RAG

Retrieval Augmented Generation import torch import torch.nn as nn

class RAGNeuron(nn.Module): def init(self, input_size, hidden_size, output_size): super(RAGNeuron, self).init() self.fc1 = nn.Linear(input_size, hidden_size) self.fc2 = nn.Linear(hidden_size, output_size) self.retrieval_module = RetrievalModule() self.activation = nn.ReLU()

def forward(self, x):
    # Retrieve relevant information from an external knowledge base
    retrieved_information = self.retrieval_module(x)

    # Combine the retrieved information with the input features
    combined_input = torch.cat((x, retrieved_information), dim=1)

    # Pass the combined input through the neural network layers
    x = self.fc1(combined_input)
    x = self.activation(x)
    x = self.fc2(x)
    return x

Combine the retrieved information with the input features

combined_input = torch.cat((x, retrieved_information), dim=1)

Pass the combined input through the neural network layers

x = self.fc1(combined_input) x = self.activation(x)

Apply the formula y = 1 on the square root of x

y = 1 / torch.sqrt(x)

Pass the modified input through the remaining layer

x = self.fc2(y) return x