diff --git a/GraphNeuralNetworks/src/samplers.jl b/GraphNeuralNetworks/src/samplers.jl
index 44fb3c825..4157b52fc 100644
--- a/GraphNeuralNetworks/src/samplers.jl
+++ b/GraphNeuralNetworks/src/samplers.jl
@@ -1,7 +1,25 @@
 using GraphNeuralNetworks
 using Graphs
 
-# Define a NeighborLoader structure for sampling neighbors
+"""
+    struct NeighborLoader
+
+A data structure for sampling neighbors from a graph for training Graph Neural Networks (GNNs). 
+It supports multi-layer sampling of neighbors for a batch of input nodes, useful for mini-batch training.
+
+# Fields:
+- `graph::GNNGraph`: The input graph containing nodes and edges, along with node features (from GraphNeuralNetworks.jl).
+- `num_neighbors::Vector{Int}`: A vector specifying the number of neighbors to sample per node at each GNN layer.
+- `input_nodes::Vector{Int}`: A vector containing the starting nodes for neighbor sampling.
+- `num_layers::Int`: The number of layers for neighborhood expansion (how far to sample neighbors).
+- `batch_size::Union{Int, Nothing}`: The size of the batch. If not specified, it defaults to the number of `input_nodes`.
+- `neighbors_cache::Dict{Int, Vector{Int}}`: A cache to store sampled neighbors for each node, preventing redundant sampling.
+
+# Usage:
+```julia
+loader = NeighborLoader(graph; num_neighbors=[10, 5], input_nodes=[1, 2, 3], num_layers=2)
+```
+"""
 struct NeighborLoader
     graph::GNNGraph             # The input GNNGraph (graph + features from GraphNeuralNetworks.jl)
     num_neighbors::Vector{Int}  # Number of neighbors to sample per node, for each layer
@@ -11,11 +29,40 @@ struct NeighborLoader
     neighbors_cache::Dict{Int, Vector{Int}}  # Cache neighbors to avoid recomputation
 end
 
-# Constructor for NeighborLoader with optional batch size
+### `NeighborLoader` constructor
+"""
+    NeighborLoader(graph::GNNGraph; num_neighbors::Vector{Int}, input_nodes::Vector{Int}, num_layers::Int, batch_size::Union{Int, Nothing}=nothing)
+
+Creates a `NeighborLoader` to sample neighbors from the provided `graph` for the training. 
+    The loader supports batching and multi-layer neighbor sampling.
+
+# Arguments:
+- `graph::GNNGraph`: The input graph with node features.
+- `num_neighbors::Vector{Int}`: Number of neighbors to sample per node, per layer.
+- `input_nodes::Vector{Int}`: Set of starting nodes for sampling.
+- `num_layers::Int`: Number of layers to expand neighborhoods for sampling.
+- `batch_size::Union{Int, Nothing}`: Optional batch size. If `nothing`, it defaults to the length of `input_nodes`.
+
+# Returns:
+A `NeighborLoader` object.
+"""
 function NeighborLoader(graph::GNNGraph; num_neighbors::Vector{Int}, input_nodes::Vector{Int}, num_layers::Int, batch_size::Union{Int, Nothing}=nothing)
     return NeighborLoader(graph, num_neighbors, input_nodes, num_layers, batch_size === nothing ? length(input_nodes) : batch_size, Dict{Int, Vector{Int}}())
 end
 
+"""
+    get_neighbors(loader::NeighborLoader, node::Int) -> Vector{Int}
+
+Returns the neighbors of a given `node` in the graph from the `NeighborLoader`. 
+    It first checks if the neighbors are cached; if not, it retrieves the neighbors from the graph and caches them for future use.
+
+# Arguments:
+- `loader::NeighborLoader`: The `NeighborLoader` instance.
+- `node::Int`: The node whose neighbors you want to sample.
+
+# Returns:
+A vector of neighbor node indices.
+"""
 # Function to get cached neighbors or compute them
 function get_neighbors(loader::NeighborLoader, node::Int)
     if haskey(loader.neighbors_cache, node)
@@ -27,6 +74,20 @@ function get_neighbors(loader::NeighborLoader, node::Int)
     end
 end
 
+"""
+    sample_neighbors(loader::NeighborLoader, node::Int, layer::Int) -> Vector{Int}
+
+Samples a specified number of neighbors for the given `node` at a particular `layer` of the GNN. 
+    The number of neighbors sampled is defined in `loader.num_neighbors`.
+
+# Arguments:
+- `loader::NeighborLoader`: The `NeighborLoader` instance.
+- `node::Int`: The node to sample neighbors for.
+- `layer::Int`: The current GNN layer (used to determine how many neighbors to sample).
+
+# Returns:
+A vector of sampled neighbor node indices.
+"""
 # Function to sample neighbors for a given node at a specific layer
 function sample_neighbors(loader::NeighborLoader, node::Int, layer::Int)
     neighbors = get_neighbors(loader, node)
@@ -38,6 +99,20 @@ function sample_neighbors(loader::NeighborLoader, node::Int, layer::Int)
     end
 end
 
+"""
+    induced_subgraph(graph::GNNGraph, nodes::Vector{Int}) -> GNNGraph
+
+Generates a subgraph from the original graph using the provided `nodes`. 
+    The function includes the nodes' neighbors and creates edges between nodes that are connected in the original graph. 
+    If a node has no neighbors, an isolated node will be added to the subgraph.
+
+# Arguments:
+- `graph::GNNGraph`: The original graph containing nodes, edges, and node features.
+- `nodes::Vector{Int}`: A vector of node indices to include in the subgraph.
+
+# Returns:
+A new `GNNGraph` containing the subgraph with the specified nodes and their features.
+"""
 function induced_subgraph(graph::GNNGraph, nodes::Vector{Int})
     if isempty(nodes)
         return GNNGraph()  # Return empty graph if no nodes are provided
@@ -73,6 +148,22 @@ function induced_subgraph(graph::GNNGraph, nodes::Vector{Int})
     return GNNGraph(source, target, ndata = new_features)  # Return the new GNNGraph with subgraph and features
 end
 
+"""
+    Base.iterate(loader::NeighborLoader, state::Int=1) -> Tuple{GNNGraph, Int}
+
+Implements the iterator protocol for `NeighborLoader`, allowing mini-batch processing for neighbor sampling in GNNs. 
+    Each call to `iterate` returns a mini-batch subgraph with sampled neighbors for a batch of input nodes, 
+    expanding their neighborhoods for a specified number of layers.
+
+# Arguments:
+- `loader::NeighborLoader`: The `NeighborLoader` instance to sample neighbors from.
+- `state::Int`: The current position in the input nodes for batching. Defaults to 1.
+
+# Returns:
+A tuple `(mini_batch_gnn, next_state)` where:
+- `mini_batch_gnn::GNNGraph`: The subgraph induced by the sampled nodes and their neighbors for the current mini-batch.
+- `next_state::Int`: The next state (index) for iterating through the input nodes. If the input nodes are exhausted, returns `nothing`.
+"""
 # Iterator protocol for NeighborLoader with lazy batch loading
 function Base.iterate(loader::NeighborLoader, state=1)
     if state > length(loader.input_nodes) 
@@ -113,4 +204,4 @@ function Base.iterate(loader::NeighborLoader, state=1)
 
     # Continue iteration for the next batch
     return mini_batch_gnn, state + batch_size
-end
\ No newline at end of file
+end