Ƙirƙiri wakilin AI ta amfani da DSPy | B4N1

Ƙirƙiri wakilin AI ta amfani da DSPy

8 months ago · b4n1 Read time: 4 min

** Haɗin DSPy da LangChain don Ci gaban AI-Driven Haɓaka Software ***

** Gabatarwa ***

Fannin haɓaka software yana haɓaka cikin sauri, tare da haɓaka haɓakar fasahar Artificial Intelligence (AI) da dabarun Koyan Injin (ML). Shahararrun ɗakunan karatu na Python guda biyu, DSPy da LangChain, suna samun karɓuwa a cikin sararin haɓaka software na AI. A cikin wannan labarin, za mu shiga cikin haɗin kai na DSPy da LangChain, bincika ƙungiyar ma'aikatan AI da Manyan Harsuna (LLMs) don haɓaka ayyukan injiniya na software da kuma daidaita ayyukan aiki.

Mene ne DSPy?

DSPy ɗakin karatu ne na Python wanda ke ba da saitin kayan aikin gini da sarrafa wakilan AI. Yana ba masu haɓaka damar ƙirƙira, horarwa, da tura wakilan AI waɗanda za su iya aiwatar da ayyuka iri-iri, kamar nazarin bayanai, sarrafa harshe na halitta, da yanke shawara. An gina DSPy a saman mashahurin ɗakin karatu na PyTorch kuma yana ba da API mai sauƙi da fahimta don gina wakilai AI.

** Menene LangChain?**

LangChain ɗakin karatu ne na Python wanda ke ba da saitin kayan aikin gini da sarrafa LLMs. Yana ba masu haɓakawa damar ƙirƙira, horarwa, da tura LLMs waɗanda zasu iya aiwatar da ayyuka iri-iri, kamar tsara rubutu, amsa tambaya, da fassarar harshe. An gina LangChain a saman mashahurin ɗakin karatu na Hugging Face Transformers kuma yana ba da API mai sauƙi da fahimta don gina LLMs.

** Haɗin DSPy da LangChain ***

Haɗin kai na DSPy da LangChain yana ba masu haɓaka damar ƙirƙirar aikace-aikacen software na ci gaba na AI wanda zai iya yin ayyuka iri-iri. Ta hanyar haɗa ikon wakilan AI da LLMs, masu haɓakawa na iya ƙirƙirar aikace-aikacen da za su iya nazarin bayanai, samar da rubutu, da yanke shawara.

Anan akwai shingen lamba mai sauƙi wanda ke nuna haɗin DSPy da LangChain: `` Python shigo da dspy shigo da langchain

wakili = dspy.Agent()

Ƙirƙiri LLM ta amfani da LangChain

llm = langchain.LLM()

Yi amfani da wakilin AI don nazarin bayanai

data = {"name": "John", "shekaru": 30} analysis = agent.analyze(bayanai)

Yi amfani da LLM don ƙirƙirar rubutu dangane da bincike

rubutu = lm.generate_text(bincike)

Buga rubutun da aka samar

buga (rubutu)

Wannan toshe lambar yana nuna yadda ake ƙirƙirar wakili na AI ta amfani da DSPy, LLM ta amfani da LangChain, da amfani da su tare don nazarin bayanai da samar da rubutu.

** Orchestration na AI Agents da LLMs ***

Ƙaddamar da wakilai na AI da LLMs wani muhimmin al'amari ne na haɗa DSPy da LangChain. Ta hanyar haɗa ikon wakilan AI da LLMs, masu haɓakawa na iya ƙirƙirar aikace-aikacen da za su iya aiwatar da ayyuka iri-iri, kamar:

* Binciken bayanai: Ma'aikatan AI na iya nazarin bayanai, yayin da LLMs za su iya samar da rubutu dangane da bincike.
* Ƙarfafa rubutu: LLMs na iya samar da rubutu, yayin da wakilan AI za su iya nazarin rubutun kuma su yanke shawara.
* Yin yanke shawara: Wakilan AI na iya yanke shawara dangane da nazarin bayanai, yayin da LLMs na iya ba da shawarwari.

Anan ga hangen nesa na ƙungiyar wakilan AI da LLMs ta amfani da Chart.js:
Json
{
  "type": "bar",
  "data": {
    "labels": ["Binciken Bayanai", "Tsarin Rubutu", "Yin yanke shawara"],
    "datasets": [
      {
        "label": "AI Agents",
        "bayanai": [10, 5, 8],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "BorderColor": "rgba(255, 99, 132, 1)",
        "BorderWidth": 1
      },
      {
        "label": "LLMs",
        "bayanai": [8, 10, 5],
        "backgroundColor": "rgba(54, 162, 235, 0.2)",
        "BorderColor": "rgba(54, 162, 235, 1)",
        "BorderWidth": 1
      }
    ]
  },
  "zaɓi": {
    "ma'auni": {
      "yAxes": [
        {
          "zama": {
            "beginAtZero": gaskiya
          }
        }
      ]
    }
  }
}

Wannan hangen nesa yana nuna yadda za a iya tsara wakilan AI da LLM don yin ayyuka daban-daban.

** Manyan Dokoki 5-10 da Akafi Amfani da su/Snippets**

Anan akwai manyan 5-10 da aka fi amfani da umarni/snippets don DSPy da LangChain:

  1. dspy.Agent(): Yana ƙirƙirar wakili na AI ta amfani da DSPy.
  2. langchain.LLM(): Yana ƙirƙirar LLM ta amfani da LangChain.
  3. agent.analyze(data): Yana nazarin bayanai ta amfani da wakilin AI.
  4. lm.generate_text(bincike): Yana haifar da rubutu bisa bincike ta amfani da LLM.
  5. agent.make_decision(data): Yana yanke shawara dangane da nazarin bayanai ta amfani da wakilin AI.
  6. lm.get_recommendations(bincike): Yana samun shawarwari dangane da bincike ta amfani da LLM.
  7. dspy.train_agent(data): Horar da wakilin AI ta amfani da bayanan da aka bayar.
  8. langchain.train_llm(data): Yana horar da LLM ta amfani da bayanan da aka bayar.
  9. dspy.deploy_agent(): Yana tura wakilin AI.
  10. langchain.deploy_llm(): Yana tura LLM.

Kammalawa

Haɗin kai na DSPy da LangChain yana ba da kayan aiki mai ƙarfi don gina manyan aikace-aikacen software na AI-kore. Ta hanyar haɗa ikon wakilan AI da LLMs, masu haɓakawa na iya ƙirƙirar aikace-aikacen da za su iya nazarin bayanai, samar da rubutu, da yanke shawara. Ƙaddamar da ma'aikatan AI da LLMs wani muhimmin al'amari ne na haɗawa da DSPy da LangChain, kuma ta hanyar fahimtar yadda ake amfani da waɗannan kayan aikin tare, masu haɓakawa zasu iya ƙirƙirar aikace-aikacen da za su iya yin ayyuka daban-daban.

Aikin Gaba

Ayyukan gaba akan haɗa DSPy da LangChain sun haɗa da:

  • Haɓaka ƙarin manyan jami'an AI da LLM waɗanda zasu iya yin ayyuka masu rikitarwa.
  • Haɓaka ƙungiyar wakilai na AI da LLMs don sauƙaƙa wa masu haɓakawa don amfani da waɗannan kayan aikin tare.
  • Ƙirƙirar ƙarin aikace-aikacen da ke amfani da DSPy da LangChain don nuna iyawar su.

Nazari

Lura: Tubalan lambar da abubuwan gani da aka yi amfani da su a cikin wannan labarin don dalilai ne na nunawa kawai kuma maiyuwa ba su zama ainihin lamba ko abubuwan gani da ake amfani da su a aikace-aikacen zahirin duniya ba.

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