Kuvaka Intelligent Agents neLangChain: Kuronga maLLM eZvechokwadi-Nyika Zvikumbiro

Kuvaka Intelligent Agents neLangChain: Kuronga maLLM eChaiyo-World Application

Introduction

Kuuya kweMienzaniso Yakakura yeMutauro (LLMs) yakashandura munda weNatural Language Processing (NLP) uye yakavhura nzira nyowani dzekugadzira vakangwara vamiririri vanogona kunzwisisa, kugadzira, uye kudyidzana nemutauro wevanhu. LangChain iraibhurari yePython inopa inochinjika uye scalable sisitimu yekuvaka vakangwara vamiririri vanowedzera maLLM. Muchikamu chino, tichaongorora kubatanidzwa kweLangChain yekugadzira vamiririri vane njere, tichitarisa mazano ekugadzirisa anoshanda, maitiro ekugadzira, uye kushandiswa kwezviitiko muinjiniya yesoftware.

Chii chinonzi Intelligent Agents?

Intelligent agents zvirongwa zvesoftware zvinogona kuona nharaunda yavo, kufunga nezveruzivo rwavanogamuchira, uye kutora matanho kuzadzisa zvinangwa zvavo. Vanogona kufungidzirwa sevanozvimiririra vanoita sarudzo vanogona kudyidzana nevashandisi, vamwe vamiririri, kana masisitimu kuti vazadzise chinangwa chaicho. Intelligent agents anogona kuiswa mumhando dzakasiyana, kusanganisira:

Rule-based agents: Vamiririri ava vanoshandisa mitemo yakafanotsanangurwa kuita sarudzo.
** Muchina wekufunda mumiririri **: Vamiririri ava vanoshandisa muchina kudzidza algorithms kudzidza kubva kudata uye kuita sarudzo.
Hybrid agents: Vamiririri ava vanosanganisa mitemo-yakavakirwa uye nzira dzekudzidza muchina kuita sarudzo.

Chii chinonzi LangChain?

LangChain iraibhurari yePython inopa inochinjika uye scalable sisitimu yekuvaka vakangwara vamiririri vanowedzera maLLM. Inobvumira vanogadzira kugadzira, kudzidzisa, uye kutumira maLLM, pamwe nekuabatanidza nemamwe maAI modhi uye masisitimu. LangChain inopa huwandu hwezvinhu, zvinosanganisira:

**LLM kubatanidzwa **: LangChain inopa nzira iri nyore uye inoshanda yekubatanidza maLLM nemamwe maAI mamodheru uye masisitimu.
** Orchestration **: LangChain inopa maitiro akasiyana-siyana ekuimba, kusanganisira kutarisira basa, kufambisa basa, uye data management.
** Scalability **: LangChain yakagadzirirwa kuyera nemaseti makuru uye yakaoma workflows.

Dzidziso Yekuita Strategies

Kuti uvake vamiririri vakangwara neLangChain, unofanirwa kutevedzera nzira idzi dzinoshanda dzekuita:

Sarudza LLM: Sarudza LLM inoenderana nezvaunoda, semutauro, saizi, uye kuoma kunzwisisa.
Gadzirira data: Gadzirira dataset inoenderana nenyaya yako yekushandisa uye yakakodzera kudzidzisa LLM.
Dzidzisa LLM: Dzidzisa LLM uchishandisa dataset yakagadzirirwa uye LangChain's training API.
** Batanidza nemamwe maAI emhando **: Batanidza LLM yakadzidziswa nemamwe maAI maitiro uye masisitimu uchishandisa LangChain's integration API.
**Kuronga mabasa **: Shandisa LangChain's orchestration API kutarisira mabasa, workflows, uye data.

Maitiro eOrchestration

LangChain inopa huwandu hwemaitiro e orchestration anogona kushandiswa kubata mabasa, workflows, uye data. Mamwe emhando huru dze orchestration tekinoroji dzinosanganisira:

**Task manejimendi **: LangChain inopa basa manejimendi API inobvumira vanogadzira kugadzira, kutonga, uye kuita mabasa.
** Workflow manejimendi **: LangChain inopa kufambiswa kwebasa API inobvumira vanogadzira kugadzira, kutonga, uye kuita mafambiro ebasa.
** Kutarisira data **: LangChain inopa data manejimendi API inobvumira vanogadzira kutonga uye kushandura data.

**Shandisa Nyaya muSoftware Engineering **

LangChain inogona kushandiswa mune dzakasiyana siyana software engineering makesi ekushandisa, anosanganisira:

** Chatbots **: LangChain inogona kushandiswa kugadzira chatbots inogona kunzwisisa, kugadzira, uye kufambidzana nemutauro wevanhu.
**Virtual vabatsiri **: LangChain inogona kushandiswa kuvaka chaiwo vabatsiri vanogona kuita mabasa, kupindura mibvunzo, uye kupa ruzivo.
**Chigadzirwa chemukati **: LangChain inogona kushandiswa kugadzira zvirimo, senge zvinyorwa, zvinyorwa zvemabhurogi, uye nhepfenyuro yemagariro.

Pamusoro 5-10 Inonyanya Kushandiswa Mirairo/Zvidimbu

Heano epamusoro 5-10 anonyanya kushandiswa mirairo/zvimedu zveLangChain:

Importing LangChain: import langchain
Kugadzira LLM: llm = langchain.LLM("my_llm")
Kudzidzisa LLM: llm.train(data)
Kubatanidza nemamwe maAI modhi: ai_model = langchain.AIModel("my_ai_model")
Kuronga mabasa: basa = langchain.Task("basa_rangu")
Kutarisira mafambiro ebasa: workflow = langchain.Workflow("my_workflow")
Kutarisira data: data = langchain.Data("my_data")
Kugadzira chatbot: chatbot = langchain.Chatbot("my_chatbot")
Kugadzira virtual mubatsiri: virtual_assistant = langchain.VirtualAssistant("my_virtual_assistant")
Kugadzira zvirimo: zviri mukati = langchain.ContentGenerator("my_content_generator")

Kuona

Heino ratidziro yeLangChain architecture uchishandisa Chart.js:

{
  "type": "bha",
  "data": {
    "labels": ["LLM", "Orchestration", "Integration", "Task Management", "Workflow Management", "Data Management"],
    "datasets": [
      {
        "label": "LangChain Architecture",
        "data": [10, 8, 6, 4, 2, 1],
        "backgroundColor": ["#4CAF50", "#FF9800", "#2196F3", "#8BC34A", "#03A9F4", "#FFC107"]
      }
    ]
  },
  "sarudzo": {
    "zita": {
      "kuratidza": chokwadi,
      "text": "LangChain Architecture"
    },
    "legend": {
      "kuratidza": chokwadi
    }
  }
}

Ichi chitarisiko chinoratidza zvikamu zvakasiyana zveLangChain architecture, kusanganisira LLMs, orchestration, kubatanidzwa, basa rekugadzirisa, basa rekufambisa, uye data management.

Mhedziso

LangChain iraibhurari ine simba inopa inochinjika uye scalable sisitimu yekuvaka vakangwara vamiririri vanowedzera maLLM. Nekutevera nzira dzinoshanda dzekuita uye nzira dzekuronga dzakatsanangurwa muchinyorwa chino, vagadziri vanogona kuvaka vakangwara vamiririri vanogona kunzwisisa, kugadzira, nekudyidzana nemutauro wevanhu. Mamiriro ekushandisa muuinjiniya wesoftware anoratidza kugona kweLangChain mune zvakasiyana-siyana zvekushandisa, kubva kune chatbots uye chaiwo vabatsiri kune chizvarwa chemukati nezvimwe. NeLangChain, vagadziri vanogona kugadzira vamiririri vakangwara vanogona kuita mabasa akaomarara uye kupa kukosha kune vashandisi.

Code Block

Heino kodhi kodhi yakapusa inoratidza maitiro ekugadzira chatbot uchishandisa LangChain:

import langchain


llm = langchain.LLM("my_llm")

# Gadzira chatbot
chatbot = langchain.Chatbot(llm)

# Tsanangura basa
basa = langchain.Task("my_task")

# Ita basa racho
mhedzisiro = chatbot.execute(basa)

# Dhinda mhedzisiro
dhinda(mhedzisiro)

Iyi kodhi kodhi inogadzira LLM, chatbot, uye basa, uye yobva yaita basa racho uchishandisa chatbot. Mhedzisiro yacho inodhindwa kune console.

**Ngura uye Go Code **

LangChain inopawo Rust and Go bindings, izvo zvinogona kushandiswa kuvaka vamiririri vakangwara mumitauro iyi. Heino muenzaniso weiyo yakapusa Rust kodhi kodhi inoratidza maitiro ekugadzira chatbot uchishandisa LangChain:

shandisa langchain::{LLM, Chatbot};

fn chikuru() {
    // Gadzira iyo LLM
    let llm = LLM::new("my_llm");

    // Gadzira chatbot
    rega chatbot = Chatbot::new(llm);

    // Tsanangura basa
    regai basa = Basa :: idzva("my_task");

    // Ita basa racho
    rega mhedzisiro = chatbot.execute(basa);

    // Dhinda mhedzisiro
    println!("{}", mhedzisiro);
}

Uye heino muenzaniso weiyo yakapusa Go code block inoratidza maitiro ekugadzira chatbot uchishandisa LangChain:

package main

import (
	"fmt"
	"github.com/langchain/langchain-go"
)

func chikuru() {
	// Gadzira iyo LLM
	llm := langchain.NewLLM("my_llm")

	// Gadzira chatbot
	chatbot := langchain.NewChatbot(llm)

	// Tsanangura basa
	basa := langchain.NewTask("my_task")

	// Ita basa racho
	mhedzisiro := chatbot.Execute(basa)

	// Dhinda mhedzisiro
	fmt.Println(mhedzisiro)
}

Aya kodhi mabhuraki anoratidza maitiro ekugadzira chatbot uchishandisa LangChain muRust uye Go.

Kuvaka Intelligent Agents neLangChain: Kuronga maLLM eZvechokwadi-Nyika Zvikumbiro

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