Update README.md

README.md

@@ -2364,7 +2364,7 @@ documents = [
 
 # Get embeddings for the legal documents
 doc_embeddings = client.embed(texts=documents, model='vstackai-law-1', is_query=False) # EmbeddingsObject(num_embeddings=3, embedding_dims=1536)
-doc_embeddings = np.array(doc_embeddings.embeddings)
+doc_embeddings = doc_embeddings.embeddings # (3, 1536) numpy array
 
 # Encode the query
 query = "How many days does the consumer have to return the product?"
@@ -2374,7 +2374,7 @@ query_embedding = client.embed(
     is_query=True, 
     instruction='Represent the query for searching legal documents'
 ) # EmbeddingsObject(num_embeddings=1, embedding_dims=1536)
-query_embedding = np.array(query_embedding.embeddings) # (1, 1536)
+query_embedding = query_embedding.embeddings # (1, 1536) numpy array
 
 # To check if the embeddings work, you can compute similarity between the query and documents
 similarities = np.dot(doc_embeddings, query_embedding.T)