**Ukudibanisa iLangChain kuLwakhiwo oluPhezulu lweRAG kwizicelo zePython **

Isishwankathelo

Kwiminyaka yakutshanje, intsimi ye-Natural Language Processing (NLP) ibone ukuqhubela phambili okubonakalayo, ngokukodwa kwiinkqubo ze-Retrieval-Augmented Generation (RAG). Ezi nkqubo ziye zayiguqula indlela esijongana ngayo nemisebenzi efana nokuphendula imibuzo, ukushwankathela okubhaliweyo, kunye nencoko ye-AI. ILangChain, ilayibrari yePython yokusika, iye yavela njengesixhobo esinamandla sokwakha izakhiwo zeRAG. Kule nqaku, siya kukhangela kwihlabathi le-LangChain kwaye sihlolisise ukuhlanganiswa kwayo kunye nezicwangciso zewebhu ezisekelwe kwi-Python ezifana ne-FastAPI kunye neFlask, ngelixa sisebenzisa izixhobo ze-orchestration zokuphucula ukufunyanwa kwedatha kunye nokusebenza.

Yintoni iLangChain?

ILangChain yilayibrari yePython evulekileyo eyenzelwe ukwenza lula uphuhliso lweenkqubo zeRAG. Inika isethi ebanzi yezixhobo kunye nee-API zokwakha, ukuqeqeshwa, kunye nokuthunyelwa kweemodeli ze-AI, kunye nokudibanisa kunye nemithombo eyahlukeneyo yedatha kunye nezicwangciso zewebhu. Ugxininiso oluphambili lukaLangChain kukuvumela abaphuhlisi ukuba benze izakhiwo zeRAG ezinobungakanani kunye nobuchule obunokusingatha imisebenzi enzima ye-NLP.

Iimpawu eziphambili zeLangChain

1. ** I-Modular Architecture **: Uyilo lwemodyuli yeLangChain ivumela abaphuhlisi ukuba badibanise ngokulula iimodeli ezahlukeneyo ze-AI, imithombo yedatha, kunye nezicwangciso zewebhu, okwenza kube yinto efanelekileyo yokwakhiwa kweenkqubo ze-RAG ezinzima.
2. **Izixhobo ze-Orchestration **: I-LangChain ibonelela ngezixhobo ezininzi ze-orchestration ezenza abaphuhlisi balawule kwaye baqhube iiseti ezinkulu zedatha, kunye nokudibanisa kunye nemithombo eyahlukeneyo yedatha kunye nee-API.
3. ** Inkxaso yeeModeli ezininzi ze-AI **: I-LangChain isekela uluhlu olubanzi lweemodeli ze-AI, kubandakanywa neemodeli ezisekelwe kwi-transformer, ezivumela abaphuhlisi ukuba bakhethe imodeli engcono kakhulu kwimeko yabo yokusetyenziswa.
4. ** Ukudibanisa kunye neZikhokelo zeWebhu **: I-LangChain ibonelela ngokudityaniswa komthungo kunye nezikhokelo zewebhu ezithandwayo zePython ezifana ne-FastAPI kunye neFlask, okwenza kube lula ukuhambisa iinkqubo ze-RAG njengezicelo zewebhu.

**Ukudibanisa iLangChain kunye neFastAPI kunye neFlask **

Ukudibanisa iLangChain kunye ne-FastAPI kunye neFlask, kufuneka silandele la manyathelo:

Inyathelo loku-1: Faka iLangChain

Okokuqala, kufuneka sifake iLangChain usebenzisa ipip:

pip ufake langchain

Inyathelo lesi-2: Yenza i-Arhente yeLangChain

Okulandelayo, kufuneka senze i-agent ye-LangChain eya kusebenza njengesiseko senkqubo yethu ye-RAG. Nanku umzekelo wendlela yokwenza i-arhente esisiseko:

ukusuka kwi-Agent yokungenisa i-langchain


iarhente = iarhente(
    igama = "ummeli_wam",
    imodeli_igama="imodeli_yam",
    imodeli_uhlobo="umguquleli",
    ubude_ubude=1024,
    min_length=128,
)

Inyathelo lesi-3: Dibanisa ne-FastAPI okanye iFlask

Emva kokuba senze i-agent yethu yeLangChain, sinokuyidibanisa ne-FastAPI okanye iFlask. Nanku umzekelo wendlela yokudibanisa ne-FastAPI:

ukusuka kwi-fastapi yokungenisa i-FastAPI
ukusuka kwi-Agent yokungenisa i-langchain

app = FastAPI ()

# Yenza iarhente esisiseko
iarhente = iarhente(
    igama = "ummeli_wam",
    imodeli_igama = "imodeli_yam",
    imodeli_uhlobo="umguquleli",
    ubude_ubude=1024,
    min_length=128,
)

@app.get("/velisa")
i-async def generate_text(ukukhawuleza: str):
    # Sebenzisa iarhente ukwenza umbhalo
    imveliso = i-agent.generate(prompt)
    buyisela {"imveliso": imveliso}

Ngokufanayo, sinokudibanisa iLangChain kunye neFlask:

kwiFlask yokungenisa, isicelo
ukusuka kwi-Agent yokungenisa i-langchain

app = I-Flask (__igama__)

# Yenza iarhente esisiseko
iarhente = iarhente(
    igama = "ummeli_wam",
    imodeli_igama="imodeli_yam",
    imodeli_uhlobo="umguquleli",
    ubude_ubude=1024,
    min_length=128,
)

@app.route("/velisa", iindlela=["GET"])
def generate_text():
    # Sebenzisa iarhente ukwenza umbhalo
    ngokukhawuleza = request.args.get("i-prompt")
    imveliso = i-agent.generate(prompt)
    buyisela {"imveliso": imveliso}

Izixhobo zeOkhestra kwiLangChain

I-LangChain inikezela uluhlu lwezixhobo ze-orchestration ezenza abaphuhlisi balawule kwaye baqhube iiseti ezinkulu zedatha, kunye nokudibanisa kunye nemithombo eyahlukeneyo yedatha kunye nee-API. Ezinye zezixhobo eziphambili ze-orchestration kwiLangChain ziquka:

1. **Imithombo yeDatha **: I-LangChain inikezela ngoluhlu lwemithombo yedatha eyenza abaphuhlisi bakwazi ukudibanisa kunye nemithombo eyahlukeneyo yedatha kunye nee-API, ezifana nedatha, iinkqubo zefayile, kunye nee-API zewebhu.
2. **AbaSebenzi beDatha **: I-LangChain inikezela ngoluhlu lweeprosesa zedatha eyenza abaphuhlisi baqhube iiseti ezinkulu zedatha, kubandakanywa ukucocwa kwedatha, ukuguqulwa kwedatha, kunye nokudibanisa idatha.
3. Ii-Model Orchestrators: I-LangChain ibonelela ngoluhlu lwee-orchestrar zemodeli eyenza abaphuhlisi balawule kwaye basebenzise iimodeli ze-AI, kubandakanywa noqeqesho lwemodeli, ukuhanjiswa kwemodeli, kunye nokubeka iliso kwimodeli.

Umbono ngeChart.js

Ukujonga ukusebenza kwenkqubo yethu yeRAG, sinokusebenzisa iChart.js, ilayibrari yeJavaScript eyaziwayo ukwenza iitshathi ezisebenzisanayo. Nanku umzekelo wendlela yokusebenzisa iChart.js ukujonga ukusebenza kwenkqubo yethu yeRAG:

const ctx = document.getElementById("myChart").getContext("2d");
const itshati = iTshati entsha (ctx, {
  uhlobo: "umgca",
  idatha: {
    iilebhile: ["Jan", "Feb", "Mar", "Epr", "Meyi"],
    uluhlu lwedatha: [
      {
        ileyibheli: "Ukuchaneka",
        idatha: [0.8, 0.9, 0.85, 0.88, 0.92],
        umbala ongasemva: "rgba(255, 99, 132, 0.2)",
        Umbala womda: "rgba(255, 99, 132, 1)",
        umdaUbubanzi: 1,
      },
    ],
  },
  iinketho: {
    izikali: {
      y: {
        startAtZero: yinyani,
      },
    },
  },
});

Ephezulu 5-10 Eyeyona Isetyenziswayo Imiyalelo/iziqwengana

Nantsi eyona miyalelo/iziqwengana eziphezulu ezi-5-10 eLangChain:

1. ukusuka kwi-Arhente yokungenisa ye-langchain: Ngenisa iklasi ye-Agent esuka kwi-LangChain.
2. iarhente = Iarhente(...): Yenza umzekelo omtsha we-Arhente.
3. iarhente.velisa(...): Sebenzisa i-Arhente ukuvelisa umbhalo.
4. ummeli.umbuzo(...): Sebenzisa i-Arhente ukubuza ngesiseko solwazi.
5. i-arhente.train(...): Qeqesha i-Arhente kwiseti yedatha.
6. iarhente.deploy(...): Sebenzisa i-Arhente njengenkonzo yewebhu.
7. I-arhente.monitha(...): Beka iliso kwintsebenzo ye-Arhente.
8. uhlaziyo lwearhente(...): Hlaziya i-Arhente ngedatha entsha.
9. umenzeli.cima(...): Cima umzekelo we-Arhente.
10. gcina i-arhente(...): Gcina umzekelo weArhente kwifayile.

Isiphelo

Kule nqaku, siye sahlola ihlabathi leLangChain kunye nokudibanisa kwayo kunye nezicwangciso zewebhu ezisekelwe kwiPython ezifana ne-FastAPI kunye neFlask. Siye saxubusha nokusetyenziswa kwezixhobo ze-orchestration kwi-LangChain kwaye sinike imizekelo yendlela yokusebenzisa i-Chart.js ukujonga ukusebenza kwenkqubo yethu ye-RAG. NgeLangChain, abaphuhlisi banokwakha izakhiwo ze-RAG ezinobungakanani kunye nokusebenzayo ezinokusingatha imisebenzi enzima ye-NLP.