Mepụta onye nnọchi anya AI site na iji DSPy | B4N1

Mepụta onye nnọchi anya AI site na iji DSPy

8 months ago · b4n1 Read time: 4 min

** Ijikọ DSPy na LangChain maka mmepe ngwanrọ AI-chụpụrụ dị elu**

** Okwu mmalite**

Mpaghara mmepe ngwanrọ na-agbanwe ngwa ngwa, yana nkwubi okwu nke Artificial Intelligence (AI) na usoro mmụta igwe (ML). Ọbá akwụkwọ Python abụọ a ma ama, DSPy na LangChain, na-enweta traction na oghere mmepe ngwanrọ AI na-akwalite. N'isiokwu a, anyị ga-abanye n'ime ntinye nke DSPy na LangChain, na-enyocha nhazi ha nke ndị ọrụ AI na Large Language Models (LLMs) iji kwalite omume injinịa software na ịhazi usoro ọrụ.

Gịnị bụ DSPy?

DSPy bụ ọba akwụkwọ Python nke na-enye otu ngwaọrụ maka iwulite na ijikwa ndị ọrụ AI. Ọ na-enye ndị mmepe ohere ịmepụta, zụọ, na ibunye ndị ọrụ AI nwere ike ịrụ ọrụ dị iche iche, dị ka nyocha data, nhazi asụsụ okike, na ime mkpebi. Ewubere DSPy n'elu ọba akwụkwọ PyTorch a ma ama ma na-enye API dị mfe ma nwee nghọta maka iwulite ndị nnọchi anya AI.

** Gịnị bụ LangChain?**

LangChain bụ ọba akwụkwọ Eke Ọgba nke na-enye otu ngwaọrụ maka iwulite na ijikwa LLM. Ọ na-enye ndị mmepe ohere ịmepụta, zụọ, na ibunye LLM nke nwere ike ịrụ ọrụ dị iche iche, dị ka ọgbọ ederede, azịza ajụjụ, na ntụgharị asụsụ. Ewubere LangChain n'elu ọbá akwụkwọ Hugging Face Transformers na-ewu ewu ma na-enye API dị mfe ma nwee nghọta maka iwulite LLM.

** Ijikọ DSPy na LangChain ***

Ngwakọta nke DSPy na LangChain na-enye ndị mmepe ohere ịmepụta ngwa ngwa ngwa ngwa AI na-ebugharị nke nwere ike ịrụ ọrụ dị iche iche. Site na ijikọ ike nke ndị ọrụ AI na LLM, ndị mmepe nwere ike ịmepụta ngwa ndị nwere ike nyochaa data, mepụta ederede, ma mee mkpebi.

Nke a bụ ngọngọ koodu dị mfe nke gosipụtara njikọ nke DSPy na LangChain: "Python mbubata dspy mbubata langchain

onye nnọchi anya = dspy.Agent()

Mepụta LLM site na iji LangChain

llm = langchain.LLM()

Jiri onye nnọchi anya AI nyocha data

data = {"aha": "John", "afọ": 30} analysis = agent.analyze(data)

Jiri LLM mepụta ederede dabere na nyocha

ederede = llm.generate_text(nyocha)

Bipụta ederede emepụtara

ebipụta(ederede) `` Ihe ngọngọ koodu a na-egosi otu esi emepụta onye ọrụ AI site na iji DSPy, LLM na-eji LangChain, ma jiri ha ọnụ nyochaa data na ịmepụta ederede.

** Orchestration nke AI Agents na LLMs ***

Nhazi nke ndị ọrụ AI na LLM bụ akụkụ dị oke mkpa nke ijikọ DSPy na LangChain. Site na ijikọ ike nke ndị ọrụ AI na LLM, ndị mmepe nwere ike ịmepụta ngwa nwere ike ịrụ ọrụ dị iche iche, dịka:

  • Nyocha data: Ndị ọrụ AI nwere ike nyochaa data, ebe LLM nwere ike iwepụta ederede dabere na nyocha.
  • Ọgbọ ederede: LLM nwere ike iwepụta ederede, ebe ndị ọrụ AI nwere ike nyochaa ederede wee mee mkpebi.
  • Mkpebi: Ndị ọrụ AI nwere ike ịme mkpebi dabere na nyocha data, ebe LLM nwere ike ịnye ndụmọdụ.

Nke a bụ nhụta nke nhazi nke ndị ọrụ AI na LLM na-eji Chart.js: Json { "ụdị": "ogwe", "data": { "labels": ["Nnyocha data", "Ọgbọ ederede", "Mkpebi ime"], "datasets": [ { "label": "AI Agents", "data": [10, 5, 8], "backgroundColor": "rgba(255, 99, 132, 0.2)", "BorderColor": "rgba(255, 99, 132, 1)", "BorderWiidth": 1 }, { "label": "LLM", "data": [8, 10, 5], "backgroundColor": "rgba(54, 162, 235, 0.2)", "borderColor": "rgba(54, 162, 235, 1)", "BorderWiidth": 1 } ] }, "Nhọrọ": { "ọnụego": { "yAxes": [ { "akara aka": { "beginAtZero": eziokwu } } ] } } } `` Ọhụụ a na-egosi otu esi ahazi ndị ọrụ AI na LLM ka ha rụọ ọrụ dị iche iche.

** Iwu 5-10 Kachasị Eji mee ihe

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

  1. dspy.Agent(): Na-emepụta onye nnọchi anya AI site na iji DSPy.
  2. langchain.LLM(): Na-eji LangChain na-emepụta LLM.
  3. agent.analyze(data): Nyochaa data site na iji onye nnọchite AI.
  4. lm.generate_text(analysis): Na-emepụta ederede dabere na nyocha site na iji LLM.
  5. agent.make_decision(data): Na-eme mkpebi dabere na nyocha data site na iji onye nnọchi anya AI.
  6. `llm.get_recommendations(analysis):' na-enweta ndụmọdụ dabere na nyocha site na iji LLM.
  7. dspy.train_agent(data): na-azụ onye nnọchi anya AI site na iji data enyere.
  8. `langchain.train_llm(data): na-azụ LLM site na iji data enyere.
  9. dspy.deploy_agent(): Na-ebunye onye nnọchi anya AI.
  10. `langchain.deploy_llm()': Na-ebuga LLM.

Mkpebi

Njikọ nke DSPy na LangChain na-enye ngwá ọrụ dị ike maka iwulite ngwa ngwanrọ AI na-akwalite elu. Site na ijikọ ike nke ndị ọrụ AI na LLM, ndị mmepe nwere ike ịmepụta ngwa ndị nwere ike nyochaa data, mepụta ederede, ma mee mkpebi. Nhazi nke ndị ọrụ AI na LLM bụ akụkụ dị oke mkpa nke ijikọta DSPy na LangChain, na site n'ịghọta otu esi eji ngwá ọrụ ndị a ọnụ, ndị mmepe nwere ike ịmepụta ngwa ndị nwere ike ịrụ ọrụ dị iche iche.

** Ọrụ n'ọdịnihu ***

Ọrụ n'ọdịnihu na ijikọ DSPy na LangChain gụnyere:

  • Mepụta ndị nnọchi anya AI dị elu yana LLM nwere ike ịrụ ọrụ siri ike.
  • Ịkwalite nhazi nke ndị ọrụ AI na LLM iji mee ka ọ dịrị ndị mmepe mfe iji ngwaọrụ ndị a ọnụ.
  • Mepụta ngwa ndị ọzọ na-eji DSPy na LangChain gosipụta ike ha.

** Ntụaka ***

Mara: Ihe mgbochi koodu na nlebanya ejiri mee ihe n'akụkọ a bụ maka ebumnuche ngosi naanị na ọ nwere ike ọ gaghị abụ koodu n'ezie ma ọ bụ ihe nlere anya ejiri na ngwa ụwa n'ezie.

Have Questions?

Let's Talk

Contact me on LinkedIn