Kev koom ua ke LangChain rau Advanced RAG Architectures hauv Python Applications | B4N1

Kev koom ua ke LangChain rau Advanced RAG Architectures hauv Python Applications

8 months ago · b4n1 Read time: 6 min

Kev koom ua ke LangChain rau Advanced RAG Architectures hauv Python Applications

Txheej txheem cej luam

Nyob rau hauv xyoo tas los no, lub teb ntawm Natural Language Processing (NLP) tau pom cov kev nce qib tseem ceeb, tshwj xeeb tshaj yog nyob rau hauv thaj tsam ntawm Retrieval-Augmented Generation (RAG) systems. Cov tshuab no tau hloov pauv txoj hauv kev uas peb mus txog cov haujlwm xws li teb cov lus nug, sau ntawv sau, thiab kev sib tham AI. LangChain, lub tsev qiv ntawv Python tshiab, tau tshwm sim los ua lub cuab yeej muaj zog rau kev tsim RAG architectures. Hauv tsab xov xwm no, peb yuav delve rau hauv lub ntiaj teb ntawm LangChain thiab tshawb nrhiav nws kev koom ua ke nrog Python-raws li lub vev xaib xws li FastAPI thiab Flask, thaum siv cov cuab yeej orchestration rau kev txhim kho cov ntaub ntawv rov qab thiab ua tiav.

LangChain yog dab tsi?

LangChain yog lub tsev qiv ntawv qhib Python tsim los ua kom yooj yim rau kev txhim kho ntawm RAG systems. Nws muab cov txheej txheem dav dav ntawm cov cuab yeej thiab APIs rau kev tsim, kev cob qhia, thiab kev siv AI qauv, nrog rau kev sib koom ua ke nrog ntau yam ntaub ntawv thiab lub vev xaib. LangChain lub hom phiaj tseem ceeb yog ua kom cov neeg tsim khoom tsim kom muaj peev xwm thiab ua tau zoo RAG architectures uas tuaj yeem ua haujlwm nyuaj NLP.

Qhov tseem ceeb ntawm LangChain

  1. Modular Architecture: LangChain's modular design tso cai rau cov neeg tsim khoom kom yooj yim sib xyaw cov qauv AI sib txawv, cov ntaub ntawv, thiab lub vev xaib ua haujlwm, ua rau nws yog qhov kev xaiv zoo tshaj plaws rau kev tsim cov RAG complex systems.
  2. Cov cuab yeej Orchestration: LangChain muab ntau yam cuab yeej orchestration uas ua rau cov neeg tsim khoom tswj hwm thiab ua cov ntaub ntawv loj, nrog rau kev koom ua ke nrog ntau yam ntaub ntawv thiab APIs.
  3. Txhawb rau Ntau Tus Qauv AI: LangChain txhawb nqa ntau yam ntawm AI qauv, suav nrog cov qauv hloov pauv, tso cai rau cov neeg tsim khoom xaiv cov qauv zoo tshaj plaws rau lawv cov ntaub ntawv siv tshwj xeeb.
  4. Kev koom ua ke nrog Web Frameworks: LangChain muab kev sib txuas ua ke nrog cov Python-raws li lub vev xaib nrov xws li FastAPI thiab Flask, ua rau nws yooj yim rau kev xa cov tshuab RAG ua lub vev xaib.

Kev koom ua ke LangChain nrog FastAPI thiab Flask

Txhawm rau koom nrog LangChain nrog FastAPI thiab Flask, peb yuav tsum ua raws li cov kauj ruam no:

Kauj Ruam 1: Nruab LangChain

Ua ntej, peb yuav tsum nruab LangChain siv pip:

``bash pip nruab langchain


### Kauj Ruam 2: Tsim tus neeg sawv cev LangChain

Tom ntej no, peb yuav tsum tau tsim ib tus neeg sawv cev LangChain uas yuav ua haujlwm tseem ceeb ntawm peb lub RAG system. Nov yog ib qho piv txwv ntawm yuav ua li cas los tsim ib tus neeg sawv cev yooj yim:

`` python
los ntawm langchain import Agent


agent = Agent(
    npe="my_agent",
    model_name="my_model",
    model_type="transformer",
    max_ntev = 1024,
    min_ntev = 128,
)

Kauj Ruam 3: Ua ke nrog FastAPI lossis Flask

Thaum peb tau tsim peb tus neeg sawv cev LangChain, peb tuaj yeem koom ua ke nrog FastAPI lossis Flask. Nov yog ib qho piv txwv ntawm yuav ua li cas koom ua ke nrog FastAPI:

`` python los ntawm fastapi import FastAPI los ntawm langchain import Agent

app = FastAPI()

Tsim tus neeg sawv cev yooj yim

agent = Agent( npe="my_agent", model_name="my_model", model_type="transformer", max_ntev = 1024, min_ntev = 128, )

@app.get("/ tsim") async def generate_text(qhia: str): # Siv tus neeg sawv cev los tsim cov ntawv tso zis = agent.generate(prompt) rov qab {"output": output}


Ib yam li ntawd, peb tuaj yeem koom ua ke LangChain nrog Flask:

`` python
los ntawm lub raj mis import Flask, thov
los ntawm langchain import Agent

app = Flask(__name__)

# Tsim tus neeg sawv cev yooj yim
agent = Agent(
    npe="my_agent",
    model_name="my_model",
    model_type="transformer",
    max_ntev = 1024,
    min_ntev = 128,
)

@app.route("/generate", method=["GET"])
def generate_text():
    # Siv tus neeg sawv cev los tsim cov ntawv
    prompt = thov.args.get("prompt")
    tso zis = agent.generate(prompt)
    rov qab {"output": output}

Cov cuab yeej Orchestration hauv LangChain

LangChain muab ntau yam cuab yeej orchestration uas ua rau cov neeg tsim khoom tswj hwm thiab ua cov ntaub ntawv loj, nrog rau kev koom ua ke nrog ntau yam ntaub ntawv thiab APIs. Qee qhov tseem ceeb orchestration cuab yeej hauv LangChain suav nrog:

  1. Data Sources: LangChain muab cov ntaub ntawv ntau yam uas ua rau cov neeg tsim khoom koom ua ke nrog ntau yam ntaub ntawv thiab APIs, xws li databases, cov ntaub ntawv kaw lus, thiab web APIs.
  2. Cov Txheej Txheem Cov Ntaub Ntawv: LangChain muab ntau cov ntaub ntawv processors uas ua rau cov neeg tsim khoom ua cov ntaub ntawv loj, suav nrog kev tu cov ntaub ntawv, hloov cov ntaub ntawv, thiab cov ntaub ntawv sib sau.
  3. Model Orchestrators: LangChain muab ntau yam qauv orchestrators uas ua rau cov neeg tsim khoom tswj hwm thiab xa cov qauv AI, suav nrog kev cob qhia qauv, kev xa tawm qauv, thiab kev saib xyuas tus qauv.

Visualization nrog Chart.js

Txhawm rau pom qhov ua tau zoo ntawm peb lub RAG system, peb tuaj yeem siv Chart.js, lub tsev qiv ntawv JavaScript nrov rau tsim cov kab kos sib tham. Nov yog ib qho piv txwv ntawm kev siv Chart.js kom pom qhov ua tau zoo ntawm peb RAG system:

``javascript const ctx = document.getElementById("myChart").getContext("2d"); const chart = new Chart(ctx, { hom: "line", cov ntaub ntawv: { Tags: ["Jan", "Feb", "Mar", "Apr", "May"], cov ntaub ntawv: [ { Label: "Qhov tseeb", cov ntaub ntawv: [0.8, 0.9, 0.85, 0.88, 0.92], backgroundColor: "rgba(255, 99, 132, 0.2)", borderColor: "rgba(255, 99, 132, 1)", ciam teb dav: 1, }, ], }, kev xaiv: { nplai: { y: { PibAtZero: tseeb, }, }, }, });


** Sab saum toj 5-10 Feem Ntau Siv Cov Lus Qhia/Snippet **


Nov yog cov saum toj kawg nkaus 5-10 feem ntau siv cov lus txib / snippets hauv LangChain:

1. "los ntawm langchain ntshuam tus neeg sawv cev": Ntshuam cov chav kawm tus neeg sawv cev los ntawm LangChain.
2. `Agent = Agent(...)`: Tsim ib tus neeg sawv cev tshiab.
3. `agent.generate(...)`: Siv tus neeg sawv cev los tsim cov ntawv.
4. `agent.query(...)`: Siv tus neeg sawv cev los nug txog kev paub.
5. `agent.train(...)`: Qhia tus neeg saib xyuas ntawm cov ntaub ntawv.
6. `agent.deploy(...)`: Deploy the Agent as a web service.
7. `agent.monitor(...)`: Saib xyuas kev ua haujlwm ntawm Tus Neeg Saib Xyuas.
8. `agent.update(...)`: Hloov kho tus neeg sawv cev nrog cov ntaub ntawv tshiab.
9. `agent.delete(...)`: Rho tawm tus neeg sawv cev piv txwv.
10. `agent.save(...)`: Txuag Agent piv txwv rau ib cov ntaub ntawv.

**Qhov xaus**


Hauv tsab xov xwm no, peb tau tshawb xyuas lub ntiaj teb ntawm LangChain thiab nws kev koom ua ke nrog Python-raws li lub vev xaib xws li FastAPI thiab Flask. Peb kuj tau tham txog kev siv cov cuab yeej orchestration hauv LangChain thiab muab cov piv txwv ntawm kev siv Chart.js kom pom qhov kev ua tau zoo ntawm peb lub RAG system. Nrog LangChain, cov neeg tsim khoom tuaj yeem tsim kom muaj peev xwm loj thiab muaj txiaj ntsig RAG architectures uas tuaj yeem ua haujlwm nyuaj NLP.

Have Questions?

Let's Talk

Contact me on LinkedIn