** Ijikọ RAG na LLMs maka Nrụpụta Ngwa Ngwa di omimi: Ntuziaka bara uru ***

** Okwu mmalite**

N'ime afọ ndị na-adịbeghị anya, ngalaba ụlọ ọrụ ngwanrọ enweela nnukwu mgbanwe site na ọbịbịa nke nnukwu ụdị asụsụ (LLMs) na usoro iweghachite-Augmented Generation (RAG). Mmekọrịta dị n'etiti teknụzụ abụọ a nwere ike ịgbanwe ụzọ anyị si emepụta na ịmepụta usoro ngwanrọ. N'isiokwu a, anyị ga-enyocha njikọ nke RAG na LLMs maka nhazi ngwa ngwa ngwa ngwa, gụnyere atụmatụ mmejuputa site na iji ngwá ọrụ nhazi dị ka LangChain na DSPy.

Gịnị bụ RAG?

RAG bụ usoro na-ejikọta ike nke ụdị dabere na Nweghachi na Ọgbọ iji wepụta mpụta ederede dị elu. Echiche bụ isi dị n'azụ RAG bụ iji ụdị eweghachite iji họrọ akwụkwọ ma ọ bụ akụkụ dị mkpa site na nnukwu ụlọ ọrụ, wee jiri usoro ọgbọ mepụta ederede dabere na akụkụ ndị a eweghachiri. Ụzọ a egosila ọganihu dị ukwuu na ọrụ ndị dị ka ịza ajụjụ, nchịkọta ederede, na mkparịta ụka AI.

Gịnị bụ LLM?

LLM bụ ụdị ọgụgụ isi mmadụ a zụrụ azụ na nnukwu data ederede iji wepụta mpụta asụsụ dịka mmadụ. Ụdị ndị a na-adaberekarị na ụlọ ọrụ mgbanwe mgbanwe ma nwetala nsonaazụ ọgbara ọhụrụ n'ọtụtụ ọrụ nhazi asụsụ okike. LLM nwere ike ịzagharị nke ukwuu ma enwere ike iji ya rụọ ọrụ dịka ntụgharị asụsụ, nchịkọta ederede na AI mkparịta ụka.

** Gịnị kpatara iji jikọta RAG na LLM?**

Njikọ RAG na LLM na-enye ọtụtụ uru maka nhazi ngwanrọ siri ike:

1. **Ezizi emeziwanye ***: RAG nwere ike inyere LLM aka ịhọrọ akwụkwọ ma ọ bụ akụkụ dị mkpa site na nnukwu corpus, na-eduga na ntinye ederede ziri ezi na nke na-enye nkọwa.
2. ** Ịbawanye arụmọrụ ***: RAG nwere ike ibelata mkpokọta mkpokọta nke LLM site na ịhọrọ naanị akwụkwọ ma ọ bụ akụkụ dị mkpa, na-eduga na oge nzaghachi ngwa ngwa.
3. ** Enwekwu Mgbanwe ***: Enwere ike iji RAG mee mgbanwe iji gbanwee mmasị na ihe ndị ọrụ chọrọ, na-enye ohere ka LLM mepụta mmepụta ederede nke dị mkpa ma na-adọrọ mmasị.

Usoro mmejuputa

Enwere ọtụtụ atụmatụ mmejuputa iwu iji jikọta RAG na LLM, gụnyere:

1. ** LangChain ***: LangChain bụ ọbá akwụkwọ mepere emepe nke na-enye API dị mfe na nghọta maka iwulite ụdị dabere na RAG. Ọ na-akwado ụdị LLM dị iche iche ma nwee ike iji mejuputa ụkpụrụ ụlọ RAG dị mgbagwoju anya.
2. ** DSPy ***: DSPy bụ ọbá akwụkwọ Python nke na-enye ụzọ dị mfe ma dị irè iji mejuputa ụdị dabeere na RAG. Ọ na-akwado ụdị LLM dị iche iche ma nwee ike iji mejuputa ụkpụrụ ụlọ RAG dị mgbagwoju anya.

** Ihe atụ koodu ***

Nke a bụ ihe atụ koodu dị mfe nke na-egosi otu esi eji LangChain ijikọ RAG na LLM: "Python mbubata langchain si langchain.chains bubata RAGChain si langchain.llms bubata LLMA

llama = llama()

Mepụta ụdị RAGChain

rag_chain = RAGChain (llama, retrieval_model = "t5-obere")

Kọwaa ngwa ngwa

ozugbo = "Gịnị bụ isi obodo France?"

Mepụta mmepụta ederede

mmepụta = rag_chain.run (ngwa ngwa)

ebipụta (mmepụta) `` Ihe atụ koodu a na-egosi otu esi eji LangChain mepụta ụdị RAGChain nke na-ejikọta ihe nlereanya LLAMA na ụdị nweghachi. A na-eji ụdị RAGChain mee ihe iji mepụta mmepụta ederede dabere na ngwa ngwa.

** Iwu 5-10 Kachasị Eji mee ihe

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

1. `langchain.RAGChain(llama, retrieval_model="t5-small")': Na-emepụta ụdị RAGChain nke na-ejikọta ihe nlereanya LLAMA na ụdị iweghachite.
2. llama.run(ngwa ngwa): Na-emepụta mmepụta ederede dabere na ngwa ngwa site na iji ụdị LLAMA.
3. rag_chain.run(prompt): Na-emepụta mmepụta ederede dabere na ngwa ngwa site na iji ụdị RAGChain.
4. langchain.chains.RAGChain (llama, retrieval_model = "t5-small", max_length=1024): Na-emepụta ihe ngosi RAGChain nke jikọtara ihe nlereanya LLAMA na ụdị iweghachite ma debe ogologo ogologo nke ederede mmepụta.
5. `llama.get_input(ngwa ngwa): na-eweghachite ederede ntinye maka ụdị LLAMA.
6. rag_chain.get_output(ngwa ngwa): weghachite ederede mmepụta maka ụdị RAGChain.
7. langchain.chains.RAGChain(llama, retrieval_model="t5-small", batch_size=32): Na-emepụta ihe nlereanya RAGChain nke na-ejikọta ihe nlereanya LLAMA na ihe ngbanwe ma na-edozi nha batch.
8. `llama.get_output(prompt):': weghachite ederede mmepụta maka ụdị LLAMA.
9. rag_chain.get_input(ngwa ngwa): weghachite ederede ndenye maka ụdị RAGChain.
10. langchain.chains.RAGChain(llama, retrieval_model="t5-small", device="cuda"): Na-emepụta ihe ngosi RAGChain nke na-ejikọta ihe nlereanya LLAMA na ụdị nweghachi ma tọọ ngwaọrụ ahụ na CUDA.

** Nlegharị anya ***

Nke a bụ nhụta dị mfe nke ụdị RAGChain na-eji Chart.js: Json { "ụdị": "ogwe", "data": { "labels": ["LLaMA", "RAGChain"], "datasets": [ { "label": "Izi ezi", "data": [0.8, 0.9], "backgroundColor": ["#FF6384", "#36A2EB"] } ] }, "Nhọrọ": { " aha": { "ngosi": eziokwu, "text": "RAGChain Model nke ziri ezi" }, "ọnụego": { "yAxes": [ { "akara aka": { "beginAtZero": eziokwu } } ] } } } `` Ọhụụ a na-egosi izi ezi nke ụdị LLAMA na ụdị RAGChain.

Mkpebi

njikọta nke RAG na LLM na-enye ọtụtụ uru maka nhazi ngwanrọ siri ike, gụnyere izi ezi emelitere, ịba ụba arụmọrụ, yana nkwalite mgbanwe. LangChain na DSPy bụ ngwá ọrụ egwu egwu abụọ a ma ama nke enwere ike iji mejuputa ụdị dabere na RAG. Site n'ịgbaso usoro mmejuputa iwu na ihe atụ koodu enyere n'isiokwu a, ndị mmepe nwere ike ịmepụta ụlọ ọrụ RAG dị mgbagwoju anya na-ejikọta LLM na ụdị iweghachite. Iwu/snippets 5-10 kacha eji eme ihe maka ijikọ RAG na LLM ka nyekwara maka ntụnye aka.