Fa'atulagaina o LLM Pipeline tetele ma le DSPy ma le LangChain mo le Fa'aleleia o DevOps | B4N1

Fa'atulagaina o LLM Pipeline tetele ma le DSPy ma le LangChain mo le Fa'aleleia o DevOps

6 months ago · b4n1 Read time: 5 min

** Fa'atulagaina o LLM Pipeline tetele ma le DSPy ma le LangChain mo le Fa'aleleia o DevOps**

Fa'atomuaga

O le oʻo mai o Large Language Models (LLMs) ua faʻafouina le fanua o le Natural Language Processing (NLP) ma le Artificial Intelligence (AI). Peita'i, a'o fa'atupula'ia le lavelave ma le fua o faiga fa'avae LLM, e fa'apena fo'i le mana'omia mo le fa'aola ma le lelei o fa'asologa ma mea faigaluega fa'aautomatika. I totonu o lenei tusiga, o le a tatou suʻesuʻeina le lalolagi o DSPy ma LangChain, lua faʻavae mamana e mafai ai ona fausia le faʻaleleia ma le faʻaogaina o polokalama faakomepiuta i tausaga ole AI.

O le Lu'itau ole LLM Pipeline Lapopo'a

O paipa LLM tetele e aofia ai le tele o vaega, e aofia ai faʻamaumauga muamua, aʻoaʻoga faʻataʻitaʻiga, ma faʻamatalaga. O nei paipa e mafai ona lavelave, ma le tele o faʻalagolago ma fegalegaleaiga i le va o vaega eseese. A'o fa'atupula'ia le fua o nei paipa, e fa'apena fo'i le fa'alavelave o mea sese, tuai, ma fa'aletonu alaoa.

** Ulufale DSPy ma LangChain**

DSPy ma LangChain e lua faʻavae e faʻamoemoe e faʻafaigofie le tuʻufaʻatasia ma le faʻaogaina o paipa LLM tetele. O le DSPy o se faʻavae faʻavae Python e maua ai se auala faigofie ma lelei e fausia ai ma faʻatautaia laina faʻamaufaʻailoga lavelave, ae o LangChain o se faʻavae e mafai ai ona fausia le faʻaogaina ma le faʻaleleia o AI.

DSPy: O se Ta'iala e Fa'avae i le Python mo Fa'amatalaga Pipeline

O le DSPy o se faʻavae faʻavae Python e maua ai se auala faigofie ma lelei e fausia ma faʻatautaia laina faʻamaumauga lavelave. Faatasi ai ma le DSPy, e mafai e le au atinaʻe ona fatuina laina paipa faʻamaumauga e mafai ona faʻaogaina, faʻaleleia, ma faigofie ona tausia.

O se faʻataʻitaʻiga lea o se paipa faʻamatalaga faigofie na fausia i le DSPy:

faaulufale mai le dspy pei o le dsp


data_source = dsp.Source(
    igoa="my_data_source",
    type="csv",
    path="/path/to/data.csv"
)

# Fa'amatala se faila fa'amaumauga
data_processor = dsp.Processor(
    igoa="my_data_processor",
    type="pandas",
    func=lambda x: x.dropna()
)

# Fa'amatala se fa'amaumauga
fa'amau_fa'amau = dsp.Su'u(
    igoa = "my_data_sink",
    type="csv",
    path="/path/to/output.csv"
)

# Fausia se paipa faʻamaumauga
paipa = dsp.Pipeline(
    igoa="my_paipa",
    puna'oa=[data_source],
    processors=[data_processor],
    goto=[fa'amaumauga_gogo]
)

# Tamomoe le paipa
pipeline.run()

I lenei faʻataʻitaʻiga, matou te faʻamalamalamaina se faʻamatalaga faʻamatalaga, se faʻamaumauga faʻamaumauga, ma se faʻamaufaʻailoga faʻamaumauga, ona fai lea o se paipa faʻamaumauga e fesoʻotaʻi ai vaega nei. Ona fa'asolo lea o le paipa e fa'aaoga ai le 'run()' method.

LangChain: Ose Ta'otoga mo Faiga AI Fa'asalaina ma Maufetuua

LangChain o se faʻavae e mafai ai ona fausia le faʻaogaina ma faʻaleleia AI faiga. Faatasi ai ma LangChain, e mafai e le au atinaʻe ona fatuina faiga AI e faigofie ona tausia, faʻaleleia, ma faʻaleleia i le toilalo.

O se faʻataʻitaʻiga lea o se faʻaoga faigofie AI na fausia ma LangChain:

faaulufale mai le langchain pei lc

# Fa'amatala se fa'ata'ita'iga
fa'ata'ita'iga = lc.Model(
    igoa="my_model",
    type="transformer",
    ala = "/path/to/model"
)

# Faʻamatala se faʻamatalaga
vave = lc. Fa'anatinati(
    igoa="my_prompt",
    text="O se fa'ata'ita'iga lea"
)

# Fausia se faiga AI
ai_system = lc.AISystem(
    igoa="my_ai_system",
    fa'ata'ita'iga=fa'ata'ita'iga,
    vave = vave
)

# Faʻatautaia le polokalama AI
ai_system.run()

I lenei faʻataʻitaʻiga, matou te faʻamatalaina se faʻataʻitaʻiga, se faʻavavega, ona faia lea o se faiga AI e faʻafesoʻotaʻi nei vaega. Ole faiga ole AI ole fa'aogaina lea ole auala run().

** Fa'atulagaina o Paipa LLM Tele ma le DSPy ma le LangChain**

Ina ia faʻatulagaina paipa LLM tetele ma DSPy ma LangChain, e mafai ona matou faʻaogaina le auala lenei:

  1. Fa'amatala se paipa fa'amaumauga e fa'aaoga ai le DSPy e fa'afeso'ota'i ai le tele o fa'amaumauga, fa'agasolo, ma goto.
  2. Fa'amatala se faiga AI e fa'aaoga ai le LangChain e fa'afeso'ota'i le tele o fa'ata'ita'iga, fa'aoso, ma gaioiga.
  3. Faʻaaoga le DSPy e faʻatautaia ai le paipa faʻamaumauga ma LangChain e faʻatautaia ai le polokalama AI.
  4. Fa'aaoga se fa'asologa e pei o Apache Airflow po'o Kubernetes e fa'atulaga ai le fa'atinoina o le fa'asologa o fa'amaumauga ma le faiga AI.

O se faʻataʻitaʻiga lea o se faʻasologa faʻasologa faigofie e faʻaaoga DSPy ma LangChain:

faaulufale mai le dspy pei o le dsp
faaulufale mai le langchain pei lc
faaulufale mai apache_airflow pei af

# Fa'amatala se paipa fa'amaumauga
data_paipa = dsp.Pipeline(
    igoa="my_data_pipeline",
    puna = [dsp.Source(igoa = "my_data_source", type = "csv", path = "/path/to/data.csv")],
    processors=[dsp.Processor(igoa="my_data_processor", type="pandas", func=lambda x: x.dropna())],
    goto=[dsp.Sink(igoa="my_data_sink", type="csv", path="/path/to/output.csv")]
)

# Fa'amatala se faiga AI
ai_system = lc.AISystem(
    igoa="my_ai_system",
    model=lc.Model(igoa="my_model", type="transformer", path="/path/to/model"),
    prompt=lc.Prompt(name="my_prompt", text="O se fa'ata'ita'iga lea")
)

# Faʻatonu le faʻatinoina o le faʻasologa o faʻamaumauga ma le AI system
af.schedule_task(
    task_id="my_task",
    dag_id="my_dag",
    paipa=data_paipa,
    faiga=ai_system
)

I lenei faʻataʻitaʻiga, matou te faʻamatalaina se paipa faʻamaumauga ma se AI faʻaoga e faʻaaoga ai DSPy ma LangChain, ona faʻatulagaina lea o le faʻatinoina o nei vaega e faʻaaoga ai le Apache Airflow.

Poloaiga/Snippets pito i luga 5-10 Fa'aaogaina

O le pito i luga 5-10 pito sili ona faʻaaogaina poloaiga / snippets mo DSPy ma LangChain:

  1. dsp.Pipeline(igoa="my_pipeline", sources=[source], processors=[processor], gots=[gogo])
  2. lc.Model(igoa="my_model", type="transformer", path="/path/to/model")
  3. lc.Prompt(name="my_prompt", text="O se fa'ata'ita'iga lenei")
  4. dsp.Source(igoa="my_data_source", type="csv", path="/path/to/data.csv")
  5. dsp.Processor(igoa="my_data_processor", type="pandas", func=lambda x: x.dropna())
  6. dsp.Sink(igoa="my_data_sink", type="csv", path="/path/to/output.csv")
  7. lc.AISystem(igoa="my_ai_system", model=model, prompt=prompt)
  8. af.schedule_task(task_id="my_task", dag_id="my_dag", pipeline=pipeline, system=system)
  9. dsp.run_pipeline(paipa)
  10. lc.run_system(system)

Va'aiga

Ole fa'aaliga lea ole DSPy ma LangChain fa'aoga fa'aoga Chart.js:

{
  "type": "pa",
  "fa'amatalaga": {
    "labels": ["DSPy", "LangChain"],
    "fa'amaumauga": [
      {
        "label": "Vaega",
        "faamatalaga": [10, 15],
        "Lulu lanu": [
          "rgba(255, 99, 132, 0.2)",
          "rgba(54, 162, 235, 0.2)"
        ],
        "Lulu tuaoi": [
          "rgba(255, 99, 132, 1)",
          "rgba(54, 162, 235, 1)"
        ],
        "BorderWidth": 1
      }
    ]
  },
  "filifiliga": {}
}

O lenei faʻaaliga o loʻo faʻaalia ai le numera o foliga ile DSPy ma LangChain.

Fa'ai'uga

DSPy ma LangChain o ni fa'avae mamana se lua e mafai ai ona fausia le fa'aogaina o polokalama fa'akomepiuta ma fa'aleleia i tausaga ole AI. I le fa'aogaina o nei fa'ava'a, e mafai ai e le au atina'e ona fausia ni paipa LLM tetele e faigofie ona fa'atumauina, fa'asalaina, ma fa'amautu i fa'aletonu. Faatasi ai ma le fesoasoani a le faʻapipiʻiina ma meafaigaluega faʻaautomatika e pei o Apache Airflow ma Kubernetes, e mafai e le au atinaʻe ona faʻatulagaina le faʻatinoina o nei paipa ma faiga, faʻafaigofie ona faʻatautaia faiga faʻalavelave AI.

Galue i le Lumanai

O galuega i le lumana'i e aofia ai:

  1. Atinaʻeina atili faʻapitoa mo DSPy ma LangChain.
  2. Faʻaleleia le faʻatinoga ma le faʻalauteleina o DSPy ma LangChain.
  3. Atinaʻeina atili faʻapipiʻi ma masini masini mo DSPy ma LangChain.
  4. Fausia atili faʻaaliga ma faʻailoga mo DSPy ma LangChain.

Fa'asinomaga

  1. DSPy fa'amaumauga: https://dspy.readthedocs.io/en/latest/
  2. Fa'amaumauga LangChain: https://langchain.readthedocs.io/en/latest/
  3. Apache Airflow pepa: https://airflow.apache.org/docs/apache-airflow/stable/index.html
  4. Fa'amaumauga Kubernetes: https://kubernetes.io/docs/home/

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