Use embedding instead of embedding function in ElasticVectorStore (#1692)

While it might be a bit more restrictive, I find that using the
Embedding interface as an input for the vector store creation is better
than an embedding function because we can use bulk requests and possibly
the retry logic if needed.

I have seen that some vector store implementations use Embedding while
others use embedding function so I don't know what is the criteria to
have one or the other, in my opinion they should all just be Embedding
or have a way more complex embedding function that accepts multiple
texts instead of one by one.

---------

Co-authored-by: Bernat Felip <bernat.felip@rea.ch>

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)