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Gabatarwa

Yanayin haɓaka software yana haɓaka cikin sauri, tare da haɓaka ƙwarewar Artificial Intelligence (AI) da Koyon Inji (ML) a cikin sarrafa kansa na DevOps. Aiwatar da AI mai ƙarfi na iya taimakawa daidaita ayyukan aiki masu rikitarwa, haɓaka inganci, da rage yuwuwar kuskuren ɗan adam. A cikin wannan labarin, za mu bincika yadda za a yi amfani da DSPy da LangChain don aiwatar da ma'aikatan AI-powered don tushen tushen bincike da ingantaccen ingantaccen bututun haɓaka software.

Menene DSPy?

DSPy ɗakin karatu ne na Python wanda ke ba masu haɓakawa damar ginawa da tura ayyukan aiki na atomatik na AI. Yana ba da tsarin daidaitawa da sassauƙa don haɗa nau'ikan AI da ML daban-daban, yana mai da shi kyakkyawan zaɓi don sarrafa kansa na DevOps.

Menene LangChain?

LangChain ɗakin karatu ne na Python wanda ke ba masu haɓaka damar ginawa da tura wakilan AI na tattaunawa. Yana ba da API mai sauƙi da fahimta don ƙirƙirar wakilai waɗanda za su iya fahimta da amsa abubuwan shigar da harshe na halitta.

Aiwatar da Wakilan AI don Binciken Tushen Tushen

Binciken tushen tushen abu ne mai mahimmanci na DevOps aiki da kai, saboda yana taimakawa gano musabbabin al'amura da hana su faruwa a nan gaba. Don aiwatar da ƙididdigar tushen tushen tushen AI, za mu iya amfani da DSPy don ƙirƙirar aikin aiki wanda ke haɗawa da LangChain.

Anan ga babban matakin bayyani na tafiyar aiki:

**Tarin Bayanai ***: Tattara bayanai masu dacewa daga tushe daban-daban, kamar rajistan ayyukan, ma'auni, da fayilolin daidaitawa.
**Tsarin bayanai ***: Tsara bayanan da aka tattara don shirya su don bincike.
** Horon Samfurin ***: Horar da samfurin ML akan bayanan da aka riga aka tsara don gano alamu da alaƙa.
** Ƙirƙirar Samfuran ***: Sanya samfurin horarwa a cikin aikin DSPy.
** Haɗin Agent ***: Haɗa aikin DSPy tare da wakilin LangChain don ba da damar hulɗar harshe na halitta.

Misalin Lambobi: Tushen Binciken Aiki

`` Python shigo da dspy a matsayin dsp daga Agent shigo da langchain

Ƙayyade aikin DSPy

aikin aiki = dsp. Gudun aiki ( suna = "tushen_cause_analysis", ayyuka =[ dsp.Task("data_tarin", "Tattara bayanai daga rajistan ayyukan da awo"), dsp.Task("bayani_preprocessing", "Bayani da aka rigaya don bincike"), dsp.Task("model_training", "Horar samfurin ML akan bayanan da aka riga aka sarrafa"), dsp.Task("model_deployment", "Aidar da samfurin horarwa a cikin aikin DSPy"), ], )

ayyana wakilin LangChain

wakili = wakili ( suna = "tushen_cause_agent", model = "distilbert-base-uncased", Quick="Mene ne tushen lamarin?", )

Haɗa aikin DSPy tare da wakilin LangChain

workflow.add_agent (wakili)

Gudanar da aikin

aikin aiki.run ()


## Aiwatar da Wakilan AI don Haɓaka Haɓakawa

Inganta ingantaccen aiki wani muhimmin sashi ne na sarrafa kansa na DevOps, saboda yana taimakawa haɓaka sauri da haɓaka bututun haɓaka software. Don aiwatar da ingantaccen ingantaccen ƙarfin AI, za mu iya amfani da DSPy don ƙirƙirar aikin aiki wanda ke haɗawa da LangChain.

Anan ga babban matakin bayyani na tafiyar aiki:

1. **Tarin Bayanai ***: Tattara bayanai masu dacewa daga tushe daban-daban, kamar rajistan ayyukan, ma'auni, da fayilolin daidaitawa.
2. **Tsarin bayanai ***: Tsara bayanan da aka tattara don shirya su don bincike.
3. ** Horon Samfurin ***: Horar da samfurin ML akan bayanan da aka riga aka tsara don gano alamu da alaƙa.
4. ** Ƙirƙirar Samfuran ***: Sanya samfurin horarwa a cikin aikin DSPy.
5. ** Haɗin Agent ***: Haɗa aikin DSPy tare da wakilin LangChain don ba da damar hulɗar harshe na halitta.

### Misalin Lambobi: Ingantaccen Ingantaccen Tsarin Aiki

`` Python
shigo da dspy a matsayin dsp
daga Agent shigo da langchain

# Ƙayyade aikin DSPy
aikin aiki = dsp. Gudun aiki (
    suna = "inganci_ingantawa",
    ayyuka =[
        dsp.Task("data_tarin", "Tattara bayanai daga rajistan ayyukan da awo"),
        dsp.Task("bayani_preprocessing", "Bayani da aka rigaya don bincike"),
        dsp.Task("model_training", "Horar samfurin ML akan bayanan da aka riga aka sarrafa"),
        dsp.Task("model_deployment", "Aidar da samfurin horarwa a cikin aikin DSPy"),
    ],
)

# ayyana wakilin LangChain
wakili = wakili (
    suna = "efficiency_agent",
    model = "distilbert-base-uncased",
    Quick="Ta yaya zan iya inganta bututun don ingantacciyar inganci?",
)

# Haɗa aikin DSPy tare da wakilin LangChain
workflow.add_agent (wakili)

# Gudanar da aikin
aikin aiki.run ()

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

Anan akwai wasu umarni da snippets da aka fi amfani da su don DSPy da LangChain:

DSPy

`dsp.Workflow (suna = "my_workflow", ayyuka = [...])': Ƙirƙiri sabon aikin DSPy.
`dsp.Task (suna = "my_task", bayanin = "Aikina")': Ƙirƙiri sabon ɗawainiya a cikin aikin DSPy.
workflow.add_agent(wakili): Ƙara wakilin LangChain zuwa aikin DSPy.
workflow.run(): Gudanar da aikin DSPy.
dsp.get_task_output(aiki): Sami fitowar aiki a cikin aikin DSPy.

LangChain

`Wakili (suna = "my_agent", model = "distilbert-base-uncased", m = "My m")': Ƙirƙiri sabon wakilin LangChain.
`agent.respond(prompt="My Quick")': Samu amsa daga wakilin LangChain.
`agent.add_context(context = "My context"): Ƙara mahallin zuwa wakilin LangChain.
`agent.remove_context(context="My mahallin")': Cire mahallin daga wakilin LangChain.
agent.get_model(): Sami samfurin ML na asali na wakilin LangChain.

Kallon gani

Anan ga hangen nesa na aikin DSPy da LangChain ta amfani da Chart.js:

Json { "type": "bar", "data": { "labels": ["Binciken Tushen Tushen", "Ingantacciyar Ingantawa"], "datasets": [ { "label": "DSPy Gudun Aiki", "bayanai": [10, 20], "Bayan launi": ["#4CAF50", "#FF9800"] }, { "label": "Wakilin LangChain", "bayanai": [5, 15], "Bayan launi": ["#2196F3", "#009688"] } ] }, "zaɓi": { "take": { "nuna": gaskiya, "rubutu": "DSPy da LangChain Workflow" }, "ma'auni": { "yAxes": [ { "zama": { "beginAtZero": gaskiya } } ] } } }


Wannan hangen nesa yana nuna adadin ayyuka a cikin kowane aikin DSPy da adadin martani daga kowane wakilin LangChain.

## Kammalawa

A cikin wannan labarin, mun bincika yadda za a yi amfani da DSPy da LangChain don aiwatar da ma'aikatan AI-powered don tushen tushen bincike da ingantaccen ingantaccen bututun haɓaka software. Mun ba da misalai na lamba da abubuwan gani don kwatanta ra'ayoyin, kuma mun haskaka wasu umarni da snippets da aka fi amfani da su don DSPy da LangChain. Ta bin wannan jagorar, masu haɓakawa za su iya ƙirƙirar nasu ayyukan sarrafa kayan aiki na AI ta amfani da DSPy da LangChain.

Ƙirƙirar AI-Powered DevOps Automation tare da DSPy da LangChain