community[patch]: Implement delete method and all async methods in opensearch_vector_search (#17321)

- **Description:** In order to use index and aindex in
libs/langchain/langchain/indexes/_api.py, I implemented delete method
and all async methods in opensearch_vector_search
- **Dependencies:** No changes

docs/docs/modules/data_connection/indexing.ipynb

@@ -60,7 +60,8 @@
     "   * document addition by id (`add_documents` method with `ids` argument)\n",
     "   * delete by id (`delete` method with `ids` argument)\n",
     "\n",
-    "Compatible Vectorstores: `AnalyticDB`, `AstraDB`, `AwaDB`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `ZepVectorStore`.\n",
+
+    "Compatible Vectorstores: `AnalyticDB`, `AstraDB`, `AwaDB`, `Bagel`, `Cassandra`, `Chroma`, `CouchbaseVectorStore`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MyScale`, `OpenSearchVectorSearch`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `VDMS`, `Vearch`, `VespaStore`, `Weaviate`, `ZepVectorStore`, `OpenSearchVectorSearch`.\n",
     "  \n",
     "## Caution\n",
     "\n",

libs/community/langchain_community/vectorstores/opensearch_vector_search.py

@@ -15,6 +15,10 @@ from langchain_community.vectorstores.utils import maximal_marginal_relevance
 IMPORT_OPENSEARCH_PY_ERROR = (
     "Could not import OpenSearch. Please install it with `pip install opensearch-py`."
 )
+IMPORT_ASYNC_OPENSEARCH_PY_ERROR = """
+Could not import AsyncOpenSearch. 
+Please install it with `pip install opensearch-py`."""
+
 SCRIPT_SCORING_SEARCH = "script_scoring"
 PAINLESS_SCRIPTING_SEARCH = "painless_scripting"
 MATCH_ALL_QUERY = {"match_all": {}}  # type: Dict
@@ -29,6 +33,15 @@ def _import_opensearch() -> Any:
     return OpenSearch
 
 
+def _import_async_opensearch() -> Any:
+    """Import AsyncOpenSearch if available, otherwise raise error."""
+    try:
+        from opensearchpy import AsyncOpenSearch
+    except ImportError:
+        raise ImportError(IMPORT_ASYNC_OPENSEARCH_PY_ERROR)
+    return AsyncOpenSearch
+
+
 def _import_bulk() -> Any:
     """Import bulk if available, otherwise raise error."""
     try:
@@ -38,6 +51,15 @@ def _import_bulk() -> Any:
     return bulk
 
 
+def _import_async_bulk() -> Any:
+    """Import async_bulk if available, otherwise raise error."""
+    try:
+        from opensearchpy.helpers import async_bulk
+    except ImportError:
+        raise ImportError(IMPORT_ASYNC_OPENSEARCH_PY_ERROR)
+    return async_bulk
+
+
 def _import_not_found_error() -> Any:
     """Import not found error if available, otherwise raise error."""
     try:
@@ -60,6 +82,19 @@ def _get_opensearch_client(opensearch_url: str, **kwargs: Any) -> Any:
     return client
 
 
+def _get_async_opensearch_client(opensearch_url: str, **kwargs: Any) -> Any:
+    """Get AsyncOpenSearch client from the opensearch_url, otherwise raise error."""
+    try:
+        async_opensearch = _import_async_opensearch()
+        client = async_opensearch(opensearch_url, **kwargs)
+    except ValueError as e:
+        raise ImportError(
+            f"AsyncOpenSearch client string provided is not in proper format. "
+            f"Got error: {e} "
+        )
+    return client
+
+
 def _validate_embeddings_and_bulk_size(embeddings_length: int, bulk_size: int) -> None:
     """Validate Embeddings Length and Bulk Size."""
     if embeddings_length == 0:
@@ -141,6 +176,57 @@ def _bulk_ingest_embeddings(
     return return_ids
 
 
+async def _abulk_ingest_embeddings(
+    client: Any,
+    index_name: str,
+    embeddings: List[List[float]],
+    texts: Iterable[str],
+    metadatas: Optional[List[dict]] = None,
+    ids: Optional[List[str]] = None,
+    vector_field: str = "vector_field",
+    text_field: str = "text",
+    mapping: Optional[Dict] = None,
+    max_chunk_bytes: Optional[int] = 1 * 1024 * 1024,
+    is_aoss: bool = False,
+) -> List[str]:
+    """Bulk Ingest Embeddings into given index asynchronously using AsyncOpenSearch."""
+    if not mapping:
+        mapping = dict()
+
+    async_bulk = _import_async_bulk()
+    not_found_error = _import_not_found_error()
+    requests = []
+    return_ids = []
+
+    try:
+        await client.indices.get(index=index_name)
+    except not_found_error:
+        await client.indices.create(index=index_name, body=mapping)
+
+    for i, text in enumerate(texts):
+        metadata = metadatas[i] if metadatas else {}
+        _id = ids[i] if ids else str(uuid.uuid4())
+        request = {
+            "_op_type": "index",
+            "_index": index_name,
+            vector_field: embeddings[i],
+            text_field: text,
+            "metadata": metadata,
+        }
+        if is_aoss:
+            request["id"] = _id
+        else:
+            request["_id"] = _id
+        requests.append(request)
+        return_ids.append(_id)
+
+    await async_bulk(client, requests, max_chunk_bytes=max_chunk_bytes)
+    if not is_aoss:
+        await client.indices.refresh(index=index_name)
+
+    return return_ids
+
+
 def _default_scripting_text_mapping(
     dim: int,
     vector_field: str = "vector_field",
@@ -334,6 +420,7 @@ class OpenSearchVectorSearch(VectorStore):
         http_auth = kwargs.get("http_auth")
         self.is_aoss = _is_aoss_enabled(http_auth=http_auth)
         self.client = _get_opensearch_client(opensearch_url, **kwargs)
+        self.async_client = _get_async_opensearch_client(opensearch_url, **kwargs)
         self.engine = kwargs.get("engine")
 
     @property
@@ -381,6 +468,47 @@ class OpenSearchVectorSearch(VectorStore):
             is_aoss=self.is_aoss,
         )
 
+    async def __aadd(
+        self,
+        texts: Iterable[str],
+        embeddings: List[List[float]],
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+        bulk_size: int = 500,
+        **kwargs: Any,
+    ) -> List[str]:
+        _validate_embeddings_and_bulk_size(len(embeddings), bulk_size)
+        index_name = kwargs.get("index_name", self.index_name)
+        text_field = kwargs.get("text_field", "text")
+        dim = len(embeddings[0])
+        engine = kwargs.get("engine", "nmslib")
+        space_type = kwargs.get("space_type", "l2")
+        ef_search = kwargs.get("ef_search", 512)
+        ef_construction = kwargs.get("ef_construction", 512)
+        m = kwargs.get("m", 16)
+        vector_field = kwargs.get("vector_field", "vector_field")
+        max_chunk_bytes = kwargs.get("max_chunk_bytes", 1 * 1024 * 1024)
+
+        _validate_aoss_with_engines(self.is_aoss, engine)
+
+        mapping = _default_text_mapping(
+            dim, engine, space_type, ef_search, ef_construction, m, vector_field
+        )
+
+        return await _abulk_ingest_embeddings(
+            self.async_client,
+            index_name,
+            embeddings,
+            texts,
+            metadatas=metadatas,
+            ids=ids,
+            vector_field=vector_field,
+            text_field=text_field,
+            mapping=mapping,
+            max_chunk_bytes=max_chunk_bytes,
+            is_aoss=self.is_aoss,
+        )
+
     def add_texts(
         self,
         texts: Iterable[str],
@@ -417,6 +545,28 @@ class OpenSearchVectorSearch(VectorStore):
             **kwargs,
         )
 
+    async def aadd_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        ids: Optional[List[str]] = None,
+        bulk_size: int = 500,
+        **kwargs: Any,
+    ) -> List[str]:
+        """
+        Asynchronously run more texts through the embeddings
+        and add to the vectorstore.
+        """
+        embeddings = await self.embedding_function.aembed_documents(list(texts))
+        return await self.__aadd(
+            texts,
+            embeddings,
+            metadatas=metadatas,
+            ids=ids,
+            bulk_size=bulk_size,
+            **kwargs,
+        )
+
     def add_embeddings(
         self,
         text_embeddings: Iterable[Tuple[str, List[float]]],
@@ -486,6 +636,28 @@ class OpenSearchVectorSearch(VectorStore):
         else:
             return False
 
+    async def adelete(
+        self, ids: Optional[List[str]] = None, **kwargs: Any
+    ) -> Optional[bool]:
+        """Asynchronously delete by vector ID or other criteria.
+
+        Args:
+            ids: List of ids to delete.
+            **kwargs: Other keyword arguments that subclasses might use.
+
+        Returns:
+            Optional[bool]: True if deletion is successful,
+            False otherwise, None if not implemented.
+        """
+        if ids is None:
+            raise ValueError("No ids provided to delete.")
+
+        actions = [{"delete": {"_index": self.index_name, "_id": id_}} for id_ in ids]
+        response = await self.async_client.bulk(body=actions, **kwargs)
+        return not any(
+            item.get("delete", {}).get("error") for item in response["items"]
+        )
+
     def similarity_search(
         self, query: str, k: int = 4, **kwargs: Any
     ) -> List[Document]:

libs/community/tests/unit_tests/vectorstores/test_indexing_docs.py

@@ -91,5 +91,6 @@ def test_compatible_vectorstore_documentation() -> None:
         "ZepVectorStore",
         "Zilliz",
         "Lantern",
+        "OpenSearchVectorSearch",
     }
     assert compatible == documented