Le tatalaina o le Geothermal Potential with AI-Driven Software Engineering and Orchestration | B4N1

Le tatalaina o le Geothermal Potential with AI-Driven Software Engineering and Orchestration

7 months ago · b4n1 Tiempo de lectura: 4 min

Tatalaina le Geothermal Potential with AI-Driven Software Engineering and Orchestration

Ole malosi ole Geothermal ose puna mama, fa'afouina, ma fa'atuatuaina o le malosi e mafai ona fesoasoani tele i le fa'aitiitia o le kasa oona ma fa'aitiitia ai suiga o le tau. Ae ui i lea, o le faʻaogaina o le geothermal e manaʻomia ai tekinolosi faʻapitoa ma faiga faʻalavelave e aveese ai le vevela mai totonu ole lalolagi. I totonu o lenei tusiga, o le a tatou suʻesuʻeina pe faʻafefea ona tatalaina e le AI-driven software engineering ma le tuʻufaʻatasiga le gafatia o le geothermal e ala i le faʻaogaina o AI biological lautele ma faʻataʻitaʻiga gagana tetele (LLMs) mo le faʻataʻitaʻiga faʻataʻitaʻiga ma le faʻataʻitaʻiina o faiga o le malosi o le geothermal.

O le Lu'itau ole Geothermal Energy System

Geothermal malosi faiga e lavelave ma malosi, e aofia ai le tele o fesuiaiga e pei o le vevela, mamafa, tafe, ma vailaʻau. O nei fesuiaiga e fegalegaleai ai le tasi ma le isi i auala e le o laina laina, ma avea ma luʻitau le vaʻai ma faʻamalieina le faʻatinoga o faiga. O metotia faʻaleaganuʻu e faʻalagolago i faʻataʻitaʻiga faʻapitoa ma faiga faʻavae tulafono, lea e masani ona faʻatapulaʻaina i lo latou gafatia e puʻeina le lavelave o faiga geothermal.

Talosagaina Generalist Biological AI ma LLMs

Generalist biological AI ma LLMs e ofoina atu se auala faʻamoemoe i le faʻataʻitaʻiina o faʻataʻitaʻiga ma le faʻaleleia atili o faiga faʻaogaina o le geothermal. O nei faʻataʻitaʻiga AI e mafai ona aʻoaʻoina mai faʻamaumauga tetele ma faʻamaonia mamanu ma mafutaga e le o faʻaalia e ala i auala masani. E ala i le faʻaogaina o nei faʻataʻitaʻiga AI, e mafai ai ona tatou atiaʻe faʻataʻitaʻiga faʻataʻitaʻiga sili atu saʻo ma malosi e mafai ona faʻamalieina le faʻatinoina o le faiga ma faʻaitiitia ai le lamatiaga o le faʻaogaina o meafaigaluega.

LangChain ma DSPy: Meafaigaluega Fa'aopoopo mo Fa'atinoga Lelei

LangChain ma DSPy o ni mea faigaluega faʻapitoa taʻutaʻua e lua e mafai ona fesoasoani i le faʻafaigofieina o le atinaʻeina ma le faʻapipiʻiina o le faʻaogaina o le geothermal energy system. LangChain o se faletusi Python lea e tuʻuina atu ai se faʻaoga fetuutuunai ma faʻaogaina mo le fausiaina o talosaga AI, ae o le DSPy o se faletusi Rust e ofoina atu se fofo maualuga ma faʻaleleia mo le tufatufaina atu o komepiuta.

** Fa'ata'ita'iga Fa'ailoga: Fa'ata'ita'iga Va'ai ma LangChain ma LLMs**

O se faʻataʻitaʻiga faʻataʻitaʻiga faigofie lea e faʻaalia ai le faʻaogaina o LangChain ma LLM mo le faʻataʻitaʻiga faʻataʻitaʻiga o faiga faʻaogaina o le geothermal:

faaulufale mai le langchain
mai langchain.llms faaulufale mai LLM


llm = LLM("davinci:3-vaega")

# Fa'amatala le fa'amaumauga ole fa'aogaina ole geothermal
fa'amatalaga = {
    "vevela": [20, 30, 40, 50],
    "omia": [10, 20, 30, 40],
    "fua_feso'o": [100, 200, 300, 400]
}

# Fausia se paipa LangChain
paipa = langchain.Pipeline(
    igoa="geothermal_model",
    galuega=[
        langchain.Tasks.TextGeneration(
            input_data=faamatalaga,
            output_data={"temperature": Leai, "pressure": Leai, "flow_rate": Leai},
            llm=llm
        )
    ]
)

# Tamomoe le paipa
gaosiga = pipeline.run()

# Lolomi le galuega faatino
lolomi(output)

O lenei faʻailoga o loʻo faʻamatalaina ai se paipa LangChain faigofie e faʻaaoga ai se LLM e vaʻai ai le vevela, mamafa, ma le tafe o le faʻaogaina o le geothermal energy system e faʻavae i luga o faʻamaumauga faʻasolopito.

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

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

  1. langchain.Pipeline(): Fausia se paipa LangChain fou.
  2. llm=LLM("davinci:3-batch"): Fa'amatala se fa'ata'ita'iga LLM e fa'aaoga ai le fa'ata'ita'iga Davinci.
  3. tasks=[langchain.Tasks.TextGeneration()]: Fa'amatala se galuega fau tusitusiga mo le paipa.
  4. input_data=data: Tu'u fa'amaumauga fa'asolopito i le paipa.
  5. output_data={"temperature": Leai, "pressure": Leai, "flow_rate": Leai}: Fa'amatala le fa'atulagaina o fa'amaumauga mo le paipa.
  6. pipeline.run(): Fa'asolo le paipa ma fa'atupu ai galuega.
  7. langchain.Tasks.TextGeneration(input_data=data, output_data=output_data, llm=llm): Fa'amatala se galuega fa'atupu tusitusiga ma fa'amatalaga fa'aoga ma fa'amatalaga.
  8. langchain.Pipeline(name="geothermal_model", tasks=[task]): Fausia se paipa fou ma se igoa patino ma se galuega.
  9. llm.predict(input_data=data): Fa'aaogā le LLM e va'ai ai mea e fai e fa'atatau i fa'amatalaga tu'uina.
  10. pipeline.update_tasks(tasks=[new_task]): Fa'afou le paipa i galuega fou.

Va'aiga

Ole fa'aaliga lea ole faiga fa'ata'ita'i va'ai ile fa'aogaina ole Chart.js:

{
  "type": "laina",
  "fa'amatalaga": {
    "labels": ["Temperature", "Omiga", "Tafe o le tafe"],
    "fa'amaumauga": [
      {
        "label": "Tulaga Taualoa",
        "faamatalaga": [20, 30, 40],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "borderColor": "rgba(255, 99, 132, 1)",
        "pointBackgroundColor": "rgba(255, 99, 132, 1)",
        "pointBorderColor": "#fff",
        "pointHoverBackgroundColor": "#fff",
        "pointHoverBorderColor": "rgba(255, 99, 132, 1)"
      },
      {
        "label": "Tulaga Taua Moni",
        "faamatalaga": [25, 35, 45],
        "backgroundColor": "rgba(54, 162, 235, 0.2)",
        "borderColor": "rgba(54, 162, 235, 1)",
        "pointBackgroundColor": "rgba(54, 162, 235, 1)",
        "pointBorderColor": "#fff",
        "pointHoverBackgroundColor": "#fff",
        "pointHoverBorderColor": "rgba(54, 162, 235, 1)"
      }
    ]
  },
  "filifiliga": {
    "igoa": {
      "fa'aaliga": moni,
      "text": "Fa'ata'ita'iga Fa'ata'ita'i ole Faiga Fa'asao ole Geothermal"
    },
    "fua": {
      "yAxes": [
        {
          "scaleLabel": {
            "fa'aaliga": moni,
            "labelString": "Taua"
          }
        }
      ]
    }
  }
}

O lenei fa'aaliga o lo'o fa'aalia ai tulaga taua (laina mumu) ma tau moni (laina lanumoana) mo le vevela, mamafa, ma le tafe.

Fa'ai'uga

O le tatalaina o le geothermal gafatia e manaʻomia ai tekinolosi faʻapitoa ma faiga faʻalavelave. E ala i le faʻaogaina o AI biological ma LLMs mo le faʻataʻitaʻiga faʻataʻitaʻiga ma le faʻataʻitaʻiina o faiga faʻaogaina o le geothermal, e mafai ai ona tatou atiaʻe faʻataʻitaʻiga sili atu le saʻo ma malosi e mafai ona faʻamalieina le faʻatinoina o le faiga ma faʻaitiitia ai le lamatiaga o mea faigaluega. LangChain ma DSPy o ni mea faigaluega faʻapitoa taʻutaʻua e lua e mafai ona fesoasoani i le faʻafaigofieina o le atinaʻeina ma le faʻapipiʻiina o le faʻaogaina o le geothermal energy system. E ala i le fa'aogaina o nei mea faigaluega ma metotia, e mafai ai ona tatou tatalaina le gafatia atoatoa o le geothermal energy ma saofagā i se lumana'i malosi fa'afouina ma fa'afouina.


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