Ijikota LangChain maka Advanced RAG Architectures na ngwa Python

Nlele anya

N'ime afọ ndị na-adịbeghị anya, ngalaba nke Nhazi Asụsụ Eke (NLP) ahụla ọganihu dị ịrịba ama, ọkachasị n'akụkụ sistemụ Retrieval-Augmented Generation (RAG). Usoro ndị a agbanweela ụzọ anyị si aga ọrụ dịka ịza ajụjụ, nchịkọta ederede, na mkparịta ụka AI. LangChain, ọbá akwụkwọ Python dị egwu, apụtala dị ka ngwá ọrụ siri ike maka iwu ụlọ RAG. N'isiokwu a, anyị ga-abanye n'ime ụwa nke LangChain ma nyochaa njikọ ya na usoro ntanetị weebụ dabeere na Python dị ka FastAPI na Flask, ebe ị na-eji ngwá ọrụ nhazi maka iweghachite na nhazi data.

** Gịnị bụ LangChain?**

LangChain bụ ọba akwụkwọ Python mepere emepe emebere iji mee ka mmepe nke sistemụ RAG dị mfe. Ọ na-enye ngwa ọrụ zuru oke na API maka iwulite, ọzụzụ, na itinye ụdị AI, yana ijikọ ha na isi mmalite data dị iche iche na usoro weebụ. Isi ihe LangChain lekwasịrị anya bụ ime ka ndị mmepe nwee ike ịmepụta ụlọ ọrụ RAG nwere ike ịrụ ọrụ nke ọma na nke nwere ike ijikwa ọrụ NLP dị mgbagwoju anya.

** Akụkụ ndị bụ isi nke LangChain ***

1. ** Modular Architecture ***: Nhazi modular nke LangChain na-enye ndị mmepe ohere ijikọta ụdị AI dị iche iche, isi mmalite data, na usoro weebụ, na-eme ka ọ bụrụ nhọrọ dị mma maka iwulite usoro RAG dị mgbagwoju anya.
2. ** Ngwá ọrụ Orchestration ***: LangChain na-enye ụdị ngwá ọrụ nhazi nke na-enyere ndị mmepe aka ijikwa ma hazie nnukwu datasets, yana ijikọ na isi mmalite data dị iche iche na API.
3. ** Nkwado maka Multiple AI Models ***: LangChain na-akwado ụdị ụdị AI dị iche iche, gụnyere ụdị ndị na-agbanwe agbanwe, na-enye ndị mmepe ohere ịhọrọ ụdị kachasị mma maka ihe eji eme ihe.
4. ** Njikọ na Weebụ Frameworks ***: LangChain na-enye njikọ na-enweghị ntụpọ na usoro ntanetị weebụ nke Python na-ewu ewu dị ka FastAPI na Flask, na-eme ka ọ dị mfe itinye usoro RAG dị ka ngwa weebụ.

** Ijikọ LangChain na FastAPI na Flask ***

Iji jikọta LangChain na FastAPI na Flask, anyị kwesịrị ịgbaso usoro ndị a:

Nzọụkwụ 1: Wụnye LangChain

First, we need to install LangChain using pip:

bash pip tinye langchain

Step 2: Create a LangChain Agent

Ọzọ, anyị kwesịrị ịmepụta onye ọrụ LangChain nke ga-eje ozi dị ka isi nke usoro RAG anyị. Nke a bụ ọmụmaatụ otu esi emepụta onye nnọchi anya:

"Python from langchain import Agent

onye nnọchi anya = onye nnọchi anya ( aha = "my_agent", model_name = "my_model", model_type = "transformer", max_length=1024, min_ogologo=128, ) ``

Step 3: Integrate with FastAPI or Flask

Ozugbo anyị mepụtara onye nnọchi anya LangChain anyị, anyị nwere ike ijikọ ya na FastAPI ma ọ bụ Flask. Nke a bụ ọmụmaatụ otu esi etinye ya na FastAPI:

"Python si fastapi mbubata FastAPI sitere na onye nnọchi anya mbubata langchain

ngwa = FastAPI()

Mepụta onye nnọchi anya

onye nnọchi anya = onye nnọchi anya ( aha = "my_agent", model_name = "my_model", model_type = "transformer", max_length=1024, min_ogologo=128, )

@app.get("/ mepụta") async def gene_text (ngwa ngwa: str): # Jiri onye nnọchi anya mepụta ederede mmepụta = agent.generate(ngwa ngwa) laghachi {"mmepụta": mmepụta} ``

N'otu aka ahụ, anyị nwere ike ijikọ LangChain na Flask:

"Python site na mbubata flask, arịrịọ sitere na onye nnọchi anya mbubata langchain

ngwa = Flask (__ aha__)

Mepụta onye nnọchi anya

onye nnọchi anya = onye nnọchi anya ( aha = "my_agent", model_name = "my_model", model_type = "transformer", max_length=1024, min_ogologo=128, )

@app.route("/ mepụta", usoro =["GET") def gene_text(): # Jiri onye nnọchi anya mepụta ederede ozugbo = request.args.get ("ngwa ngwa") output = agent.generate(prompt) laghachi {"mmepụta": mmepụta} ``

** Ngwa orchestration na LangChain ***

LangChain na-enye ngwá ọrụ nhazi dị iche iche na-enyere ndị mmepe aka ijikwa ma hazie nnukwu datasets, yana ijikọ na isi mmalite data dị iche iche na API. Ụfọdụ n'ime isi ihe eji egwu egwu na LangChain gụnyere:

1. ** Isi mmalite data ***: LangChain na-enye ụdị data dị iche iche na-enyere ndị mmepe aka ijikọ na isi mmalite data dị iche iche na API, dị ka ọdụ data, usoro faịlụ, na API weebụ.
2. ** Ndị na-arụ ọrụ data ***: LangChain na-enye usoro nhazi data nke na-enyere ndị mmepe aka ịhazi nnukwu datasets, gụnyere ihicha data, mgbanwe data, na nchịkọta data.
3. ** Ndị na-eme ihe nkiri na-emepụta ihe ***: LangChain na-enye ụdị ndị na-eme ihe ngosi nke na-enyere ndị mmepe aka ijikwa ma tinye ụdị AI, gụnyere ọzụzụ nlereanya, ntinye ihe nlereanya, na nlekota ihe nlereanya.

** Ngosipụta na Chart.js ***

Iji were anya nke uche hụ arụmọrụ nke sistemụ RAG anyị, anyị nwere ike iji Chart.js, ọbá akwụkwọ Javascript ama ama maka imepụta eserese mmekọrịta. Nke a bụ ọmụmaatụ otu esi eji Chart.js were were anya nke uche hụ arụmọrụ nke sistemụ RAG anyị:

javascript const ctx = document.getElementById("MyChart").getContext("2d"); const chart = Chart ọhụrụ (ctx, { ụdị: "akara", data: { akara: ["Jan", "Feb", "Mar", "Apr", "Mee"], ihe ndekọ data: [ { akara: "Izizi", data: [0.8, 0.9, 0.85, 0.88, 0.92], ndabereAgba: "rgba(255, 99, 132, 0.2)", ókè-ala Agba: "rgba(255, 99, 132, 1)", obosara oke: 1, }, ], }, nhọrọ: { akpịrịkpa: { y: { beginAtZero: eziokwu, }, }, }, }

** Iwu 5-10 Kachasị Eji mee ihe

Nke a bụ iwu/snippets 5-10 kacha eji na LangChain:

1. si na langchain ibubata Agent: Bubata klas Agent si LangChain.
2. agent = Agent(...): Mepụta ihe nnọchite anya ọhụrụ.
3. agent.generate(...): Jiri ihe nnọchi anya mepụta ederede.
4. agent.query(...): Jiri onye nnọchi anya jụọ ntọala ihe ọmụma.
5. agent.train(...): zụọ onye nnọchite anya na dataset.
6. agent.deploy(...): Nyefee onye nnọchi anya dị ka ọrụ webụ.
7. agent.monitor(...): Nyochaa arụmọrụ nke onye nnọchi anya.
8. agent.update(...): Jiri data ọhụrụ melite onye nnọchi anya.
9. agent.delete(...): Hichapụ ihe nnọchite anya.
10. agent.save(...): Chekwaa ihe atụ na faịlụ.

Mkpebi

N'isiokwu a, anyị enyochala ụwa nke LangChain na ntinye ya na usoro weebụ dabeere na Python dị ka FastAPI na Flask. Anyị atụlewokwa iji ngwá ọrụ orchestration na LangChain wee nye ihe atụ nke otu esi eji Chart.js iji hụ na arụmọrụ nke usoro RAG anyị. Site na LangChain, ndị mmepe nwere ike wulite ụkpụrụ ụlọ RAG nwere ike ịrụ ọrụ nke ọma na nke nwere ike ijikwa ọrụ NLP dị mgbagwoju anya.