Okwu mmalite

AI nwere ike ịkọwa (XAI) abụrụla akụkụ dị mkpa nke ụkpụrụ ụlọ ọrụ ọgbara ọhụrụ, na-eme ka ndị otu nwee ntụkwasị obi na ịghọta mkpebi ndị sistemụ AI na-achị mere. Agbanyeghị, imejuputa XAI na sistemụ sọftụwia dị mgbagwoju anya nwere ike bụrụ ọrụ na-akụda mmụọ, ọkachasị mgbe ị na-emekọ ihe gbasara ụlọ ọrụ nnukwu ụlọ ọrụ. N'isiokwu a, anyị ga-enyocha otú LangChain na DSPy nwere ike isi mee ka ike nke XAI dị na ụlọ ọrụ mmepụta ihe, na-elekwasị anya na-eme mkpebi AI.

Kedu ihe AI ​​(XAI) nwere ike ịkọwa?

AI nwere ike ịkọwapụta na-ezo aka n'ikike nke sistemụ AI iji nye nkọwa doro anya na nkọwa maka mkpebi na amụma ha. Nke a dị mkpa karịsịa na ngalaba dị elu dị ka ahụike, ego, na njem njem, ebe mkpebi AI na-ebute nwere ike inwe nnukwu nsonaazụ.

LangChain: Usoro maka iwulite sistemụ AI nwere ike ịkọwa

LangChain bụ usoro mepere emepe maka iwulite sistemụ AI nwere ike ịkọwa. Ọ na-enye otu ngwaọrụ na ọba akwụkwọ maka ịmepụta ụdị nke nwere ike inye nkọwa doro anya na nkọwa maka mkpebi ha. Ewubere LangChain n'elu usoro mmụta miri emi ama ama dị ka TensorFlow na PyTorch, na-eme ka ọ dị mfe ijikọ na sistemụ AI dị adị.

DSPy: Ọbá akwụkwọ maka iwulite ụdị AI nwere ike ịkọwa

DSPy bụ ọba akwụkwọ maka iwulite ụdị AI nwere ike ịkọwa site na iji usoro PyTorch. Ọ na-enye otu ụdị na ngwa arụrụla mbụ maka imepụta sistemu AI nwere ike ịkọwa, gụnyere ụdị maka nhazi onyonyo, nhazi asụsụ okike na ndị ọzọ.

Na-arụ ọrụ AI nwere ike ịkọwa na nhazi ụlọ ọrụ na LangChain na DSPy

Ịmejuputa XAI na ụlọ ọrụ ụlọ ọrụ chọrọ nghọta miri emi nke usoro AI dị n'okpuru na ike ijikọ ihe XAI n'ime usoro ndị dị ugbu a. N'ebe a, anyị ga-enyocha ihe atụ dị mfe nke otu esi eme XAI na usoro ngwanrọ mgbagwoju anya site na iji LangChain na DSPy.

Ọmụmaatụ: Iwulite ihe nlere AI nwere ike ịkọwa maka nhazi onyonyo

N'ihe atụ a, anyị ga-ewu ihe atụ AI nwere ike ịkọwa maka nhazi onyonyo site na iji LangChain na DSPy. Anyị ga-eji nhazi netwọkụ neural convolutional dị mfe (CNN) wee jikọta ihe XAI site na iji LangChain.

"Python mbubata ọwa mbubata torch.nn ka nn mbubata langchain si langchain mbubata LLAMA

Kọwaa ihe owuwu CNN

klas CNN(nn.Module): def init(onwe): super(CNN, onwe).init() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d (10, 20, kernel_size=5) self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear (50, 10)

gbapụ n'ihu (onwe, x):
    x = ọkụ.relu (self.conv1(x))
    x = ọkụ.relu (self.conv2(x))
    x = x. nlele (-1, 320)
    x = ọkụ.relu (self.fc1 (x))
    x = onwe.fc2 (x)
    laghachi x

Bido ihe ngosi CNN

nlereanya = CNN()

Bido ụdị LangChain LLAMA

llama = LLAMA (ihe nlereanya)

Kọwaa akụrụngwa XAI

Klas XAIComponent: def init (onwe, ihe nlereanya): self.model = ihe nlereanya

def kọwaa (onwe, ntinye_data):
    # Jiri LangChain wepụta nkọwa maka data ntinye
    nkọwa = llama.generate_explanation(input_data)
    laghachi nkọwa

Bido akụrụngwa XAI

xai_component = XAIComponent(ihe nlereanya)

Nwalee akụrụngwa XAI

input_data = ọkụ.randn (1, 1, 28, 28) nkọwa = xai_component.explain(input_data) ebipụta (nkọwa) ``

N'ihe atụ a, anyị na-akọwapụta ihe owuwu CNN dị mfe site na iji PyTorch wee jikọta ihe XAI site na iji LangChain. Anyị na-akọwapụta mpaghara XAI nke na-eji LangChain mepụta nkọwa maka data ntinye.

Top 5-10 Iwu/Snippets kacha eji

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

LangChain

1. langchain.LLAMA(ihe nlere anya): na-ebido ụdị LangChain LLAMA.
2. `llama.generate_explanation(data_data)': Na-ewepụta nkọwa maka data ntinye.
3. langchain.Nkọwa(ụdị, input_data): Na-emepụta ihe nkọwa maka ntinye data.
4. llama.explain(input_data): Na-eji ụdị LLAMA na-akọwa data ntinye.
5. langchain.ModelWrapper(model): Kechie ihe nlereanya na LangChain's nlereanya wrapper.

DSPy

1. dsp.DSPy(model): Na-ebido ụdị DSPy.
2. `dsp.explain(input_data):': Na-akọwa ntinye data site na iji ụdị DSPy.
3. dsp.ModelWrapper(model): Kechie ihe nlereanya na DSPy si nlereanya wrapper.
4. `dsp.Nkọwa(model, input_data): Na-emepụta ihe nkowa maka data ntinye.
5. dsp.generate_explanation(input_data): Na-ewepụta nkọwa maka data ntinye.

Nlere anya

Nke a bụ nhụta dị mfe nke mpaghara XAI site na iji Chart.js: Json { "ụdị": "ogwe", "data": { "labels": ["Data ntinye", "Nkọwa"], "datasets": [ { "label": "Akụkụ XAI", "data": [10, 20], "backgroundAgba": [ "rgba(255, 99, 132, 0.2)", "rgba (54, 162, 235, 0.2)" ], "BorderAgba": [ "rgba (255, 99, 132, 1)", "rgba (54, 162, 235, 1)" ], "BorderWiidth": 1 } ] }, "Nhọrọ": { "ọnụego": { "yAxes": [ { "akara aka": { "beginAtZero": eziokwu } } ] } } } `` Ọhụụ a na-egosi data ntinye yana nkọwa nke akụrụngwa XAI mepụtara.

Mmechi

Ịmejuputa XAI na ụlọ ọrụ ụlọ ọrụ chọrọ nghọta miri emi nke usoro AI dị n'okpuru na ike ijikọ ihe XAI n'ime usoro ndị dị ugbu a. LangChain na DSPy na-enye otu ngwaọrụ na ọba akwụkwọ maka iwulite usoro AI nwere ike ịkọwa, na-eme ka ọ dị mfe iji mejuputa XAI na usoro ngwanrọ mgbagwoju anya. Site n'ịgbaso ihe atụ na nhụta anya enyere n'isiokwu a, ndị mmepe nwere ike imeghe ike XAI n'ime ụlọ ọrụ ha wee wulite usoro AI na-atụgharị nke ọma na ntụgharị.

Ọrụ Ọdịnihu

Ọrụ n'ọdịnihu na isiokwu a nwere ike ịgụnye:

• Mepụta akụrụngwa XAI dị elu karịa site na iji LangChain na DSPy.
• Ijikọ akụrụngwa XAI na usoro AI ndị ọzọ na ọba akwụkwọ.
• Mepụta ngwa nhụta ọkaibe maka akụrụngwa XAI.
• Itinye XAI na sistemụ AI siri ike na ngalaba.

Ntụaka

• LangChain: https://langchain.dev/
• DSPy: https://dspy.dev/
• PyTorch: https://pytorch.org/ TensorFlow: https://www.tensorflow.org/