Ukwakha ii-Agents ezihlakaniphile ngeLangChain: Ukulungelelanisa ii-LLM ze-Real-World Application | B4N1

Ukwakha ii-Agents ezihlakaniphile ngeLangChain: Ukulungelelanisa ii-LLM ze-Real-World Application

8 months ago · b4n1 Read time: 4 min

Ukwakha iiarhente ezikrelekrele ngeLangChain: UkuLungiselela iiLLMs zeReal-World Application

Intshayelelo

Ukufika kweMifuziselo yeeLwimi ezinkulu (LLMs) kuguqule icandelo leNatural Language Processing (NLP) kwaye kuvule iindlela ezintsha zokuphuhlisa ii-arhente ezikrelekrele ezinokuqonda, ukuvelisa, kunye nokusebenzisana nolwimi lwabantu. ILangChain lithala leencwadi lePython elibonelela ngesakhelo esiguquguqukayo nesinokwehla sokwakha iiarhente ezikrelekrele ezisebenzisa iiLLMs. Kule nqaku, siza kuhlolisisa ukuhlanganiswa kweLangChain ekuphuhliseni ii-agent ezihlakaniphile, kugxininise kwizicwangciso zokuphunyezwa okusebenzayo, iindlela zokusebenzisa i-orchestration, kunye nokusetyenziswa kwamatyala kubunjineli besofthiwe.

Zintoni ii-Intelligent Agents?

Iiarhente ezikrelekrele ziinkqubo zesoftware ezinokubona okusingqongileyo, ziqiqe malunga nolwazi abalufumanayo, kwaye bathathe amanyathelo okuphumeza iinjongo zabo. Banokucingwa njengabenzi-zigqibo abazimeleyo abanokunxibelelana nabasebenzisi, ezinye iiarhente, okanye iinkqubo zokufezekisa injongo ethile. Iiarhente ezikrelekrele zinokuhlelwa ngokweendidi ezahlukeneyo, kubandakanywa:

  • Iiarhente ezisekelwe kumthetho: Ezi arhente zisebenzisa imithetho echazwe kwangaphambili ukwenza izigqibo.
  • Iiarhente zokufunda ngomatshini: Ezi arhente zisebenzisa i-algorithms yokufunda ngomatshini ukufunda kwiidatha kunye nokwenza izigqibo.
  • Ii-agent zeHybrid: Ezi arhente zidibanisa imigaqo-siseko kunye neendlela zokufunda ngomatshini ukwenza izigqibo.

Yintoni iLangChain?

ILangChain lithala leencwadi lePython elibonelela ngesakhelo esiguquguqukayo nesinokwehla sokwakha iiarhente ezikrelekrele ezisebenzisa iiLLMs. Ivumela abaphuhlisi ukuba benze, baqeqeshe, kwaye basebenzise ii-LLM, kunye nokuzidibanisa nezinye iimodeli ze-AI kunye neenkqubo. ILangChain ibonelela ngoluhlu lweempawu, kubandakanya:

  • ** Ukuhlanganiswa kwe-LLM **: I-LangChain ibonelela ngendlela elula nesebenzayo yokudibanisa ii-LLM kunye nezinye iimodeli ze-AI kunye neenkqubo.
  • I-Orchestration: I-LangChain ibonelela ngeendlela ezininzi zobuchule be-orchestration, kubandakanywa nolawulo lomsebenzi, ulawulo lokuhamba komsebenzi, kunye nolawulo lwedatha.
  • Scalability: I-LangChain yenzelwe ukulinganisa kunye nedatha enkulu kunye nokuhamba komsebenzi onzima.

IiNdlela eziSebenzayo zokuPhumeza

Ukwakha iiarhente ezikrelekrele ngeLangChain, kufuneka ulandele ezi zicwangciso zokuphumeza ezisebenzayo:

  1. Khetha iLLM: Khetha iLLM ezanelisayo iimfuno zakho, ezinjengolwimi, ubungakanani, kunye nobunzima.
  2. Lungisa idatha: Lungiselela i-dataset efanelekileyo kwimeko yakho yosetyenziso nefanelekileyo ekuqeqesheni iLLM.
  3. Qeqesha i-LLM: Qeqesha i-LLM usebenzisa i-dataset elungiselelwe kunye ne-API yoqeqesho ye-LangChain.
  4. ** Ukudibanisa kunye nezinye iimodeli ze-AI **: Hlanganisa i-LLM eqeqeshiwe kunye nezinye iimodeli ze-AI kunye neenkqubo usebenzisa i-API yokuhlanganiswa kwe-LangChain.
  5. **Imisebenzi ye-Orchestrate **: Sebenzisa i-API ye-orchestration ye-LangChain ukulawula imisebenzi, ukuhamba komsebenzi, kunye nedatha.

Iindlela zokuSebenza

I-LangChain inikeza uluhlu lweendlela zokucwangcisa ezinokuthi zisetyenziswe ukulawula imisebenzi, ukuhamba komsebenzi, kunye nedatha. Ezinye zeendlela eziphambili zeokhestra ziquka:

  • **Ulawulo lomsebenzi **: ILangChain ibonelela nge-API yolawulo lomsebenzi evumela abaphuhlisi ukuba benze, balawule, kwaye baqhube imisebenzi.
  • Ulawulo lokuhamba komsebenzi: I-LangChain ibonelela nge-API yolawulo lokuhamba komsebenzi evumela abaphuhlisi ukuba benze, balawule, kwaye baqhube ukuhamba komsebenzi.
  • ** Ulawulo lwedatha **: ILangChain inikezela nge-API yolawulo lwedatha evumela abaphuhlisi ukuba balawule kwaye basebenzise idatha.

Sebenzisa iimeko kubuNjineli beSoftware

ILangChain ingasetyenziswa kwiimeko ezahlukeneyo zokusebenzisa ubunjineli besoftware, kubandakanya:

  • ** Ii-Chatbots **: ILangChain ingasetyenziselwa ukwakha ii-chatbots ezinokuqonda, ukuvelisa, kunye nokusebenzisana nolwimi lwabantu.
  • ** Abancedisi beVirtual **: I-LangChain ingasetyenziselwa ukwakha abancedisi benyani abanokwenza imisebenzi, baphendule imibuzo, kwaye banike ulwazi.
  • ** Ukuveliswa komxholo **: I-LangChain ingasetyenziselwa ukuvelisa umxholo, njengamanqaku, izithuba zeblogi, kunye nezithuba zeendaba zoluntu.

Ephezulu 5-10 Eyeyona Isetyenziswayo Imiyalelo/iziqwengana

Nantsi eyona miyalelo/iziqwengana eziphezulu ezi-5-10 zeLangChain:

  1. Ukungeniswa kweLangChain: importingchain
  2. Ukwenza iLLM: llm = langchain.LLM("my_llm")
  3. Uqeqesho lwe-LLM: llm.train(idatha)
  4. Ukudibanisa nezinye iimodeli ze-AI: ai_model = langchain.AIModel("my_ai_model")
  5. Imisebenzi yokucwangcisa: umsebenzi = langchain.Task("my_task")
  6. Ukulawula ukuqhutywa komsebenzi: `ukuhamba komsebenzi = langchain.
  7. Ukulawula idatha: idatha = langchain.Data("data_yam")
  8. Ukwenza i-chatbot: chatbot = langchain.Chatbot("my_chatbot")
  9. Ukwenza umncedisi wenyani: umncedisi_wokwenene = langchain.VirtualAssistant("my_virtual_assistant")
  10. Ukuvelisa umxholo: umxholo = langchain.ContentGenerator("my_content_generator")

Umbono

Nantsi imbonakalo yoyilo lweLangChain usebenzisa iChart.js:

{
  "type": "bar",
  "idatha": {
    "labels": ["LLM", "Orchestration", "Integration", "Task Management", "Workflow Management", "Ulawulo lweData"],
    "iiseti zedatha": [
      {
        "label": "LangChain Architecture",
        "idatha": [10, 8, 6, 4, 2, 1],
        "backgroundColor": ["#4CAF50", "#FF9800", "#2196F3", "#8BC34A", "#03A9F4", "#FFC107"]
      }
    ]
  },
  "iinketho": {
    "isihloko": {
      "bonisa": yinyani,
      "text": "LangChain Architecture"
    },
    "legend": {
      "bonisa": yinyani
    }
  }
}

Lo mboniso ubonisa amacandelo ahlukeneyo okwakhiwa kweLangChain, kubandakanywa ii-LLM, i-orchestration, ukuhlanganiswa, ulawulo lomsebenzi, ulawulo lokuhamba komsebenzi, kunye nolawulo lwedatha.

Isiphelo

ILangChain lithala leencwadi elinamandla elibonelela ngesakhelo esiguquguqukayo nesinokwehla sokwakha iiarhente ezikrelekrele ezisebenzisa iiLLMs. Ngokulandela amacebo okuphumeza asebenzayo kunye nobuchule bokuqulunqa obuchazwe kweli nqaku, abaphuhlisi banokwakha iiarhente ezikrelekrele ezinokuqonda, ukuvelisa, kunye nokusebenzisana nolwimi lwabantu. Iimeko zokusetyenziswa kubunjineli besoftware zibonisa amandla eLangChain kwizicelo ezahlukeneyo, ukusuka kwii-chatbots kunye nabancedisi benyani ukuya kwisizukulwana somxholo kunye nokunye. NgeLangChain, abaphuhlisi banokwenza ii-agent ezihlakaniphile ezinokwenza imisebenzi enzima kwaye zinike ixabiso kubasebenzisi.

Ikhowudi yokuvala

Nantsi ibhloko yekhowudi elula ebonisa indlela yokwenza i-chatbot usebenzisa iLangChain:

ngenisa i-langchain


llm = langchain.LLM("my_llm")

# Yenza i-chatbot
chatbot = langchain.Chatbot(llm)

# Chaza umsebenzi
umsebenzi = langchain.Task("my_task")

# Yenza umsebenzi
iziphumo = chatbot.execute(umsebenzi)

# Shicilela umphumo
shicilela(isiphumo)

Le khowudi block yenza LLM, chatbot, kwaye umsebenzi, kwaye ke iphumeze umsebenzi usebenzisa chatbot. Isiphumo siprintwa kwi-console.

Rust and Go Code

I-LangChain ikwabonelela nge-Rust and Go bindings, engasetyenziselwa ukwakha ii-arhente ezikrelekrele kwezi lwimi. Nanku umzekelo webhlokhi yekhowudi yeRust elula ebonisa indlela yokwenza i-chatbot usebenzisa iLangChain:

sebenzisa i-langchain::{LLM, Chatbot};

fn eyona () {
    // Yenza iLLM
    vumela llm = LLM::ntsha("my_llm");

    // Yenza i-chatbot
    vumela i-chatbot = I-Chatbot::intsha(llm);

    // Chaza umsebenzi
    vumela umsebenzi = Umsebenzi:: omtsha ("my_task");

    // Yenza umsebenzi
    vumela isiphumo = chatbot.execute(umsebenzi);

    // Shicilela umphumo
    println!("{}", isiphumo);
}

Kwaye nanku umzekelo webhloko yekhowudi ye-Go elula ebonisa indlela yokudala i-chatbot usebenzisa i-LangChain:

iphakheji engundoqo

ngenisa ngaphandle (
	"fmt"
	"github.com/langchain/langchain-go"
)

func main() {
	// Yenza iLLM
	llm := langchain.NewLLM("my_llm")

	// Yenza i-chatbot
	chatbot := langchain.NewChatbot(llm)

	// Chaza umsebenzi
	umsebenzi := langchain.NewTask("my_task")

	// Yenza umsebenzi
	iziphumo := chatbot.Phumeza(umsebenzi)

	// Shicilela umphumo
	fmt.Println(isiphumo)
}

Ezi khowudi zeebhloko zibonisa indlela yokwenza i-chatbot usebenzisa i-LangChain kwi-Rust kunye ne-Go.

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