diff --git a/src/lib.rs b/src/lib.rs
index 8135c95..c42927a 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -33,7 +33,7 @@ mod semantic_embedding;
 use std::time::Duration;
 
 use http::HttpClient;
-use semantic_embedding::SemanticEmbeddingOutput;
+use semantic_embedding::{BatchSemanticEmbeddingOutput, SemanticEmbeddingOutput};
 
 pub use self::{
     completion::{CompletionOutput, Sampling, Stopping, TaskCompletion},
@@ -43,7 +43,9 @@ pub use self::{
     },
     http::{Error, Job, Task},
     prompt::{Modality, Prompt},
-    semantic_embedding::{SemanticRepresentation, TaskSemanticEmbedding},
+    semantic_embedding::{
+        SemanticRepresentation, TaskBatchSemanticEmbedding, TaskSemanticEmbedding,
+    },
 };
 
 /// Execute Jobs against the Aleph Alpha API
@@ -122,6 +124,15 @@ impl Client {
         self.http_client.output_of(task, how).await
     }
 
+    /// An batch of embeddings trying to capture the semantic meaning of a text.
+    pub async fn batch_semantic_embedding(
+        &self,
+        task: &TaskBatchSemanticEmbedding<'_>,
+        how: &How,
+    ) -> Result<BatchSemanticEmbeddingOutput, Error> {
+        self.http_client.output_of(task, how).await
+    }
+
     /// Instruct a model served by the aleph alpha API to continue writing a piece of text (or
     /// multimodal document).
     ///
diff --git a/src/semantic_embedding.rs b/src/semantic_embedding.rs
index 5b7739f..c64501f 100644
--- a/src/semantic_embedding.rs
+++ b/src/semantic_embedding.rs
@@ -1,4 +1,5 @@
 use serde::{Deserialize, Serialize};
+use std::fmt::Debug;
 
 use crate::{http::Task, Job, Prompt};
 
@@ -42,13 +43,14 @@ pub struct TaskSemanticEmbedding<'a> {
 }
 
 /// Appends model and hosting to the bare task
+/// T stands for TaskSemanticEmbedding or TaskBatchSemanticEmbedding
 #[derive(Serialize, Debug)]
-struct RequestBody<'a> {
+struct RequestBody<'a, T: Serialize + Debug> {
     /// Currently semantic embedding still requires a model parameter, even though "luminous-base"
     /// is the only model to support it. This makes Semantic embedding both a Service and a Method.
     model: &'a str,
     #[serde(flatten)]
-    semantic_embedding_task: &'a TaskSemanticEmbedding<'a>,
+    semantic_embedding_task: &'a T,
 }
 
 /// Heap allocated embedding. Can hold full embeddings or compressed ones
@@ -96,3 +98,50 @@ impl Job for TaskSemanticEmbedding<'_> {
         response
     }
 }
+
+/// Create embeddings for multiple prompts
+#[derive(Serialize, Debug)]
+pub struct TaskBatchSemanticEmbedding<'a> {
+    /// The prompt (usually text) to be embedded.
+    pub prompts: Vec<Prompt<'a>>,
+    /// Semantic representation to embed the prompt with. This parameter is governed by the specific
+    /// usecase in mind.
+    pub representation: SemanticRepresentation,
+    /// Default behaviour is to return the full embedding, but you can optionally request an
+    /// embedding compressed to a smaller set of dimensions. A size of `128` is supported for every
+    /// model.
+    ///
+    /// The 128 size is expected to have a small drop in accuracy performance (4-6%), with the
+    /// benefit of being much smaller, which makes comparing these embeddings much faster for use
+    /// cases where speed is critical.
+    ///
+    /// The 128 size can also perform better if you are embedding short texts or documents.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub compress_to_size: Option<u32>,
+}
+
+/// Heap allocated vec of embeddings. Can hold full embeddings or compressed ones
+#[derive(Deserialize)]
+pub struct BatchSemanticEmbeddingOutput {
+    pub embeddings: Vec<Vec<f32>>,
+}
+
+impl Job for TaskBatchSemanticEmbedding<'_> {
+    type Output = BatchSemanticEmbeddingOutput;
+    type ResponseBody = BatchSemanticEmbeddingOutput;
+
+    fn build_request(&self, client: &reqwest::Client, base: &str) -> reqwest::RequestBuilder {
+        let model = "luminous-base";
+        let body = RequestBody {
+            model,
+            semantic_embedding_task: self,
+        };
+        client
+            .post(format!("{base}/batch_semantic_embed"))
+            .json(&body)
+    }
+
+    fn body_to_output(&self, response: Self::ResponseBody) -> Self::Output {
+        response
+    }
+}
diff --git a/tests/integration.rs b/tests/integration.rs
index 228222b..9972bec 100644
--- a/tests/integration.rs
+++ b/tests/integration.rs
@@ -2,8 +2,8 @@ use std::{fs::File, io::BufReader};
 
 use aleph_alpha_client::{
     cosine_similarity, Client, Granularity, How, ImageScore, ItemExplanation, Modality, Prompt,
-    PromptGranularity, Sampling, SemanticRepresentation, Stopping, Task, TaskCompletion,
-    TaskExplanation, TaskSemanticEmbedding, TextScore,
+    PromptGranularity, Sampling, SemanticRepresentation, Stopping, Task,
+    TaskBatchSemanticEmbedding, TaskCompletion, TaskExplanation, TaskSemanticEmbedding, TextScore,
 };
 use dotenv::dotenv;
 use image::ImageFormat;
@@ -341,3 +341,38 @@ async fn answer_should_continue() {
     assert!(response.completion.starts_with(" Says."));
     assert!(response.completion.len() > " Says.".len());
 }
+
+#[tokio::test]
+async fn batch_semanitc_embed_with_luminous_base() {
+    // Given
+    let robot_fact = Prompt::from_text(
+        "A robot is a machine—especially one programmable by a computer—capable of carrying out a \
+        complex series of actions automatically.",
+    );
+    let pizza_fact = Prompt::from_text(
+        "Pizza (Italian: [ˈpittsa], Neapolitan: [ˈpittsə]) is a dish of Italian origin consisting \
+        of a usually round, flat base of leavened wheat-based dough topped with tomatoes, cheese, \
+        and often various other ingredients (such as various types of sausage, anchovies, \
+        mushrooms, onions, olives, vegetables, meat, ham, etc.), which is then baked at a high \
+        temperature, traditionally in a wood-fired oven.",
+    );
+
+    let client = Client::new(&AA_API_TOKEN).unwrap();
+
+    // When
+    let embedding_task = TaskBatchSemanticEmbedding {
+        prompts: vec![robot_fact, pizza_fact],
+        representation: SemanticRepresentation::Document,
+        compress_to_size: Some(128),
+    };
+
+    let embeddings = client
+        .batch_semantic_embedding(&embedding_task, &How::default())
+        .await
+        .unwrap()
+        .embeddings;
+
+    // Then
+    // There should be 2 embeddings
+    assert_eq!(embeddings.len(), 2);
+}