Kubatanidza LangChain yeAdvanced RAG Architectures muPython Applications

Pfupiso

Mumakore achangopfuura, munda weNatural Language Processing (NLP) wakaona kufambira mberi kwakakosha, kunyanya munzvimbo yeRetrieval-Augmented Generation (RAG) masisitimu. Aya masisitimu akachinja maitiro atinoita mabasa akadai sekupindura mibvunzo, kupfupisa zvinyorwa, uye yekukurukurirana AI. LangChain, yekucheka-kumucheto Python raibhurari, yakabuda sechishandiso chine simba chekuvaka RAG zvivakwa. Muchikamu chino, tichaongorora munyika yeLangChain uye tiongorore kubatanidzwa kwayo nePython-based web frameworks seFastAPI neFlask, tichishandisa maturusi ekuronga kuti awedzere kudzosa uye kugadzirisa.

Chii chinonzi LangChain?

LangChain iraibhurari yakavhurika-sosi yePython yakagadzirirwa kurerutsa kuvandudzwa kweRAG masisitimu. Inopa seti yakazara yezvishandiso uye maAPIs ekuvaka, kudzidzisa, uye kuendesa AI modhi, pamwe nekubatanidza iwo akasiyana siyana data masosi uye webhu masisitimu. Chakanyanya kutariswa naLangChain ndechekugonesa vagadziri kugadzira scalable uye inoshanda RAG zvivakwa zvinogona kubata yakaoma NLP mabasa.

**Zvinhu zvakakosha zveLangChain **

1. ** Modular Architecture **: LangChain's modular design inobvumira vagadziri kuti vabatanidze nyore nyore mhando dzakasiyana dzeAI, zvinyorwa zvemashoko, uye web frameworks, zvichiita kuti ive sarudzo yakanaka yekuvaka maitiro eRAG akaoma.
2. ** Orchestration Tools **: LangChain inopa huwandu hwezvishandiso zvekugadzira izvo zvinoita kuti vagadziri vagadzirise uye vagadzirise datasets yakakura, pamwe nekubatanidza nemhando dzakasiyana dze data uye APIs.
3. ** Rutsigiro rweMienzaniso yakawanda yeAI **: LangChain inotsigira mhando dzakasiyana dzeAI, kusanganisira transformer-based models, zvichiita kuti vagadziri vasarudze muenzaniso wakanakisisa wenyaya yavo chaiyo yekushandisa.
4. ** Kubatanidzwa neWeb Frameworks **: LangChain inopa kusanganiswa kusinganzwisisiki nePython-based web frameworks yakakurumbira yakafanana neFastAPI neFlask, zvichiita kuti zvive nyore kuisa zvirongwa zveRAG semashandisirwo ewebhu.

Kubatanidza LangChain ne FastAPI uye Flask

Kuti ubatanidze LangChain ne FastAPI uye Flask, isu tinofanirwa kutevedzera aya matanho:

Danho 1: Isa LangChain

Kutanga, tinoda kuisa LangChain tichishandisa pip:

pip kuisa langchain

Danho 2: Gadzira LangChain Agent

Tevere, isu tinofanirwa kugadzira LangChain mumiriri anozoshanda semusimboti weRAG system yedu. Heino muenzaniso wekugadzira mumiriri wekutanga:

kubva ku langchain import Agent


mumiririri = mumiririri (
    zita = "mumiririri_wangu",
    muenzaniso_zita = "my_model",
    mhando_rudzi = "transformer",
    max_length=1024,
    min_length=128,
)

Danho rechitatu: Batanidza neFastAPI kana Flask

Kana tangogadzira mumiririri wedu weLangChain, tinogona kuibatanidza ne FastAPI kana Flask. Heino muenzaniso wekuti ungaibatanidza sei ne FastAPI:

kubva fastapi pinza FastAPI
kubva ku langchain import Agent

app = FastAPI ()

# Gadzira yakakosha mumiriri
mumiririri = mumiririri (
    zita = "mumiririri_wangu",
    muenzaniso_zita = "my_model",
    mhando_rudzi = "transformer",
    max_length=1024,
    min_length=128,
)

@app.get("/generate")
async def generate_text(kukurumidza: str):
    # Shandisa mumiririri kugadzira zvinyorwa
    kubuda = agent.generate(prompt)
    return {"output": output}

Saizvozvo, isu tinogona kubatanidza LangChain neFlask:

kubva kune flask import Flask, chikumbiro
kubva ku langchain import Agent

app = Flask(__zita__)

# Gadzira yakakosha mumiriri
mumiririri = mumiririri (
    zita = "mumiririri_wangu",
    muenzaniso_zita = "my_model",
    mhando_rudzi = "transformer",
    max_length=1024,
    min_length=128,
)

@app.route("/generate", nzira=["GET"])
def generate_text():
    # Shandisa mumiririri kugadzira zvinyorwa
    prompt = request.args.get("prompt")
    kubuda = agent.generate(prompt)
    return {"output": output}

Zvishandiso zveOrchestration muLangChain

LangChain inopa huwandu hwezvishandiso zvekuronga zvinoita kuti vagadziri vagone kubata uye kugadzirisa makuru dataset, pamwe nekubatanidza neakasiyana dhata zvinyorwa uye APIs. Mamwe eakakosha ekushandisa orchestration muLangChain anosanganisira:

1. ** Data Source **: LangChain inopa huwandu hwezvinyorwa zvemashoko zvinoita kuti vagadziri vabatanidze nemhando dzakasiyana-siyana dzemashoko uye maAPIs, akadai sedhetabhesi, mafaira mafaira, uye web APIs.
2. ** Data processors **: LangChain inopa huwandu hwehuwandu hwemashoko hunoita kuti vagadziri vagadzirise madheti makuru, kusanganisira kucheneswa kwedata, kushandura data, uye kuunganidza data.
3. ** Model Orchestrators **: LangChain inopa mhando yemhando ye orchestrators inoita kuti vagadziri vatarise uye vatumire ma AI mamodheru, kusanganisira kudzidziswa kwemuenzaniso, kuburitsa kwemhando, uye kuongorora muenzaniso.

Kuona neChati.js

Kuona kuita kweRAG system yedu, tinogona kushandisa Chart.js, raibhurari yakakurumbira yeJavaScript kugadzira machati anodyidzana. Heino muenzaniso wekushandisa Chart.js kuona mabatiro eRAG system yedu:

const ctx = document.getElementById("myChart").getContext("2d");
const chati = Chati itsva (ctx, {
  mhando: "mutsara",
  data: {
    mavara: ["Jan", "Kukadzi", "Kurume", "Kubvumbi", "Chivabvu"],
    datasets: [
      {
        label: "Kururama",
        data: [0.8, 0.9, 0.85, 0.88, 0.92],
        backgroundRuvara: "rgba(255, 99, 132, 0.2)",
        borderColor: "rgba(255, 99, 132, 1)",
        borderWidth: 1,
      },
    ],
  },
  sarudzo: {
    zviyero: {
      y:{
        startAtZero: chokwadi,
      },
    },
  },
});

Pamusoro 5-10 Inonyanya Kushandiswa Mirairo/Zvidimbu

Heano epamusoro 5-10 anonyanya kushandiswa mirairo / zvimedu muLangChain:

1. kubva ku langchain import Agent: Pinza kirasi yeAgent kubva kuLangChain.
2. mumiririri = Mumiririri(...): Gadzira chiitiko chitsva cheMumiririri.
3. agent.generate(...): Shandisa Mumiririri kugadzira zvinyorwa.
4. agent.query(...): Shandisa Mumiririri kubvunza hwaro hweruzivo.
5. agent.train(...): Dzidzisa Mumiririri pane dataset.
6. agent.deploy(...): Shandisa Mumiririri sebasa rewebhu.
7. agent.monitor(...): Tarisa mashandiro eAgent.
8. agent.update(...): Gadzirisa Mumiririri nedata idzva.
9. agent.delete(...): Delete the Agent muenzaniso.
10. agent.save(...): Sevha chiitiko cheAgent kufaira.

Mhedziso

Muchikamu chino, takaongorora nyika yeLangChain uye kubatanidzwa kwayo nePython-based web frameworks se FastAPI uye Flask. Takakurukurawo kushandiswa kwezvishandiso zvekuimba muLangChain uye takapa mienzaniso yekushandisa Chart.js kuti tione kushanda kweRAG system yedu. NeLangChain, vagadziri vanogona kuvaka scalable uye inoshanda RAG zvivakwa zvinogona kubata yakaoma NLP mabasa.