** Ijikọ RAG na LLM maka usoro mmepe ngwanrọ emelitere ***

** Okwu mmalite**

Odida obodo mmepe ngwanrọ na-agbanwe ngwa ngwa, site na ọganihu na ọgụgụ isi mmadụ (AI), mmụta igwe (ML), na nhazi asụsụ eke (NLP). Teknụzụ isi abụọ na-agbanwe ụzọ anyị si emepụta ngwanrọ bụ Retrieval-Augmented Generation (RAG) na Ụdị Asụsụ Large (LLMs). N'edemede a, anyị ga-enyocha njikọ RAG na LLM iji kwalite usoro injinịa ngwanrọ, melite ọgbọ koodu, na ịkwalite mmepụta mmepụta ihe.

Gịnị bụ Ọgbọ eweghachite-Augmented (RAG)?

RAG bụ usoro na-ejikọta ike nke usoro nweghachi na ọgbọ dabere na NLP. Ọ na-agụnye ịweghachite ozi dị mkpa site na nnukwu akụkụ ederede na iji ya mee ka ọgbọ nke ederede ọhụrụ dịkwuo elu. E gosiputara ụzọ a ka ọ dị irè nke ukwuu n'ọtụtụ ọrụ NLP, gụnyere ịza ajụjụ, nchịkọta ederede, na mmepụta koodu.

Gịnị bụ Ụdị Asụsụ buru ibu (LLM)?

LLM bụ ụdị netwọkụ akwara emebere iji hazie yana mepụta asụsụ dịka mmadụ. A na-azụkarị ha na nnukwu dataset nke ederede ma nwee ike ịhazi ya nke ọma maka ọrụ ụfọdụ dị ka ntụgharị asụsụ, nchịkọta ederede, na ọgbọ koodu. Ndị LLM enwetala nsonaazụ ọgbara ọhụrụ n'ọtụtụ ọrụ NLP ma na-eji ya eme ihe na ngwa dịka chatbots, ndị enyemaka mebere, na sistemụ ntụgharị asụsụ.

** Ijikọ RAG na LLM maka usoro mmepe ngwanrọ emelitere ***

Njikọ RAG na LLM nwere ikike ịgbanwe ụzọ anyị si emepụta ngwanrọ. Site na ijikọta ike nke usoro nweghachi na usoro ọgbọ, RAG nwere ike inyere LLM aka ịmepụta koodu ziri ezi na nke dabara adaba. Nke a nwere ike ibute mmụba dị ukwuu na nrụpụta onye nrụpụta, ogo koodu, yana usoro mmepe ngwanrọ n'ozuzu ya.

** Ngwa orchestration: LangChain na DSPy ***

Iji jikọta RAG na LLM, anyị chọrọ ngwá ọrụ orchestration nke nwere ike ijikwa ọsọ nke data na ịchịkwa mmezu nke RAG na LLM components. Ngwá ọrụ egwu egwu abụọ a ma ama nke enwere ike iji maka ebumnuche a bụ LangChain na DSPy.

** LangChain ***

LangChain bụ ọba akwụkwọ Python mepere emepe nke na-enye API dị mfe ma nwee nghọta maka iwulite ngwa dabere na RAG. Ọ na-enye ndị mmepe ohere ijikọ RAG na LLM na ngwa NLP ndị ọzọ iji mepụta usoro mmepe ngwanrọ siri ike.

DSPy

DSPy bụ ọba akwụkwọ Python mepere emepe nke na-enye API ọkwa dị elu maka iwulite ngwa dabere na RAG. Emebere ya ka ọ rụọ ọrụ n'enweghị nsogbu na LLMs na akụrụngwa NLP ndị ọzọ, na-eme ka ọ bụrụ ezigbo nhọrọ maka ijikọ RAG na LLM.

** Ọmụmaatụ koodu: iji LangChain jikọọ RAG na LLM.

Nke a bụ ihe atụ koodu dị mfe nke na-egosi otu esi ejikọta RAG na LLM site na iji LangChain: "Python mbubata langchain si langchain.chains bubata RAGChain

rag_model = langchain.RAG( index=langchain.Index( aha = "my_index", akwụkwọ =["akwụkwọ1", "akwụkwọ2", "akwụkwọ3"], ), retriever=langchain.Retriever( aha = "my_retriever", ụdị = "my_retriever_model", ), jenerato=langchain.Onye na-emepụta ihe( aha = "my_generator", ụdị = "my_generator_model", ), )

Kọwaa ụdị LLM

llm_model = langchain.LLM( aha = "my_llm", ụdị = "my_llm_model", )

Mepụta yinye RAG

rag_chain = RAGChain ( aha = "my_rag_chain", ụdị = rag_model, llm=llm_model, )

Jiri agbụ RAG mepụta koodu

koodu = rag_chain.generate( input_text="Dee ọrụ na-ewe ọnụọgụ abụọ dị ka ntinye wee weghachi nchikota ha", )

ebipụta (koodu) `` Ihe atụ koodu a na-egosi otu esi akọwa ụdị RAG site na iji LangChain, kọwapụta ụdị LLM, mepụta ụdọ RAG, ma jiri yinye mepụta koodu.

** Iwu 5-10 Kachasị Eji mee ihe

Nke a bụ iwu/snippets 5-10 kacha eji eme ihe maka ijikọ RAG na LLM site na iji LangChain na DSPy:

1. `langchain.RAG()': Na-emepụta ihe atụ RAG.
2. `langchain.Index()': Na-emepụta ihe atụ index maka ịchekwa na iweghachi akwụkwọ.
3. `langchain.Retriever()': Na-emepụta ihe atụ eweghachi maka iweghachite akwụkwọ ndị dị mkpa.
4. `langchain.Generator()': Na-emepụta ihe atụ generator maka ịmepụta ederede ọhụrụ.
5. `langchain.LLM()': Na-emepụta ihe atụ LLM.
6. `RAGChain()': Na-emepụta ihe atụ yinye RAG.
7. rag_chain.generate(): Na-eji agbụ RAG mepụta koodu.
8. llm_model.generate(): Na-eji ụdị LLM wepụta koodu.
9. index.add_document(): Na-agbakwunye akwụkwọ na ndeksi.
10. retriever.retrieve(): Na-eweghachite akwụkwọ ndị dị mkpa site na iji ihe eweghachite.

** Nlegharị anya ***

Nke a bụ nleba anya nke ngwa RAG na LLM na-eji Chart.js: Json { "ụdị": "ogwe", "data": { "labels": ["RAG", "LLM"], "datasets": [ { "label": "Components", "data": [10, 20], "backgroundColor": ["#4CAF50", "#FF9800"] } ] }, "Nhọrọ": { " aha": { "ngosi": eziokwu, "ederede": "RAG na Ngwa LLM" }, "ọnụego": { "yAxes": [ { "ngosi": eziokwu, "beginAtZero": eziokwu } ] } } } `` Ọhụụ a na-egosi ọnụọgụ nke akụrụngwa na ụdị RAG na LLM.

Mkpebi

Njikọ RAG na LLM nwere ikike ịgbanwe ụzọ anyị si emepụta ngwanrọ. Site na ijikọta ike nke usoro nweghachi na usoro ọgbọ, RAG nwere ike inyere LLM aka ịmepụta koodu ziri ezi na nke dabara adaba. Nke a nwere ike ibute mmụba dị ukwuu na nrụpụta onye nrụpụta, ogo koodu, yana usoro mmepe ngwanrọ n'ozuzu ya. Site na ngwa orchestration dị ka LangChain na DSPy, ndị mmepe nwere ike ijikọ RAG na LLM na ngwa NLP ndị ọzọ iji mepụta usoro mmepe ngwanrọ siri ike.