Gadzira mumiriri weAI uchishandisa DSPy

Kubatanidza DSPy neLangChain yeAdvanced AI-Driven Software Development

Introduction

Munda wekuvandudza software uri kukurumidza kubuda, nekuwedzera kutorwa kweArtificial Intelligence (AI) uye Machine Kudzidza (ML) matekiniki. Maraibhurari maviri anozivikanwa ePython, DSPy uye LangChain, ari kuwana traction munzvimbo yeAI-inotyairwa software yekuvandudza nzvimbo. Muchinyorwa chino, tichaongorora kubatanidzwa kweDSPy neLangChain, tichiongorora kurongeka kwavo kweAI vamiririri uye Makuru Mutauro Models (LLMs) kusimudzira maitiro einjiniya esoftware uye kufambisa mafambiro ebasa.

Chii chinonzi DSPy?

DSPy iraibhurari yePython inopa seti yezvishandiso zvekuvaka uye kutonga maAI vamiririri. Inobvumira vanogadzira kugadzira, kudzidzisa, uye kuendesa AI vamiririri vanogona kuita akasiyana emabasa, akadai sekuongorora data, kugadzirwa kwemutauro wechisikigo, uye kuita sarudzo. DSPy yakavakirwa pamusoro peiyo yakakurumbira PyTorch raibhurari uye inopa iri nyore uye intuitive API yekuvaka AI vamiririri.

Chii chinonzi LangChain?

LangChain iraibhurari yePython inopa seti yezvishandiso zvekuvaka nekugadzirisa maLLM. Inobvumira vanogadzira kugadzira, kudzidzisa, uye kutumira maLLM anogona kuita akasiyana mabasa, sekugadzira zvinyorwa, kupindura mibvunzo, uye kushandura mutauro. LangChain yakavakirwa pamusoro peiyo yakakurumbira Hugging Face Transformers raibhurari uye inopa iri nyore uye intuitive API yekuvaka LLMs.

Kubatanidza DSPy uye LangChain

Kubatanidzwa kweDSPy neLangChain kunobvumira vashanduri kuti vagadzire mapurogiramu epurogiramu epamusoro eAI anogona kuita mabasa akasiyana-siyana. Nekubatanidza simba revamiririri veAI uye maLLM, vagadziri vanogona kugadzira maapplication anogona kuongorora data, kugadzira zvinyorwa, uye kuita sarudzo.

Heino kodhi yekodhi yakapfava inoratidza kubatanidzwa kweDSPy neLangChain:

import dspy
import langchain


mumiririri = dspy.Agent()

# Gadzira iyo LLM uchishandisa LangChain
llm = langchain.LLM()

# Shandisa iyo AI mumiriri kuongorora data
data = {"zita": "John", "zera": 30}
ongororo = agent. analyze(data)

# Shandisa iyo LLM kugadzira zvinyorwa zvinoenderana nekuongorora
zvinyorwa = llm.generate_text(analysis)

# Dhinda zvinyorwa zvakagadzirwa
dhinda(zvinyorwa)

Iyi kodhi yekodhi inoratidza nzira yekugadzira mumiriri weAI uchishandisa DSPy, iyo LLM inoshandisa LangChain, uye inoishandisa pamwe chete kuongorora data uye kugadzira zvinyorwa.

Kurongeka kweAI Agents uye LLMs

Iko kurongeka kweAI agents uye LLMs chinhu chakakosha chekubatanidza DSPy neLangChain. Nekubatanidza simba reAI vamiririri neLLMs, vagadziri vanogona kugadzira maapplication anogona kuita akasiyana mabasa, akadai se:

Ongororo yedata: vamiririri veAI vanogona kuongorora data, nepo maLLM achigona kugadzira zvinyorwa zvichienderana nekuongorora.
Kugadzira zvinyorwa: maLLM anogona kugadzira zvinyorwa, nepo AI vamiririri vanogona kuongorora zvinyorwa uye kuita sarudzo.
Kuita Sarudzo: Vamiriri veAI vanogona kuita sarudzo zvichienderana nekuongorora data, nepo maLLM achigona kupa kurudziro.

Heino ratidziro yekurongeka kweAI vamiririri uye maLLM vachishandisa Chart.js:

{
  "type": "bha",
  "data": {
    "labels": ["Data Analysis", "Text Generation", "Kuita Sarudzo"],
    "datasets": [
      {
        "label": "AI Agents",
        "data": [10, 5, 8],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "borderColor": "rgba(255, 99, 132, 1)",
        "borderWidth": 1
      },
      {
        "label": "LLMs",
        "data": [8, 10, 5],
        "backgroundColor": "rgba(54, 162, 235, 0.2)",
        "borderColor": "rgba(54, 162, 235, 1)",
        "borderWidth": 1
      }
    ]
  },
  "sarudzo": {
    "zvikero": {
      "yAxes": [
        {
          "zvikwekwe": {
            "beginAtZero": chokwadi
          }
        }
      ]
    }
  }
}

Ichi chitarisiko chinoratidza maitiro eAI vamiririri uye maLLM anogona kurongwa kuita mabasa akasiyana.

Pamusoro 5-10 Inonyanya Kushandiswa Mirairo/Zvidimbu

Heano epamusoro 5-10 anonyanya kushandiswa mirairo/zvimedu zveDSPy uye LangChain:

dspy.Agent(): Inogadzira mumiririri weAI uchishandisa DSPy.
langchain.LLM(): Inogadzira LLM uchishandisa LangChain.
agent.analyze(data): Inoongorora data uchishandisa AI mumiririri.
llm.generate_text(analysis): Inogadzira zvinyorwa zvichibva pakuongorora uchishandisa LLM.
agent.make_decision(data): Inoita sarudzo inobva pakuongorora data uchishandisa AI mumiririri.
llm.get_recommendations(analysis): Inowana kurudziro zvichienderana nekuongorora uchishandisa LLM.
dspy.train_agent(data): Inodzidzisa mumiririri weAI vachishandisa data rakapihwa.
langchain.train_llm(data): Dzidzisa LLM uchishandisa data rakapihwa.
dspy.deploy_agent(): Inotumira mumiririri weAI.
langchain.deploy_llm(): Inotumira iyo LLM.

Mhedziso

Kubatanidzwa kweDSPy neLangChain kunopa chigadziro chine simba chekuvaka advanced AI-driven software applications. Nekubatanidza simba revamiririri veAI uye maLLM, vagadziri vanogona kugadzira maapplication anogona kuongorora data, kugadzira zvinyorwa, uye kuita sarudzo. Kurongeka kweAI agents neLLMs chinhu chakakosha chekubatanidza DSPy neLangChain, uye nekunzwisisa mashandisiro ezvishandiso izvi pamwe chete, vagadziri vanogona kugadzira zvikumbiro zvinogona kuita mabasa akasiyana.

Basa Remangwana

Basa remangwana rekubatanidza DSPy neLangChain rinosanganisira:

Kugadzira mamwe epamberi eAI vamiririri uye maLLM anogona kuita mabasa akaomarara.
Kuvandudza kurongeka kweAI vamiririri uye maLLM kuti zvive nyore kune vanogadzira kushandisa zvishandiso izvi pamwechete.
Kugadzira mamwe maapplication anoshandisa DSPy uye LangChain kuratidza kugona kwavo.

Mareferenzi

DSPy zvinyorwa: https://dspy.readthedocs.io/
LangChain zvinyorwa: https://langchain.readthedocs.io/
Chart.js zvinyorwa: https://www.chartjs.org/docs/latest/

Ongorora: Iyo kodhi inovharira uye maonesheni anoshandiswa muchinyorwa ichi ndeekuratidzira chete uye anogona kunge asiri iwo chaiwo kodhi kana maonesheni anoshandiswa mumashandisirwo epasirese.

Gadzira mumiriri weAI uchishandisa DSPy

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