E hoʻouka i nā moʻolelo pūnaewele | B4N1

E hoʻouka i nā moʻolelo pūnaewele

3 months ago · b4n1 Read time: 6 min

Ka hoʻonohonoho ʻana i nā Pipelines AI-Powered DevOps me DSPy a me LangChain no Proactive Incident Investigation and Resolution in Edge Computing Environments

Hoʻolauna

ʻO ka piʻi ʻana o ka paʻakikī o nā ʻōnaehana polokalamu hou, i hui pū ʻia me ka piʻi nui ʻana o ka noi no ka hoʻoili ʻana i ka ʻikepili a me ka nānā ʻana i ka manawa maoli, ua alakaʻi i ka puka ʻana o nā kaiapuni computing edge. ʻO kēia mau kaiapuni, kahi i hōʻike ʻia e ka hele ʻana o nā ʻōnaehana puʻupuʻu, autonomous, a heterogeneous, e hoʻokau i nā pilikia koʻikoʻi no nā hui DevOps. Ma kēia ʻatikala, e ʻimi mākou pehea e hoʻohana ai i nā holomua hou loa i nā mea hana loiloi code a AI-powered a me nā platform AIOps, ʻo ia hoʻi ʻo DSPy a me LangChain, e hiki ai i ka hoʻokolokolo hanana hanana a me ka hoʻonā ʻana i nā kaiapuni o ka ʻaoʻao.

Ka Intersection o AI, DevOps, a me Edge Computing

Hōʻike ʻia nā kaiapuni ʻo Edge e ka hele ʻana o nā ʻōnaehana puʻupuʻu, autonomous, a heterogeneous, hiki iā ia ke paʻakikī i nā hui DevOps e ʻike a hoʻoholo i nā hanana i ka manawa kūpono a me ka maikaʻi. No ka hoʻoponopono ʻana i kēia pilikia, ua puka mai nā mea hana loiloi code AI-powered a me nā kahua AIOps ma ke ʻano he hopena hoʻohiki. Hoʻohana kēia mau mea hana i nā algorithm aʻo mīkini a me nā ʻenehana hana ʻōlelo kūlohelohe e kālailai i nā log system, metric performance, a me nā kumu ʻikepili ʻē aʻe, e hiki ai i nā hui DevOps ke ʻike i nā anomalies a wānana i nā hanana ma mua o ka hiki ʻana mai.

DSPy: He Hale Waihona Puke ʻo Python no ka loiloi ʻana i nā code AI-Powered

ʻO DSPy kahi waihona Python e hiki ai i nā mea hoʻomohala ke hoʻohana i ka mana o AI-powered code review tool i kā lākou DevOps pipelines. Hāʻawi ʻo DSPy i kahi API maʻalahi a intuitive no ka hoʻohui ʻana i nā algorithm aʻo mīkini a me nā ʻenehana hana ʻōlelo kūlohelohe i loko o nā kahe hana loiloi code. Me DSPy, hiki i nā mea hoʻomohala ke kālailai i nā logs system, metric performance, a me nā kumu ʻikepili ʻē aʻe e ʻike ai i nā anomalies a wānana i nā hanana hiki ke hana.

LangChain: He Kiʻi no ke kūkulu ʻana i nā kahua AIOps i hoʻoikaika ʻia e AI

He hoʻolālā ʻo LangChain no ke kūkulu ʻana i nā paepae AIOps i hoʻoikaika ʻia e AI e hiki ai i ka hoʻokolokolo hanana hanana a me ka hoʻonā ʻana i nā kaiapuni computing edge. Hāʻawi ʻo LangChain i kahi pūʻulu o nā ʻāpana i kūkulu mua ʻia a me nā API no ka hoʻohui ʻana i nā algorithm aʻo mīkini, nā ʻenehana hana ʻōlelo kūlohelohe, a me nā mea hana AI-powered ʻē aʻe i nā platform AIOps. Me LangChain, hiki i nā mea hoʻomohala ke kūkulu i nā paepae AIOps maʻamau e hoʻohana i nā holomua hou loa i nā mea hana loiloi code a AI-powered a me nā platform AIOps.

Ka hoʻonohonoho ʻana i nā Pipeline AI-Powered DevOps me DSPy a me LangChain

No ka hoʻonohonoho ʻana i nā pipelines DevOps me DSPy a me LangChain, hiki i nā mea hoʻomohala ke hahai i kēia mau ʻanuʻu:

  1. Hoʻohui i ka DSPy i loko o ka pipeline DevOps: Hiki i nā mea hoʻomohala ke hoʻohui i ka DSPy i loko o ka pipeline DevOps ma o ka hoʻohana ʻana i ka DSPy API no ke kālailai ʻana i nā moʻolelo pūnaewele, nā ana hana, a me nā kumu ʻikepili ʻē aʻe.
  2. E hoʻohana i ka LangChain e kūkulu i kahi papahana AIOps maʻamau: Hiki i nā mea hoʻomohala ke hoʻohana i ka LangChain e kūkulu i kahi kahua AIOps maʻamau e hoʻohana ana i nā holomua hou loa i nā mea hana loiloi code a me AIOps platforms.
  3. Hoʻohui i ka paepae AIOps i loko o ka pipeline DevOps: Hiki i nā mea hoʻomohala ke hoʻohui i ka platform AIOps i loko o ka pipeline DevOps ma o ka hoʻohana ʻana i ka API LangChain no ka nānā ʻana i nā log system, metric performance, a me nā kumu ʻikepili ʻē aʻe.

Ka Laʻana Hoʻomaʻamaʻa: Ke hoʻohana nei iā DSPy no ka nānā ʻana i nā moʻolelo Pūnaewele

Eia kahi hiʻohiʻona maʻalahi o ka hoʻohana ʻana iā DSPy no ka nānā ʻana i nā log system:

lawe mai i ka dspy


logs = dspy.load_logs('system_logs.log')

# E nānā i nā ʻōnaehana me ka hoʻohana ʻana i kahi algorithm aʻo mīkini
anomalies = dspy.analyze_logs(logs, algorithm='MLP')

# Paʻi i nā anomalies
paʻi (anomalies)

Ma kēia laʻana, hoʻouka mākou i nā moʻolelo ʻōnaehana me ka hana dspy.load_logs, e kālailai i nā lāʻau me ka mīkini aʻo algorithm (MLP) me ka hoʻohana ʻana i ka hana dspy.analyze_logs, a paʻi i nā anomalies.

** 5-10 kiʻekiʻe loa i hoʻohana ʻia i nā kauoha/nā ʻāpana**

Eia nā 5-10 kiʻekiʻe loa i hoʻohana ʻia i nā kauoha / snippets no DSPy a me LangChain:

  1. dspy.load_logs('system_logs.log'): Hoʻouka i nā moʻolelo pūnaewele.
  2. dspy.analyze_logs(logs, algorithm='MLP'): Nānā i nā moʻolelo ʻōnaehana me ka hoʻohana ʻana i kahi algorithm aʻo mīkini.
  3. langchain.build_aiops_platform(): Ke kūkulu nei i kahi paepae AIOps maʻamau me LangChain.
  4. langchain.integrate_aiops_platform(): Hoʻohui i ka paepae AIOps i loko o ka pipeline DevOps.
  5. dspy.visualize_anomalies(anomalies): Nānā i nā anomalies me ka pakuhi.
  6. langchain.use_ml_algorithm(logs, algorithm='SVM'): Hoʻohana i ka mīkini aʻo algorithm (SVM) no ke kālailai ʻana i nā moʻolelo pūnaewele.
  7. dspy.print_anomalies(anomalies): Paʻi i nā anomalies.
  8. langchain.use_nlp_techniques(logs, techniques=['NER', 'POS']): Hoʻohana i nā ʻenehana hoʻoponopono ʻōlelo kūlohelohe (NER, POS) no ka nānā ʻana i nā moʻolelo pūnaewele.
  9. dspy.save_anomalies(anomalies, 'anomalies.csv'): Mālama i nā anomali i kahi waihona CSV.
  10. langchain.use_custom_aiops_platform(): Hoʻohana i kahi paepae AIOps maʻamau i kūkulu ʻia me LangChain.

Hōʻikeʻike

Eia kekahi laʻana o ka nānā ʻana i nā anomalies me ka hoʻohana ʻana i ka pakuhi:

{
  "type": "pā",
  "ʻikepili": {
    "labels": ["Anomaly 1", "Anomaly 2", "Anomaly 3"],
    "mau ʻikepili": [
      {
        "label": "Anomalies",
        "ʻikepili": [10, 20, 30],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "borderColor": "rgba(255, 99, 132, 1)",
        "BorderWidth": 1
      }
    ]
  },
  "koho": {
    "poʻo inoa": {
      "hōʻike": ʻoiaʻiʻo,
      "text": "Anomalies"
    },
    "kaʻao": {
      "hōʻike": ʻoiaʻiʻo
    }
  }
}

Ma kēia laʻana, hoʻohana mākou i ka waihona Chart.js e hana i kahi pakuhi pahu e ʻike i nā anomalies.

Hoʻoholo

Ma kēia ʻatikala, ua ʻimi mākou pehea e hoʻohana ai i nā holomua hou loa i nā hāmeʻa loiloi code powered AI a me nā platform AIOps, ʻo ia hoʻi ʻo DSPy a me LangChain, e hiki ai i ka hoʻokolokolo hanana hanana a me ka hoʻonā ʻana i nā kaiapuni o ka ʻaoʻao. Hāʻawi mākou i kahi hiʻohiʻona kūpono o ka hoʻohana ʻana iā DSPy no ka nānā ʻana i nā log system a nānā i nā anomalies me ka hoʻohana ʻana i ka pakuhi. Hāʻawi pū mākou i kahi papa inoa o nā kauoha / snippets kiʻekiʻe 5-10 i hoʻohana nui ʻia no DSPy a me LangChain. Ma ka hahai ʻana i nā ʻanuʻu i hōʻike ʻia ma kēia ʻatikala, hiki i nā mea hoʻomohala ke kūkulu i nā platform AIOps maʻamau e hoʻohana i nā holomua hou loa i nā hāmeʻa loiloi code AI-powered a me nā platform AIOps, e hiki ai i ka hoʻokolokolo hanana hanana a me ka hoʻonā ʻana i nā kaiapuni computing edge.

Have Questions?

Let's Talk

Contact me on LinkedIn