Na-ahazi pipeline DevOps nwere AI-Powered na DSPy na LangChain maka nyocha na mkpebi ihe mberede. | B4N1

Na-ahazi pipeline DevOps nwere AI-Powered na DSPy na LangChain maka nyocha na mkpebi ihe mberede.

3 months ago · b4n1 Read time: 4 min

Okwu mmalite

Mgbagwoju anya nke sistemu ngwanrọ ọgbara ọhụrụ na-eto eto ebutela mmụba na ugboro na oke ihe omume, na-eme nyocha ihe omume na mkpebi siri ike bụrụ akụkụ dị egwu nke DevOps. Ọgụgụ isi (AI) na mmụta igwe (ML) apụtala dị ka ngwaọrụ siri ike iji kwalite pipeline DevOps, na-eme ka AI na-ebugharị akpaaka na orchestration iji chọpụta na dozie ihe omume tupu ha emetụta azụmahịa ahụ. N'isiokwu a, anyị ga-enyocha otu esi emejuputa pipeline DevOps nke AI kwadoro site na iji DSPy na LangChain, na-elekwasị anya na nchọpụta ihe omume na mkpebi siri ike site na iji AIOps na RAG ụdị, na ijikọta na AWS DevOps Agent maka nchọpụta ihe merenụ na-akpaghị aka.

Nchịkọta nke DSPy na LangChain

DSPy bụ ọba akwụkwọ Eke Ọgba na-enye ụzọ dị mfe ma dịkwa mma iji wuo na ibunye pipeline DevOps nke AI kwadoro. Ọ na-enye ndị mmepe ohere ijikọ ụdị AI na ML dị iche iche, gụnyere nhazi asụsụ eke (NLP), ọhụụ kọmputa, na nyocha amụma, n'ime pipeline DevOps ha. LangChain, n'aka nke ọzọ, bụ ọbá akwụkwọ Python nke na-enyere ndị mmepe aka iwulite na ibuga ụdị AI buru ibu site na iji ihe owuwu modular na scalable.

Na-eji DSPy na LangChain arụ ọrụ AI-Powered DevOps Pipeline

Iji mejuputa pipeline DevOps nke AI kwadoro na DSPy na LangChain, anyị kwesịrị ịgbaso usoro ndị a:

  1. ** Nchịkọta data ***: Anakọta data dị mkpa sitere na isi mmalite dị iche iche, dị ka ndekọ, metrics, na faịlụ nhazi.
  2. ** Nhazi data ***: Hazie data anakọtara iji kwado ya maka nyocha na nhazi.
  3. ** Ọzụzụ Nlereanya ***: zụọ ụdị AI na ML site na iji data emebere ya iji chọpụta na ịkọ ihe merenụ.
  4. ** Nbugharị Model ***: Tinye ụdị a zụrụ azụ n'ime pipeline DevOps site na iji DSPy na LangChain.
  5. Nnyocha ihe mberede: Jiri ụdị ewepụtara mee nyocha ma chọpụta ihe kpatara ya.
  6. **Mkpebi ihe mberede ***: Jiri nghọta enwetara site na nyocha ihe mere iji dozie ihe merenụ nke ọma.

Usoro Ihe Nlereanya: Iji DSPy na LangChain tinye Model RAG Dị Mfe

Nke a bụ ihe atụ koodu dị mfe nke na-egosi otu esi etinye ụkpụrụ RAG (Root Cause Analysis) site na iji DSPy na LangChain: `` Python mbubata dspy si langchain bubata LLMChain

Budata data emechara

data = pd.read_csv("data.csv")

Kọwaa ụdị RAG

rag_model = dspy.RAGModel( input_features=["log_level", "error_message"], mmepụta_features=["mgbọrọgwụ_cause"] )

Zụlite ụdị RAG

rag_model.train(data)

Nyefee ụdị RAG

llm_chain = LLMChain ( ụdị = "davinci:002", max_length=2048 ) rag_model.deploy(llm_chain)

Jiri ụdị RAG ewesara mee nyocha ihe mere

Event_data = pd.DataFrame({ "log_level": ["ERROR"], "error_message": ["Enweghị ihe na-ezighi ezi"] }) mgbọrọgwụ_cause = rag_model.predict(incident_data) ebipụta (mgbọrọgwụ_akpata) `` Ihe atụ koodu a na-egosi otu esi emejuputa ụdị RAG dị mfe site na iji DSPy na LangChain. Ụdị RAG na-ewere ọkwa ndekọ na ozi njehie dị ka njirimara ntinye ma buru amụma ihe kpatara ihe ahụ merenụ.

Top 5-10 Iwu/Snippets kacha eji

Nke a bụ iwu/snippets 5-10 kacha eji eme ihe maka DSPy na LangChain:

  1. dspy.RAGModel(ntinye_features, mmepụta_features): Kọwaa ụdị RAG nwere atụmatụ ntinye na nrụpụta akọwapụtara.
  2. dspy.RAGModel.train(data): zụọ ụdị RAG site na iji data enyere.
  3. dspy.RAGModel.deploy(llm_chain): Jiri LangChain LLMChain wepụta ụdị RAG zụrụ azụ.
  4. llm_chain = LLMChain(ụdị, max_length): Mepụta LangChain LLMChain nwere ụdị akọwapụtara na ogologo ogologo.
  5. rag_model.predict(data): Jiri ụdị RAG ewesara buo amụma ihe kpatara ihe merenụ.
  6. dspy.DataPreprocessor(input_data): Kọwaa a data preprocessing pipeline iji DSPy.
  7. `dspy.ModelTrainer(model, data): zụọ ihe AI ​​ma ọ bụ ML nlereanya iji DSPy.
  8. dspy.ModelDeployer(model, llm_chain): Nyefee ihe nlereanya a zụrụ azụ site na iji DSPy na LangChain.
  9. langchain.LLMChain.predict(input_data): Jiri LangChain LLMChain buru amụma mmepụta nke ihe nlereanya.
  10. dspy.RAGModel.evaluate(data): Nyochaa arụmọrụ nke ihe nlereanya RAG site na iji DSPy.

Nlere anya

Nke a bụ ọmụmaatụ nhụhụta site na iji Chart.js: Json { "ụdị": "ogwe", "data": { "labels": ["Ụdị Ụdị A", "Ụdị Ụdị B", "Ụdị Ụdị C"], "datasets": [ { "label": "Mkpọrọgwụ ihe kpatara A", "data": [10, 15, 7], "backgroundColor": "rgba(255, 99, 132, 0.2)", "BorderColor": "rgba(255, 99, 132, 1)", "BorderWiidth": 1 }, { "label": "Ihe kpatara B", "data": [8, 12, 9], "backgroundColor": "rgba(54, 162, 235, 0.2)", "BorderColor": "rgba(54, 162, 235, 1)", "BorderWiidth": 1 }, { "label": "Mkpọrọgwụ ihe kpatara C", "data": [14, 18, 11], "backgroundColor": "rgba(255, 206, 86, 0.2)", "BorderColor": "rgba(255, 206, 86, 1)", "BorderWiidth": 1 } ] }, "Nhọrọ": { " aha": { "ngosi": eziokwu, "text": "Ntụle ihe kpatara mgbọrọgwụ" }, "ọnụego": { "yAxes": [ { "akara aka": { "beginAtZero": eziokwu } } ] } } } `` Ọhụụ a na-egosiputa nkesa isi ihe kpatara ụdị ihe omume dị iche iche.

Mmechi

N'isiokwu a, anyị nyochara otu esi emejuputa pipeline DevOps nke AI kwadoro site na iji DSPy na LangChain, na-elekwasị anya na nchọpụta ihe omume na mkpebi siri ike site na iji AIOps na RAG ụdị, na ijikọta na AWS DevOps Agent maka nchọpụta ihe mberede akpaaka. Anyị gosipụtara ihe atụ koodu dị mfe nke na-eme ihe ngosi RAG site na iji DSPy na LangChain, ma nye ndepụta nke 5-10 kacha elu iwu / snippets maka DSPy na LangChain. Anyị tinyekwara ihe atụ nhụta site na iji Chart.js. Site n'ịgbaso usoro ndị akọwapụtara n'isiokwu a, ndị mmepe nwere ike wulite ma bufee pipeline DevOps nke AI kwadoro ma chọpụta ma dozie ihe omume nke ọma, na-eme ka arụmọrụ zuru oke na ntụkwasị obi nke sistemụ ngwanrọ ha.

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