diff --git a/comps/retrievers/opensearch/langchain/requirements.txt b/comps/retrievers/opensearch/langchain/requirements.txt
index 829c80e50..7fb987ccb 100644
--- a/comps/retrievers/opensearch/langchain/requirements.txt
+++ b/comps/retrievers/opensearch/langchain/requirements.txt
@@ -12,3 +12,5 @@ pymupdf
 sentence_transformers
 shortuuid
 uvicorn
+pydantic
+numpy
diff --git a/comps/retrievers/opensearch/langchain/retriever_opensearch.py b/comps/retrievers/opensearch/langchain/retriever_opensearch.py
index 3e7bbce94..f5b260230 100644
--- a/comps/retrievers/opensearch/langchain/retriever_opensearch.py
+++ b/comps/retrievers/opensearch/langchain/retriever_opensearch.py
@@ -3,12 +3,14 @@
 
 import os
 import time
-from typing import Union
+import numpy as np
+from typing import Union, List, Callable
 
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 from langchain_community.vectorstores import OpenSearchVectorSearch
 from langchain_huggingface import HuggingFaceEndpointEmbeddings
 from opensearch_config import EMBED_MODEL, INDEX_NAME, OPENSEARCH_INITIAL_ADMIN_PASSWORD, OPENSEARCH_URL
+from pydantic import conlist
 
 from comps import (
     CustomLogger,
@@ -34,6 +36,33 @@
 tei_embedding_endpoint = os.getenv("TEI_EMBEDDING_ENDPOINT", None)
 
 
+async def search_all_embeddings_vectors(
+    embeddings: Union[conlist(float, min_length=0), List[conlist(float, min_length=0)]],
+    func: Callable,
+    *args,
+    **kwargs
+    ):
+    try:
+        if not isinstance(embeddings, np.ndarray):
+            embeddings = np.array(embeddings)
+
+        if not np.issubdtype(embeddings.dtype, np.floating):
+            raise ValueError("All embeddings values must be floating point numbers")
+
+        if embeddings.ndim == 1:
+            return await func(embedding=embeddings, *args, **kwargs)
+        elif embeddings.ndim == 2:
+            responses = []
+            for emb in embeddings:
+                response = await func(embedding=emb, *args, **kwargs)
+                responses.extend(response)
+            return responses
+        else:
+            raise ValueError("Embeddings must be one or two dimensional")
+    except Exception as e:
+        raise ValueError(f"Embedding data is not valid: {e}")
+
+
 @register_microservice(
     name="opea_service@retriever_opensearch",
     service_type=ServiceType.RETRIEVER,
@@ -65,12 +94,19 @@ async def retrieve(
             query = input.input
         # if the OpenSearch index has data, perform the search
         if input.search_type == "similarity":
-            search_res = await vector_db.asimilarity_search_by_vector(embedding=input.embedding, k=input.k)
+            search_res = await search_all_embeddings_vectors(
+                embeddings=input.embedding,
+                func=vector_db.asimilarity_search_by_vector,
+                k=input.k,
+            )
         elif input.search_type == "similarity_distance_threshold":
             if input.distance_threshold is None:
                 raise ValueError("distance_threshold must be provided for " + "similarity_distance_threshold retriever")
-            search_res = await vector_db.asimilarity_search_by_vector(
-                embedding=input.embedding, k=input.k, distance_threshold=input.distance_threshold
+            search_res = await search_all_embeddings_vectors(
+                embeddings=input.embedding,
+                func=vector_db.asimilarity_search_by_vector,
+                k=input.k,
+                distance_threshold=input.distance_threshold,
             )
         elif input.search_type == "similarity_score_threshold":
             doc_and_similarities = await vector_db.asimilarity_search_with_relevance_scores(