diff --git a/src/embedder.cpp b/src/embedder.cpp
index 8753e18..9d5ef00 100644
--- a/src/embedder.cpp
+++ b/src/embedder.cpp
@@ -186,6 +186,43 @@ llama_embedder *init_embedder(const char *embedding_model, const uint32_t poolin
     return embedder;
 }
 
+std::vector<std::vector<float>> weighted_mean_pooling(
+        const std::vector<std::vector<std::vector<float>>>& last_hidden_state,
+        const std::vector<std::vector<int>>& attention_mask, const int max_length) {
+
+    if (last_hidden_state.empty() || attention_mask.empty() ||
+        last_hidden_state.size() != attention_mask.size()) {
+        throw std::invalid_argument("Invalid input sizes");
+    }
+
+    size_t batch_size = last_hidden_state.size();
+    size_t seq_length = last_hidden_state[0].size();
+    size_t hidden_dim = last_hidden_state[0][0].size();
+
+    std::vector<std::vector<float>> result(batch_size, std::vector<float>(hidden_dim, 0.0f));
+
+    for (size_t i = 0; i < batch_size; ++i) {
+        std::vector<float> sum(hidden_dim, 0.0f);
+        float mask_sum = 0.0f;
+
+        for (size_t j = 0; j < seq_length; ++j) {
+            auto mask_value = static_cast<float>(attention_mask[i][j]);
+            for (size_t k = 0; k < hidden_dim; ++k) {
+                sum[k] += last_hidden_state[i][j][k] * mask_value;
+            }
+            mask_sum += mask_value;
+        }
+
+        // Normalize, with a minimum value to prevent division by zero
+        double norm_factor = std::max(mask_sum, 1e-9f);
+        for (size_t k = 0; k < hidden_dim; ++k) {
+            result[i][k] = sum[k] / norm_factor;
+        }
+    }
+
+    return result;
+}
+
 void tokenize(llama_embedder *embedder, const std::vector<std::string>& texts, std::vector<llama_tokenizer_data> &output,const bool add_special_tokens, const bool parse_special, const bool enable_padding) {
     if (!embedder) {
         throw std::runtime_error("Error: Null pointer passed to tokenize function");