diff --git a/langchain/vectorstores/elastic_vector_search.py b/langchain/vectorstores/elastic_vector_search.py
index 4f37800e..57a73dcd 100644
--- a/langchain/vectorstores/elastic_vector_search.py
+++ b/langchain/vectorstores/elastic_vector_search.py
@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 import uuid
-from typing import Any, Callable, Dict, Iterable, List, Optional
+from typing import Any, Dict, Iterable, List, Optional
 
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
@@ -19,7 +19,7 @@ def _default_text_mapping(dim: int) -> Dict:
     }
 
 
-def _default_script_query(query_vector: List[int]) -> Dict:
+def _default_script_query(query_vector: List[float]) -> Dict:
     return {
         "script_score": {
             "query": {"match_all": {}},
@@ -41,14 +41,12 @@ class ElasticVectorSearch(VectorStore):
             elastic_vector_search = ElasticVectorSearch(
                 "http://localhost:9200",
                 "embeddings",
-                embedding_function
+                embedding
             )
 
     """
 
-    def __init__(
-        self, elasticsearch_url: str, index_name: str, embedding_function: Callable
-    ):
+    def __init__(self, elasticsearch_url: str, index_name: str, embedding: Embeddings):
         """Initialize with necessary components."""
         try:
             import elasticsearch
@@ -57,7 +55,7 @@ class ElasticVectorSearch(VectorStore):
                 "Could not import elasticsearch python package. "
                 "Please install it with `pip install elasticsearch`."
             )
-        self.embedding_function = embedding_function
+        self.embedding = embedding
         self.index_name = index_name
         try:
             es_client = elasticsearch.Elasticsearch(elasticsearch_url)  # noqa
@@ -91,13 +89,14 @@ class ElasticVectorSearch(VectorStore):
             )
         requests = []
         ids = []
+        embeddings = self.embedding.embed_documents(list(texts))
         for i, text in enumerate(texts):
             metadata = metadatas[i] if metadatas else {}
             _id = str(uuid.uuid4())
             request = {
                 "_op_type": "index",
                 "_index": self.index_name,
-                "vector": self.embedding_function(text),
+                "vector": embeddings[i],
                 "text": text,
                 "metadata": metadata,
                 "_id": _id,
@@ -121,7 +120,7 @@ class ElasticVectorSearch(VectorStore):
         Returns:
             List of Documents most similar to the query.
         """
-        embedding = self.embedding_function(query)
+        embedding = self.embedding.embed_query(query)
         script_query = _default_script_query(embedding)
         response = self.client.search(index=self.index_name, query=script_query)
         hits = [hit["_source"] for hit in response["hits"]["hits"][:k]]
@@ -196,4 +195,4 @@ class ElasticVectorSearch(VectorStore):
             requests.append(request)
         bulk(client, requests)
         client.indices.refresh(index=index_name)
-        return cls(elasticsearch_url, index_name, embedding.embed_query)
+        return cls(elasticsearch_url, index_name, embedding)