogolo zokolola zamapulogalamu.

Kodi Retrieval-Augmented Generation (RAG) ndi chiyani?

RAG ndi njira yomwe imaphatikizira mphamvu zotengera kutengera komanso njira zopangira mibadwo ku NLP. Zimaphatikizapo kupezanso chidziwitso choyenera kuchokera kumagulu akuluakulu a malemba ndikuchigwiritsa ntchito kuwonjezera kutulutsidwa kwa malemba atsopano. Njirayi yasonyezedwa kuti ndi yothandiza kwambiri pa ntchito zosiyanasiyana za NLP, kuphatikizapo kuyankha mafunso, mwachidule malemba, ndi kupanga ma code.

Kodi Zinenero Zazinenero Zazikulu (LLM) ndi ziti?

Ma LLM ndi mtundu wa neural network womwe umapangidwa kuti uzitha kupanga ndi kupanga chilankhulo chofanana ndi cha anthu. Nthawi zambiri amaphunzitsidwa pazosungidwa zazikulu zamawu ndipo amatha kusanjidwa bwino kuti azigwira ntchito zinazake monga kumasulira zilankhulo, kufupikitsa mawu, ndi kupanga ma code. Ma LLM apeza zotsatira zapamwamba mu ntchito zambiri za NLP ndipo amagwiritsidwa ntchito kwambiri pamapulogalamu monga ma chatbots, othandizira kwenikweni, ndi machitidwe omasulira zilankhulo.

** Kuphatikiza RAG ndi ma LLM a Enhanced Software Development Workflows**

Zida Zoyimba: LangChain ndi DSPy

Kuti tiphatikize RAG ndi ma LLM, timafunikira zida za orchestration zomwe zimatha kuyendetsa kayendedwe ka data ndikuwongolera magwiridwe antchito a RAG ndi LLM zigawo. Zida ziwiri zodziwika bwino zoyimba zomwe zingagwiritsidwe ntchito pazifukwa izi ndi LangChain ndi DSPy.

LangChain

LangChain ndi laibulale ya Python yotseguka yomwe imapereka API yosavuta komanso yodziwikiratu pomanga mapulogalamu a RAG. Zimalola otukula kuti aphatikize mosavuta RAG ndi ma LLM ndi zigawo zina za NLP kuti apange mayendedwe amphamvu opanga mapulogalamu.

DSPy

DSPy ndi laibulale ina ya Python yotseguka yomwe imapereka API yapamwamba kwambiri yomanga mapulogalamu a RAG. Idapangidwa kuti igwire ntchito mosasunthika ndi ma LLM ndi zida zina za NLP, ndikupangitsa kuti ikhale chisankho choyenera kuphatikiza RAG ndi ma LLM.

Code Chitsanzo: Kuphatikiza RAG ndi LLMs pogwiritsa ntchito LangChain

Nachi chitsanzo chosavuta chomwe chikuwonetsa momwe mungaphatikizire RAG ndi ma LLM pogwiritsa ntchito LangChain:

import langchain
kuchokera ku langchain.chains import RAGChain


rag_model = langchain.RAG(
    index=langchain.Index(
        dzina = "mndandanda_wanga",
        zolemba="document1", "document2", "document3"],
    ),
    retriever=langchain.Retriever(
        dzina = "my_retriever",
        chitsanzo="my_retriever_model",
    ),
    jenereta=langchain.Jenereta(
        dzina = "my_generator",
        chitsanzo="my_generator_model",
    ),
)

# Fotokozani mtundu wa LLM
llm_model = langchain.LLM(
    dzina = "my_llm",
    chitsanzo="my_llm_model",
)

# Pangani unyolo wa RAG
rag_chain = RAGChain (
    dzina = "my_rag_chain",
    chitsanzo=chiguduli_chitsanzo,
    llm=llm_model,
)

# Gwiritsani ntchito unyolo wa RAG kupanga ma code
kodi = rag_chain.generate(
    input_text="Lembani ntchito yomwe imatenga magawo awiri monga cholowa ndikubweza ndalama zawo",
)

sindikiza (kodi)

Chitsanzo cha code ichi chikuwonetsa momwe mungatanthauzire chitsanzo cha RAG pogwiritsa ntchito LangChain, kufotokozera chitsanzo cha LLM, kupanga unyolo wa RAG, ndikugwiritsa ntchito unyolo kupanga code.

**Malamulo/Zidutswa Zapamwamba za 5-10 **

Nawa malamulo / zidule zapamwamba 5-10 zophatikizira RAG ndi LLMs pogwiritsa ntchito LangChain ndi DSPy:

langchain.RAG(): Imapanga chitsanzo cha RAG.
langchain.Index(): Imapanga cholozera chosungira ndikuchotsa zikalata.
langchain.Retriever(): Amapanga chofufumitsa kuti atengenso zolemba zoyenera.
langchain.Generator(): Amapanga chitsanzo cha jenereta kuti apange mawu atsopano.
langchain.LLM(): Amapanga chitsanzo cha LLM.
RAGChain (): Amapanga chitsanzo cha RAG.
rag_chain.generate(): Amagwiritsa ntchito tcheni cha RAG kupanga khodi.
llm_model.generate(): Amagwiritsa ntchito mtundu wa LLM kupanga khodi.
index.add_document(): Imawonjezera chikalata ku index.
retriever.retrieve(): Imapezanso zolemba zofunikira pogwiritsa ntchito chotsitsa.

**Kuwona **

Nachi chithunzithunzi cha zigawo za RAG ndi LLM pogwiritsa ntchito Chart.js:

{
  "type": "bar",
  "data": {
    "labels": ["RAG", "LLM"],
    "ma dataset": [
      {
        "label": "Components",
        "data": [10, 20],
        "backgroundColor": ["#4CAF50", "#FF9800"]
      }
    ]
  },
  "zosankha": {
    "mutu": {
      "kuwonetsa": zoona,
      "text": "RAG ndi LLM Components"
    },
    "masikelo": {
      "yAxes": [
        {
          "kuwonetsa": zoona,
          "beginAtZero": zoona
        }
      ]
    }
  }
}

Kuwoneka uku kukuwonetsa kuchuluka kwa zigawo mumitundu ya RAG ndi LLM.

Mapeto

Kuphatikiza kwa RAG ndi ma LLM kuli ndi kuthekera kosintha momwe timapangira mapulogalamu. Pophatikiza mphamvu za njira zopezera komanso zotengera mibadwo, RAG imatha kuthandiza ma LLM kupanga ma code olondola komanso oyenera. Izi zitha kubweretsa kusintha kwakukulu pakupanga kwaopanga mapulogalamu, mtundu wama code, komanso kufalikira kwa mapulogalamu onse. Ndi zida zoyimba ngati LangChain ndi DSPy, opanga amatha kuphatikiza RAG ndi ma LLM ndi zida zina za NLP kuti apange mayendedwe amphamvu opangira mapulogalamu.

Kuphatikiza RAG ndi LLMs kwa Enhanced Software Development Workflows

Have Questions?