** Haɗa DSPy tare da LLMs don Gwajin Software Na atomatik: Wani Sabon Tsarin A Tabbacin Inganci**

** Gabatarwa ***

Haɗin Samfuran Manyan Harshe (LLMs) da Deep Software Programming (DSPy) ya canza fasalin gwajin software. Ta hanyar ba da damar iyawar LLMs da DSPy, masu haɓakawa za su iya sarrafa tsarin gwaji, haɓaka aminci da inganci a cikin injiniyan software. A cikin wannan labarin, za mu bincika yadda DSPy zai iya daidaita tsarin gwaji don aikace-aikacen da aka gina akan LLMs, kuma mu tattauna fa'idodi da ƙalubalen wannan yanayin da ke tasowa.

Mene ne DSPy?

DSPy ɗakin karatu ne na Python wanda ke ba da saitin kayan aiki don sarrafa sarrafa software. Yana ba masu haɓakawa damar ƙirƙira da aiwatar da shari'o'in gwaji, saka idanu sakamakon gwaji, da tantance bayanan gwaji. An ƙirƙira DSPy don yin aiki ba tare da wata matsala ba tare da LLMs, yana ba da damar ƙirƙirar ginshiƙan na'urori masu sarrafa kansa na gwaji.

** Menene LLMs?**

LLMs wani nau'in samfurin hankali ne na wucin gadi (AI) wanda zai iya sarrafawa da samar da harshe irin na ɗan adam. An horar da su akan ɗimbin bayanan rubutu kuma suna iya koyan gane alamu, alaƙa, da mahallin. LLMs suna da aikace-aikace da yawa a cikin injiniyan software, gami da sarrafa harshe na halitta, nazarin rubutu, da tsara lamba.

Haɗin DSPy tare da LLMs

Haɗin DSPy tare da LLMs yana ba da damar ƙirƙirar shari'o'in gwaji na atomatik waɗanda za su iya aiwatar da al'amura masu rikitarwa, daidaita ma'amalar mai amfani, da kuma nazarin sakamakon gwaji. Wannan haɗin kai yana ba da fa'idodi da yawa, gami da:

** Ingantaccen ɗaukar hoto ***: DSPy na iya haifar da shari'o'in gwaji waɗanda ke rufe abubuwa da yawa, tabbatar da cewa an gwada aikace-aikacen sosai.
**Ƙara haɓakawa ***: Gwaji ta atomatik tare da DSPy da LLMs yana rage lokaci da ƙoƙarin da ake buƙata don aiwatar da shari'o'in gwaji, yantar da masu haɓakawa don mai da hankali kan wasu ayyuka.
** Ingantaccen Aminci ***: DSPy na iya saka idanu akan sakamakon gwajin da faɗakar da masu haɓakawa ga kowane al'amura ko kurakurai, tabbatar da cewa aikace-aikacen abin dogaro ne kuma karko.

** Lambobin Misali: Mai sarrafa Lambobin Gwaji tare da DSPy da LLMs ***

Anan akwai toshe lambar misali wanda ke nuna yadda ake amfani da DSPy tare da LLM don sarrafa shari'o'in gwaji: `` Python shigo da dspy daga masu canji suna shigo da AutoModelForSeq2SeqLM, AutoTokenizer

samfurin = AutoModelForSeq2SeqLM.from_pretrained("t5-base") tokenizer = AutoTokenizer.from_pretrained("t5-base")

Ƙirƙiri shari'ar gwajin DSPy

test_case = dspy.TestCase( name="Tsarin Gwaji 1", description=" Gwada aikin shiga", matakai=[ dspy.Step("Bude shafin shiga"), dspy.Mataki("Shigar da ingantattun takaddun shaida"), dspy.Step(" Danna maɓallin shiga"), ] )

Yi aikin gwajin ta amfani da LLM

test_results = dspy.execute_test_case(case_case, model, tokenizer)

Yi nazarin sakamakon gwajin

test_results.analyze()

Wannan toshe lambar yana nuna yadda ake ƙirƙirar shari'ar gwajin DSPy, ɗora samfurin LLM da tokenizer, aiwatar da shari'ar gwajin ta amfani da LLM, da kuma nazarin sakamakon gwajin.

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

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

1. `dspy.TestCase()`: Yana ƙirƙira sabon shari'ar gwajin DSPy.
2. `dspy.Step()`: Yana ƙara sabon mataki zuwa gwajin gwajin DSPy.
3. `dspy.execute_test_case()`: Yana aiwatar da shari'ar gwajin DSPy ta amfani da LLM.
4. `model = AutoModelForSeq2SeqLM.daga_pretrained("t5-base")`: Yana Load da samfurin LLM.
5. `tokenizer = AutoTokenizer.from_pretrained("t5-base")`: Yana Load da alamar LLM.
6. `test_results.analyze()`: Yana nazarin sakamakon gwajin gwajin DSPy.
7. `dspy.monitor_test_results()`: Yana lura da sakamakon gwajin gwajin DSPy.
8. `dspy.alert_developer()`: Yana faɗakar da mai haɓakawa ga kowace matsala ko kurakurai.
9. `dspy.generate_test_cases()`: Yana haifar da sabbin maganganun gwaji ta amfani da LLM.
10. `dspy.optimize_test_cases()`: Yana inganta aiwatar da shari'o'in gwaji ta amfani da LLM.

**Kallon gani**

Anan ga hangen nesa na haɗin DSPy da LLM ta amfani da Chart.js:
Json
{
  "type": "bar",
  "data": {
    "labels": ["Tsarin Gwaji 1", "Gwaji na 2", "Harkokin Gwaji 3"],
    "datasets": [
      {
        "label": "Lokacin Ƙirar Shari'ar Gwaji",
        "bayanai": [10, 20, 30],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "BorderColor": "rgba(255, 99, 132, 1)",
        "BorderWidth": 1
      },
      {
        "label": "Ƙididdigar Ƙirar Ƙirar Gwaji",
        "bayanai": [5, 10, 15],
        "backgroundColor": "rgba(54, 162, 235, 0.2)",
        "BorderColor": "rgba(54, 162, 235, 1)",
        "BorderWidth": 1
      }
    ]
  },
  "zaɓi": {
    "take": {
      "nuna": gaskiya,
      "text": "Haɗin DSPy da LLM"
    },
    "ma'auni": {
      "yAxes": [
        {
          "zama": {
            "beginAtZero": gaskiya
          }
        }
      ]
    }
  }
}

Wannan hangen nesa yana nuna lokacin aiwatarwa da ƙimar gazawar shari'o'in gwaji uku da aka aiwatar ta amfani da DSPy da LLM.

Kammalawa

Haɗin kai na DSPy tare da LLMs ya canza fasalin gwajin software, yana ba da damar ƙirƙirar shari'o'in gwaji na atomatik waɗanda zasu iya aiwatar da al'amura masu rikitarwa, kwaikwayi hulɗar masu amfani, da kuma nazarin sakamakon gwaji. Ta hanyar haɓaka iyawar DSPy da LLMs, masu haɓakawa zasu iya daidaita tsarin gwaji, haɓaka aminci da inganci, da haɓaka ƙimar aikace-aikacen su gabaɗaya. Yayin da wannan fasaha ke ci gaba da haɓakawa, muna iya tsammanin ganin ƙarin sabbin aikace-aikacen DSPy da LLMs a cikin injiniyan software.

Load da samfurin LLM da tokenizer

Ƙirƙiri shari'ar gwajin DSPy

Yi aikin gwajin ta amfani da LLM

Yi nazarin sakamakon gwajin

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