Ho hlophisa Liphaephe tse kholo tsa LLM tse nang le DSPy le LangChain bakeng sa li-DevOps tse Ntlafetseng.

**Ho Hlophisa Liphaephe tsa LLM tse Khōlō tse nang le DSPy le LangChain bakeng sa Li-DevOps tse Ntlafetseng **

Selelekela

Ho fihla ha Mehlala e Meholo ea Lipuo (LLMs) ho entse phetoho e kholo lefapheng la Natural Language Processing (NLP) le Artificial Intelligence (AI). Leha ho le joalo, ha ho rarahana le boholo ba litsamaiso tse thehiloeng ho LLM li ntse li eketseha, ho joalo le ka tlhokahalo ea lisebelisoa tse matla le tse sebetsang tsa 'mino le li-automation. Sehloohong sena, re tla hlahloba lefats'e la DSPy le LangChain, mekhoa e 'meli e matla e nolofalletsang ho haha litsamaiso tsa software tse tsitsitseng le tse senyehang mehleng ea AI.

Phephetso ea Liphaephe tse Kholo tsa LLM

Liphaephe tse kholo tsa LLM li kenyelletsa likarolo tse ngata, ho kenyeletsoa tlhahlobo ea data pele, koetliso ea mohlala le boithuto. Liphaephe tsena li ka ba tse rarahaneng, tse itšetlehileng ka bongata le likamano pakeng tsa likarolo tse fapaneng. Ha tekanyo ea lipeipi tsena e ntse e eketseha, le kotsi ea liphoso, tieho, le mathata a lisebelisoa a eketseha.

kena DSPy le LangChain

DSPy le LangChain ke meralo e 'meli e ikemiselitseng ho nolofatsa orchestration le automation ea liphaephe tse kholo tsa LLM. DSPy ke moralo o thehiloeng ho Python o fanang ka mokhoa o bonolo le o sebetsang oa ho haha le ho laola liphaephe tse rarahaneng tsa data, ha LangChain e le moralo o nolofalletsang ho hahoa ha mekhoa ea AI e tsitsitseng le e tsitsitseng.

DSPy: Moralo o Thehiloeng ka Python bakeng sa Liphaephe tsa Boitsebiso

DSPy ke moralo o thehiloeng ho Python o fanang ka mokhoa o bonolo le o sebetsang oa ho haha le ho laola liphaephe tse rarahaneng tsa data. Ka DSPy, bahlahisi ba ka etsa liphaephe tsa data tse scalable, tse tsitsitseng, le tse bonolo ho li hlokomela.

Mohlala ke ona oa phaephe e bonolo ea data e hahiloeng ka DSPy:

kenya dspy joalo ka dsp


data_source = dsp.Mohloli(
    lebitso = "my_data_source",
    mofuta = "csv",
    tsela="/path/to/data.csv"
)

# Hlalosa processor ea data
data_processor = dsp.Processor(
    lebitso = "my_data_processor",
    mofuta = "panda",
    func=lambda x: x.dropna()
)

# Hlalosa sinki ea data
data_sink = dsp.Sink(
    lebitso="senki_ya_ya_data",
    mofuta = "csv",
    tsela="/path/to/output.csv"
)

# Theha pompo ea data
pipeline = dsp.Pipeline(
    lebitso = "pipeline" eaka",
    mehloli=[data_source],
    processors=[data_processor],
    ho teba=[semela_tsa_boitsebiso]
)

# Tsamaisa lipeipi
pipeline.run()

Mohlaleng ona, re hlalosa mohloli oa data, processor ea data, le sink ea data, ebe re theha phaephe ea data e hokahanyang likarolo tsena. Pepeline e sebetsoa ka mokhoa oa run().

**LangChain: Moralo oa Litsamaiso tsa AI tse Scalable le tse Resilient **

LangChain ke moralo o nolofalletsang ho aha mekhoa ea AI e tsitsitseng le e tsitsitseng. Ka LangChain, bahlahisi ba ka etsa litsamaiso tsa AI tseo ho leng bonolo ho li boloka, tse senyehang, le tse mamellang mefokolo.

Mona ke mohlala oa sistimi e bonolo ea AI e hahiloeng ka LangChain:

kenya langchain joalo ka lc

# Hlalosa mohlala
mohlala = lc.Mohlala(
    lebitso = "mohlala_waka",
    mofuta = "transformer",
    tsela="/tsela/ho/mohlala"
)

# Hlalosa molaetsa
kapele = lc.Prompt(
    lebitso = "metso_ya_ka",
    text="Ona ke mohlala oa molaetsa"
)

# Theha sistimi ea AI
ai_system = lc.AISystem(
    lebitso = "my_ai_system",
    mohlala=mohlala,
    phakisa=potlakisa
)

# Tsamaisa sistimi ea AI
ai_system.run()

Mohlala ona, re hlalosa mohlala, ho potlakisa, ebe re theha tsamaiso ea AI e kopanyang likarolo tsena. Tsamaiso ea AI e tsamaisoa ho sebelisoa run() mokhoa.

Ho hlophisa liphaephe tse kholo tsa LLM tse nang le DSPy le LangChain

Ho hlophisa liphaephe tse kholo tsa LLM ka DSPy le LangChain, re ka sebelisa mokhoa o latelang:

Hlalosa phaephe ea data e sebelisang DSPy e hokahanyang mehloli e mengata ea data, li-processor, le lisinki.
Hlalosa mokhoa oa AI o sebelisang LangChain o kopanyang mefuta e mengata, litlhahiso, le lihlahisoa.
Sebelisa DSPy ho laola pipeline ea data le LangChain ho laola tsamaiso ea AI.
Sebelisa kemiso e kang Apache Airflow kapa Kubernetes ho hlophisa ts'ebetso ea pipeline ea data le tsamaiso ea AI.

Mona ke mohlala oa mongolo o bonolo oa 'mino o sebelisang DSPy le LangChain:

kenya dspy joalo ka dsp
kenya langchain joalo ka lc
kenya apache_airflow joalo ka af

# Hlalosa pompo ea data
data_pipeline = dsp.Pipeline(
    lebitso = "my_data_pipeline",
    mehloli =[dsp.Mohloli(lebitso = "my_data_source", mofuta = "csv", tsela="/path/to/data.csv")],
    processors=[dsp.Processor(lebitso="my_data_processor", type="pandas", func=lambda x: x.dropna())],
    sink=[dsp.Sink(lebitso = "my_data_sink", mofuta = "csv", tsela="/path/to/output.csv")]
)

# Hlalosa sistimi ea AI
ai_system = lc.AISystem(
    lebitso = "my_ai_system",
    mohlala=lc.Moetso(lebitso = "my_model", mofuta = "transformer", tsela="/tsela/ho/mohlala"),
    prompt=lc.Prompt(lebitso="my_prompt", text="Ona ke mohlala oa tlhahiso").
)

# Etsa moralo oa ts'ebetso ea lipeipi tsa data le sistimi ea AI
af.schedule_task(
    task_id="my_task",
    dag_id="my_dag",
    pipeline=data_pipeline,
    tsamaiso=ai_system
)

Mohlala ona, re hlalosa pipeline ea data le tsamaiso ea AI e sebelisang DSPy le LangChain, ebe re hlophisa ho etsoa ha likarolo tsena ho sebelisa Apache Airflow.

Top 5-10 Litaelo/Lipotlana tse Sebelisitsoeng ka ho Fetisisa

Mona ke tse 5-10 tse sebelisoang ka ho fetesisa litaelo/likotoana tsa DSPy le LangChain:

dsp.Pipeline(lebitso="my_pipeline", mehloli=[mohloli], processors=[processor], sinks=[sinki])
lc.Moetso(lebitso = "my_model", mofuta = "transformer", tsela="/tsela/ho/mohlala")
lc.Prompt(lebitso="my_prompt", text="Ona ke mohlala oa tlhahiso")
dsp.Mohloli(lebitso="my_data_source", type="csv", path="/path/to/data.csv")
dsp.Processor(lebitso="my_data_processor", type="pandas", func=lambda x: x.dropna())
dsp.Sink(lebitso="my_data_sink", type="csv", path="/path/to/output.csv")
lc.AISystem(lebitso="my_ai_system", model=model, prompt=prompt)
af.schedule_task(task_id="my_task", dag_id="my_dag", pipeline=pipeline, system=system)
dsp.run_pipeline(pipeline)
lc.run_system(system)

Pono ya pono

Mona ke pono ea meralo ea DSPy le LangChain e sebelisang Chart.js:

{
  "type": "bar",
  "data": {
    "labels": ["DSPy", "LangChain"],
    "lintlha tsa boitsebiso": [
      {
        "label": "Likarolo",
        "data": [10, 15],
        "backgroundColour": [
          "rgba(255, 99, 132, 0.2)",
          "rgba(54, 162, 235, 0.2)"
        ],
        "Mmala oa moeli": [
          "rgba(255, 99, 132, 1)",
          "rgba(54, 162, 235, 1)"
        ],
        "borderWidth": 1
      }
    ]
  },
  "likhetho": {}
}

Pontšo ena e bonts'a palo ea likarolo ho DSPy le LangChain.

Qetello

Qetellong, DSPy le LangChain ke mekhoa e 'meli e matla e etsang hore ho hahoe mekhoa ea software e tsitsitseng le e senyehang nakong ea AI. Ka ho sebelisa meralo ena, bahlahisi ba ka etsa liphaephe tse kholo tsa LLM tseo ho leng bonolo ho li hlokomela, tse ka senyehang, le tse mamellang liphoso. Ka thuso ea lisebelisoa tsa 'mino le li-automation tse kang Apache Airflow le Kubernetes, bahlahisi ba ka hlophisa ts'ebetso ea liphaephe tsena le litsamaiso, ho etsa hore ho be bonolo ho laola mekhoa e rarahaneng ea AI.

Mosebetsi o Tlang

Mosebetsi oa nakong e tlang o kenyelletsa:

Ho hlahisa likarolo tse ling tsa DSPy le LangChain.
Ho ntlafatsa ts'ebetso le scalability ea DSPy le LangChain.
Ho nts'etsapele lisebelisoa tse ngata tsa 'mino oa' mino oa 'mino le li-automation bakeng sa DSPy le LangChain.
Ho etsa lipono le li-dashboards tse ngata bakeng sa DSPy le LangChain.

Litšupiso

Litokomane tsa DSPy: https://dspy.readthedocs.io/en/latest/
Litokomane tsa LangChain: https://langchain.readthedocs.io/en/latest/
Litokomane tsa Apache Airflow: https://airflow.apache.org/docs/apache-airflow/stable/index.html
Litokomane tsa Kubernetes: https://kubernetes.io/docs/home/

Ho hlophisa Liphaephe tse kholo tsa LLM tse nang le DSPy le LangChain bakeng sa li-DevOps tse Ntlafetseng.

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