diff --git a/src/python/paperai/embeddings.py b/src/python/paperai/embeddings.py
index 727db1b..7a81f8e 100644
--- a/src/python/paperai/embeddings.py
+++ b/src/python/paperai/embeddings.py
@@ -128,9 +128,8 @@ def index(self, documents):
             documents: list of documents
         """
 
-        ids = []
-        embeddings = []
-        stream = None
+        # Initialize local variables
+        ids, embeddings, dimensions, stream = [], None, None, None
 
         # Shared objects with Pool
         args = (self.config, self.scoring)
@@ -140,26 +139,26 @@ def index(self, documents):
             with tempfile.NamedTemporaryFile(mode="wb", suffix=".npy", delete=False) as output:
                 stream = output.name
                 for uid, embedding in pool.imap(transform, documents):
+                    if not dimensions:
+                        # Set number of dimensions for embeddings
+                        dimensions = embedding.shape[0]
+
                     ids.append(uid)
                     pickle.dump(embedding, output)
 
-        # Load streamed embeddings
+        # Load streamed embeddings back to memory
+        embeddings = np.empty((len(ids), dimensions), dtype=np.float32)
         with open(stream, "rb") as stream:
-            while True:
-                try:
-                    embeddings.append(pickle.load(stream))
-                except EOFError:
-                    break
-
-        # Convert embeddings into a numpy array
-        embeddings = np.array(embeddings)
+            for x in range(embeddings.shape[0]):
+                embeddings[x] = pickle.load(stream)
 
         # Build LSA model (if enabled). Remove principal components from embeddings.
-        self.lsa = self.buildLSA(embeddings, self.config["pca"]) if self.config["pca"] else None
-        embeddings = self.removePC(embeddings) if self.lsa else embeddings
+        if self.config["pca"]:
+            self.lsa = self.buildLSA(embeddings, self.config["pca"])
+            self.removePC(embeddings)
 
         # Normalize embeddings
-        embeddings = self.normalize(embeddings)
+        self.normalize(embeddings)
 
         # Create embeddings index. Inner product is equal to cosine similarity on normalized vectors.
         # pylint: disable=E1136
@@ -189,40 +188,35 @@ def buildLSA(self, embeddings, components):
 
     def removePC(self, embeddings):
         """
-        Applies a LSA model to embeddings, removed the top n principal components.
+        Applies a LSA model to embeddings, removed the top n principal components. Operation applied
+        directly on array.
 
         Args:
             embeddings: input embeddings matrix
-
-        Returns:
-            embeddings with the principal component(s) removed
         """
 
         pc = self.lsa.components_
 
+        # Apply LSA model
         # Calculation is different if n_components = 1
         if pc.shape[0] == 1:
-            return embeddings - embeddings.dot(pc.transpose()) * pc
-
-        # Apply LSA model
-        return embeddings - embeddings.dot(pc.transpose()).dot(pc)
+            embeddings -= embeddings.dot(pc.transpose()) * pc
+        else:
+            embeddings -= embeddings.dot(pc.transpose()).dot(pc)
 
     def normalize(self, embeddings):
         """
-        Normalizes embeddings using L2 normalization.
+        Normalizes embeddings using L2 normalization. Operation applied directly on array.
 
         Args:
             embeddings: input embeddings matrix
-
-        Returns:
-            normalized embeddings
         """
 
         # Calculation is different for matrices vs vectors
         if len(embeddings.shape) > 1:
-            return embeddings / np.linalg.norm(embeddings, axis=1).reshape(-1, 1)
-
-        return embeddings / np.linalg.norm(embeddings)
+            embeddings /= np.linalg.norm(embeddings, axis=1)[:, np.newaxis]
+        else:
+            embeddings /= np.linalg.norm(embeddings)
 
     def transform(self, document):
         """
@@ -248,10 +242,14 @@ def transform(self, document):
 
         # Reduce the dimensionality of the embeddings. Scale the embeddings using this
         # model to reduce the noise of common but less relevant terms.
-        embedding = self.removePC(embedding) if self.lsa else embedding
+        if self.lsa:
+            self.removePC(embedding)
 
         # Normalize vector if embeddings index exists, normalization is skipped during index builds
-        return self.normalize(embedding) if self.embeddings else embedding
+        if self.embeddings:
+            self.normalize(embedding)
+
+        return embedding
 
     def lookup(self, tokens):
         """