diff --git a/libs/langchain/langchain/vectorstores/opensearch_vector_search.py b/libs/langchain/langchain/vectorstores/opensearch_vector_search.py
index 12e1928d05..bb3508eaa1 100644
--- a/libs/langchain/langchain/vectorstores/opensearch_vector_search.py
+++ b/libs/langchain/langchain/vectorstores/opensearch_vector_search.py
@@ -736,7 +736,7 @@ class OpenSearchVectorSearch(VectorStore):
         ids: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> OpenSearchVectorSearch:
-        """Construct OpenSearchVectorSearch wrapper from raw documents.
+        """Construct OpenSearchVectorSearch wrapper from raw texts.
 
         Example:
             .. code-block:: python
@@ -754,6 +754,74 @@ class OpenSearchVectorSearch(VectorStore):
         and lucene engines recommended for large datasets. Also supports brute force
         search through Script Scoring and Painless Scripting.
 
+        Optional Args:
+            vector_field: Document field embeddings are stored in. Defaults to
+            "vector_field".
+
+            text_field: Document field the text of the document is stored in. Defaults
+            to "text".
+
+        Optional Keyword Args for Approximate Search:
+            engine: "nmslib", "faiss", "lucene"; default: "nmslib"
+
+            space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2"
+
+            ef_search: Size of the dynamic list used during k-NN searches. Higher values
+            lead to more accurate but slower searches; default: 512
+
+            ef_construction: Size of the dynamic list used during k-NN graph creation.
+            Higher values lead to more accurate graph but slower indexing speed;
+            default: 512
+
+            m: Number of bidirectional links created for each new element. Large impact
+            on memory consumption. Between 2 and 100; default: 16
+
+        Keyword Args for Script Scoring or Painless Scripting:
+            is_appx_search: False
+
+        """
+        embeddings = embedding.embed_documents(texts)
+        return cls.from_embeddings(
+            embeddings,
+            texts,
+            embedding,
+            metadatas=metadatas,
+            bulk_size=bulk_size,
+            ids=ids,
+            **kwargs,
+        )
+
+    @classmethod
+    def from_embeddings(
+        cls,
+        embeddings: List[List[float]],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        bulk_size: int = 500,
+        ids: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> OpenSearchVectorSearch:
+        """Construct OpenSearchVectorSearch wrapper from pre-vectorized embeddings.
+
+        Example:
+            .. code-block:: python
+
+                from langchain.vectorstores import OpenSearchVectorSearch
+                from langchain.embeddings import OpenAIEmbeddings
+                embedder = OpenAIEmbeddings()
+                embeddings = embedder.embed_documents(["foo", "bar"])
+                opensearch_vector_search = OpenSearchVectorSearch.from_embeddings(
+                    embeddings,
+                    texts,
+                    embedder,
+                    opensearch_url="http://localhost:9200"
+                )
+
+        OpenSearch by default supports Approximate Search powered by nmslib, faiss
+        and lucene engines recommended for large datasets. Also supports brute force
+        search through Script Scoring and Painless Scripting.
+
         Optional Args:
             vector_field: Document field embeddings are stored in. Defaults to
             "vector_field".
@@ -799,7 +867,6 @@ class OpenSearchVectorSearch(VectorStore):
             "max_chunk_bytes",
             "is_aoss",
         ]
-        embeddings = embedding.embed_documents(texts)
         _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
         dim = len(embeddings[0])
         # Get the index name from either from kwargs or ENV Variable
@@ -843,8 +910,8 @@ class OpenSearchVectorSearch(VectorStore):
             index_name,
             embeddings,
             texts,
-            metadatas=metadatas,
             ids=ids,
+            metadatas=metadatas,
             vector_field=vector_field,
             text_field=text_field,
             mapping=mapping,