community: add SingleStoreDB semantic cache (#23218)

This PR adds a `SingleStoreDBSemanticCache` class that implements a
cache based on SingleStoreDB vector store, integration tests, and a
notebook example.

Additionally, this PR contains minor changes to SingleStoreDB vector
store:
 - change add texts/documents methods to return a list of inserted ids
 - implement delete(ids) method to delete documents by list of ids
 - added drop() method to drop a correspondent database table
- updated integration tests to use and check functionality implemented
above


CC: @baskaryan, @hwchase17

---------

Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>

docs/docs/how_to/indexing.ipynb

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

docs/docs/integrations/llm_caching.ipynb

@@ -2147,6 +2147,32 @@
     "llm(\"Tell me one joke\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SingleStoreDB Semantic Cache\n",
+    "You can use [SingleStoreDB](https://python.langchain.com/docs/integrations/vectorstores/singlestoredb/) as a semantic cache to cache prompts and responses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d82f1bdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.cache import SingleStoreDBSemanticCache\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "\n",
+    "set_llm_cache(\n",
+    "    SingleStoreDBSemanticCache(\n",
+    "        embedding=OpenAIEmbeddings(),\n",
+    "        host=\"root:pass@localhost:3306/db\",\n",
+    "    )\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ae1f5e1c-085e-4998-9f2d-b5867d2c3d5b",
@@ -2178,7 +2204,7 @@
    "source": [
     "**Cache** classes are implemented by inheriting the [BaseCache](https://api.python.langchain.com/en/latest/caches/langchain_core.caches.BaseCache.html) class.\n",
     "\n",
-    "This table lists all 20 derived classes with links to the API Reference.\n",
+    "This table lists all 21 derived classes with links to the API Reference.\n",
     "\n",
     "\n",
     "| Namespace 🔻 | Class |\n",
@@ -2195,6 +2221,7 @@
     "| langchain_community.cache | [MomentoCache](https://api.python.langchain.com/en/latest/cache/langchain_community.cache.MomentoCache.html) |\n",
     "| langchain_community.cache | [OpenSearchSemanticCache](https://api.python.langchain.com/en/latest/cache/langchain_community.cache.OpenSearchSemanticCache.html) |\n",
     "| langchain_community.cache | [RedisSemanticCache](https://api.python.langchain.com/en/latest/cache/langchain_community.cache.RedisSemanticCache.html) |\n",
+    "| langchain_community.cache | [SingleStoreDBSemanticCache](https://api.python.langchain.com/en/latest/cache/langchain_community.cache.SingleStoreDBSemanticCache.html) |\n",
     "| langchain_community.cache | [SQLAlchemyCache](https://api.python.langchain.com/en/latest/cache/langchain_community.cache.SQLAlchemyCache.html) |\n",
     "| langchain_community.cache | [SQLAlchemyMd5Cache](https://api.python.langchain.com/en/latest/cache/langchain_community.cache.SQLAlchemyMd5Cache.html) |\n",
     "| langchain_community.cache | [UpstashRedisCache](https://api.python.langchain.com/en/latest/cache/langchain_community.cache.UpstashRedisCache.html) |\n",

libs/community/langchain_community/cache.py

@@ -58,6 +58,7 @@ from langchain_community.vectorstores.azure_cosmos_db import (
     CosmosDBSimilarityType,
     CosmosDBVectorSearchType,
 )
+from langchain_community.vectorstores.utils import DistanceStrategy
 
 try:
     from sqlalchemy.orm import declarative_base
@@ -84,6 +85,7 @@ from langchain_community.vectorstores import (
     OpenSearchVectorSearch as OpenSearchVectorStore,
 )
 from langchain_community.vectorstores.redis import Redis as RedisVectorstore
+from langchain_community.vectorstores.singlestoredb import SingleStoreDB
 
 logger = logging.getLogger(__file__)
 
@@ -2373,3 +2375,221 @@ class OpenSearchSemanticCache(BaseCache):
         if index_name in self._cache_dict:
             self._cache_dict[index_name].delete_index(index_name=index_name)
             del self._cache_dict[index_name]
+
+
+class SingleStoreDBSemanticCache(BaseCache):
+    """Cache that uses SingleStore DB as a backend"""
+
+    def __init__(
+        self,
+        embedding: Embeddings,
+        *,
+        cache_table_prefix: str = "cache_",
+        search_threshold: float = 0.2,
+        **kwargs: Any,
+    ):
+        """Initialize with necessary components.
+
+        Args:
+            embedding (Embeddings): A text embedding model.
+            cache_table_prefix (str, optional): Prefix for the cache table name.
+                Defaults to "cache_".
+            search_threshold (float, optional): The minimum similarity score for
+                a search result to be considered a match. Defaults to 0.2.
+
+            Following arguments pertrain to the SingleStoreDB vector store:
+
+            distance_strategy (DistanceStrategy, optional):
+                Determines the strategy employed for calculating
+                the distance between vectors in the embedding space.
+                Defaults to DOT_PRODUCT.
+                Available options are:
+                - DOT_PRODUCT: Computes the scalar product of two vectors.
+                    This is the default behavior
+                - EUCLIDEAN_DISTANCE: Computes the Euclidean distance between
+                    two vectors. This metric considers the geometric distance in
+                    the vector space, and might be more suitable for embeddings
+                    that rely on spatial relationships. This metric is not
+                    compatible with the WEIGHTED_SUM search strategy.
+
+            content_field (str, optional): Specifies the field to store the content.
+                Defaults to "content".
+            metadata_field (str, optional): Specifies the field to store metadata.
+                Defaults to "metadata".
+            vector_field (str, optional): Specifies the field to store the vector.
+                Defaults to "vector".
+            id_field (str, optional): Specifies the field to store the id.
+                Defaults to "id".
+
+            use_vector_index (bool, optional): Toggles the use of a vector index.
+                Works only with SingleStoreDB 8.5 or later. Defaults to False.
+                If set to True, vector_size parameter is required to be set to
+                a proper value.
+
+            vector_index_name (str, optional): Specifies the name of the vector index.
+                Defaults to empty. Will be ignored if use_vector_index is set to False.
+
+            vector_index_options (dict, optional): Specifies the options for
+                the vector index. Defaults to {}.
+                Will be ignored if use_vector_index is set to False. The options are:
+                index_type (str, optional): Specifies the type of the index.
+                    Defaults to IVF_PQFS.
+                For more options, please refer to the SingleStoreDB documentation:
+                https://docs.singlestore.com/cloud/reference/sql-reference/vector-functions/vector-indexing/
+
+            vector_size (int, optional): Specifies the size of the vector.
+                Defaults to 1536. Required if use_vector_index is set to True.
+                Should be set to the same value as the size of the vectors
+                stored in the vector_field.
+
+            Following arguments pertain to the connection pool:
+
+            pool_size (int, optional): Determines the number of active connections in
+                the pool. Defaults to 5.
+            max_overflow (int, optional): Determines the maximum number of connections
+                allowed beyond the pool_size. Defaults to 10.
+            timeout (float, optional): Specifies the maximum wait time in seconds for
+                establishing a connection. Defaults to 30.
+
+            Following arguments pertain to the database connection:
+
+            host (str, optional): Specifies the hostname, IP address, or URL for the
+                database connection. The default scheme is "mysql".
+            user (str, optional): Database username.
+            password (str, optional): Database password.
+            port (int, optional): Database port. Defaults to 3306 for non-HTTP
+                connections, 80 for HTTP connections, and 443 for HTTPS connections.
+            database (str, optional): Database name.
+
+            Additional optional arguments provide further customization over the
+            database connection:
+
+            pure_python (bool, optional): Toggles the connector mode. If True,
+                operates in pure Python mode.
+            local_infile (bool, optional): Allows local file uploads.
+            charset (str, optional): Specifies the character set for string values.
+            ssl_key (str, optional): Specifies the path of the file containing the SSL
+                key.
+            ssl_cert (str, optional): Specifies the path of the file containing the SSL
+                certificate.
+            ssl_ca (str, optional): Specifies the path of the file containing the SSL
+                certificate authority.
+            ssl_cipher (str, optional): Sets the SSL cipher list.
+            ssl_disabled (bool, optional): Disables SSL usage.
+            ssl_verify_cert (bool, optional): Verifies the server's certificate.
+                Automatically enabled if ``ssl_ca`` is specified.
+            ssl_verify_identity (bool, optional): Verifies the server's identity.
+            conv (dict[int, Callable], optional): A dictionary of data conversion
+                functions.
+            credential_type (str, optional): Specifies the type of authentication to
+                use: auth.PASSWORD, auth.JWT, or auth.BROWSER_SSO.
+            autocommit (bool, optional): Enables autocommits.
+            results_type (str, optional): Determines the structure of the query results:
+                tuples, namedtuples, dicts.
+            results_format (str, optional): Deprecated. This option has been renamed to
+                results_type.
+
+        Examples:
+            Basic Usage:
+
+            .. code-block:: python
+
+                import langchain
+                from langchain.cache import SingleStoreDBSemanticCache
+                from langchain.embeddings import OpenAIEmbeddings
+
+                langchain.llm_cache = SingleStoreDBSemanticCache(
+                    embedding=OpenAIEmbeddings(),
+                    host="https://user:password@127.0.0.1:3306/database"
+                )
+
+            Advanced Usage:
+
+            .. code-block:: python
+
+                import langchain
+                from langchain.cache import SingleStoreDBSemanticCache
+                from langchain.embeddings import OpenAIEmbeddings
+
+                langchain.llm_cache = = SingleStoreDBSemanticCache(
+                    embeddings=OpenAIEmbeddings(),
+                    use_vector_index=True,
+                    host="127.0.0.1",
+                    port=3306,
+                    user="user",
+                    password="password",
+                    database="db",
+                    table_name="my_custom_table",
+                    pool_size=10,
+                    timeout=60,
+                )
+        """
+
+        self._cache_dict: Dict[str, SingleStoreDB] = {}
+        self.embedding = embedding
+        self.cache_table_prefix = cache_table_prefix
+        self.search_threshold = search_threshold
+
+        # Pass the rest of the kwargs to the connection.
+        self.connection_kwargs = kwargs
+
+    def _index_name(self, llm_string: str) -> str:
+        hashed_index = _hash(llm_string)
+        return f"{self.cache_table_prefix}{hashed_index}"
+
+    def _get_llm_cache(self, llm_string: str) -> SingleStoreDB:
+        index_name = self._index_name(llm_string)
+
+        # return vectorstore client for the specific llm string
+        if index_name not in self._cache_dict:
+            self._cache_dict[index_name] = SingleStoreDB(
+                embedding=self.embedding,
+                table_name=index_name,
+                **self.connection_kwargs,
+            )
+        return self._cache_dict[index_name]
+
+    def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
+        """Look up based on prompt and llm_string."""
+        llm_cache = self._get_llm_cache(llm_string)
+        generations: List = []
+        # Read from a Hash
+        results = llm_cache.similarity_search_with_score(
+            query=prompt,
+            k=1,
+        )
+        if results:
+            for document_score in results:
+                if (
+                    document_score[1] > self.search_threshold
+                    and llm_cache.distance_strategy == DistanceStrategy.DOT_PRODUCT
+                ) or (
+                    document_score[1] < self.search_threshold
+                    and llm_cache.distance_strategy
+                    == DistanceStrategy.EUCLIDEAN_DISTANCE
+                ):
+                    generations.extend(loads(document_score[0].metadata["return_val"]))
+        return generations if generations else None
+
+    def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
+        """Update cache based on prompt and llm_string."""
+        for gen in return_val:
+            if not isinstance(gen, Generation):
+                raise ValueError(
+                    "SingleStoreDBSemanticCache only supports caching of "
+                    f"normal LLM generations, got {type(gen)}"
+                )
+        llm_cache = self._get_llm_cache(llm_string)
+        metadata = {
+            "llm_string": llm_string,
+            "prompt": prompt,
+            "return_val": dumps([g for g in return_val]),
+        }
+        llm_cache.add_texts(texts=[prompt], metadatas=[metadata])
+
+    def clear(self, **kwargs: Any) -> None:
+        """Clear semantic cache for a given llm_string."""
+        index_name = self._index_name(kwargs["llm_string"])
+        if index_name in self._cache_dict:
+            self._cache_dict[index_name].drop()
+            del self._cache_dict[index_name]

libs/community/langchain_community/chat_message_histories/singlestoredb.py

@@ -153,7 +153,7 @@ class SingleStoreDBChatMessageHistory(BaseChatMessageHistory):
             self.connection_kwargs["conn_attrs"] = dict()
 
         self.connection_kwargs["conn_attrs"]["_connector_name"] = "langchain python sdk"
-        self.connection_kwargs["conn_attrs"]["_connector_version"] = "1.0.1"
+        self.connection_kwargs["conn_attrs"]["_connector_version"] = "2.1.0"
 
         # Create a connection pool.
         try:

libs/community/langchain_community/vectorstores/singlestoredb.py

@@ -276,7 +276,7 @@ class SingleStoreDB(VectorStore):
             self.connection_kwargs["conn_attrs"] = dict()
 
         self.connection_kwargs["conn_attrs"]["_connector_name"] = "langchain python sdk"
-        self.connection_kwargs["conn_attrs"]["_connector_version"] = "2.0.0"
+        self.connection_kwargs["conn_attrs"]["_connector_version"] = "2.1.0"
 
         # Create connection pool.
         self.connection_pool = QueuePool(
@@ -354,6 +354,7 @@ class SingleStoreDB(VectorStore):
         uris: List[str],
         metadatas: Optional[List[dict]] = None,
         embeddings: Optional[List[List[float]]] = None,
+        return_ids: bool = False,
         **kwargs: Any,
     ) -> List[str]:
         """Run images through the embeddings and add to the vectorstore.
@@ -367,7 +368,8 @@ class SingleStoreDB(VectorStore):
                 embeddings. Defaults to None.
 
         Returns:
-            List[str]: empty list
+            List[str]: list of document ids added to the vectorstore
+                if return_ids is True. Otherwise, an empty list.
         """
         # Set embeddings
         if (
@@ -376,13 +378,16 @@ class SingleStoreDB(VectorStore):
             and hasattr(self.embedding, "embed_image")
         ):
             embeddings = self.embedding.embed_image(uris=uris)
-        return self.add_texts(uris, metadatas, embeddings, **kwargs)
+        return self.add_texts(
+            uris, metadatas, embeddings, return_ids=return_ids, **kwargs
+        )
 
     def add_texts(
         self,
         texts: Iterable[str],
         metadatas: Optional[List[dict]] = None,
         embeddings: Optional[List[List[float]]] = None,
+        return_ids: bool = False,
         **kwargs: Any,
     ) -> List[str]:
         """Add more texts to the vectorstore.
@@ -395,8 +400,10 @@ class SingleStoreDB(VectorStore):
                 embeddings. Defaults to None.
 
         Returns:
-            List[str]: empty list
+            List[str]: list of document ids added to the vectorstore
+                if return_ids is True. Otherwise, an empty list.
         """
+        ids: List[str] = []
         conn = self.connection_pool.connect()
         try:
             cur = conn.cursor()
@@ -424,13 +431,48 @@ class SingleStoreDB(VectorStore):
                             json.dumps(metadata),
                         ),
                     )
+                    if return_ids:
+                        cur.execute("SELECT LAST_INSERT_ID();")
+                        row = cur.fetchone()
+                        if row:
+                            ids.append(str(row[0]))
                 if self.use_vector_index or self.use_full_text_search:
                     cur.execute("OPTIMIZE TABLE {} FLUSH;".format(self.table_name))
             finally:
                 cur.close()
         finally:
             conn.close()
-        return []
+        return ids
+
+    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> bool | None:
+        """Delete documents from the vectorstore.
+
+        Args:
+            ids (List[str], optional): List of document ids to delete.
+                If None, all documents will be deleted. Defaults to None.
+
+        Returns:
+            bool: True if deletion was successful, False otherwise.
+        """
+        if ids is None:
+            return True
+
+        conn = self.connection_pool.connect()
+        try:
+            cur = conn.cursor()
+            try:
+                cur.execute(
+                    "DELETE FROM {} WHERE {} IN ({})".format(
+                        self.table_name, self.id_field, ",".join(ids)
+                    )
+                )
+                if self.use_vector_index or self.use_full_text_search:
+                    cur.execute("OPTIMIZE TABLE {} FLUSH;".format(self.table_name))
+            finally:
+                cur.close()
+        finally:
+            conn.close()
+        return True
 
     def similarity_search(
         self,
@@ -995,6 +1037,20 @@ class SingleStoreDB(VectorStore):
         instance.add_texts(texts, metadatas, embedding.embed_documents(texts), **kwargs)
         return instance
 
+    def drop(self) -> None:
+        """Drop the table and delete all data from the vectorstore.
+        Vector store will be unusable after this operation.
+        """
+        conn = self.connection_pool.connect()
+        try:
+            cur = conn.cursor()
+            try:
+                cur.execute("DROP TABLE IF EXISTS {}".format(self.table_name))
+            finally:
+                cur.close()
+        finally:
+            conn.close()
+
 
 # SingleStoreDBRetriever is not needed, but we keep it for backwards compatibility
 SingleStoreDBRetriever = VectorStoreRetriever

libs/community/tests/integration_tests/cache/test_singlestoredb_cache.py

@@ -0,0 +1,43 @@
+"""Test SingleStoreDB semantic cache. Requires a SingleStore DB database.
+
+Required to run this test:
+    - a recent `singlestoredb` Python package available
+    - a SingleStore DB instance;
+"""
+
+from importlib.util import find_spec
+
+import pytest
+from langchain_core.globals import get_llm_cache, set_llm_cache
+from langchain_core.outputs import Generation
+
+from langchain_community.cache import SingleStoreDBSemanticCache
+from tests.integration_tests.cache.fake_embeddings import FakeEmbeddings
+from tests.unit_tests.llms.fake_llm import FakeLLM
+
+TEST_SINGLESTOREDB_URL = "root:pass@localhost:3306/db"
+
+singlestoredb_installed = find_spec("singlestoredb") is not None
+
+
+@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
+def test_tinglestoredb_semantic_cache() -> None:
+    """Test opensearch semantic cache functionality."""
+    set_llm_cache(
+        SingleStoreDBSemanticCache(
+            embedding=FakeEmbeddings(),
+            host=TEST_SINGLESTOREDB_URL,
+            search_threshold=0.0,
+        )
+    )
+    llm = FakeLLM()
+    params = llm.dict()
+    params["stop"] = None
+    llm_string = str(sorted([(k, v) for k, v in params.items()]))
+    get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
+    cache_output = get_llm_cache().lookup("bar", llm_string)
+    assert cache_output == [Generation(text="fizz")]
+
+    get_llm_cache().clear(llm_string=llm_string)
+    output = get_llm_cache().lookup("bar", llm_string)
+    assert output != [Generation(text="fizz")]

libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py

@@ -152,7 +152,7 @@ def test_singlestoredb(texts: List[str]) -> None:
     )
     output = docsearch.similarity_search("foo", k=1)
     assert output == TEST_SINGLE_RESULT
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -169,7 +169,7 @@ def test_singlestoredb_new_vector(texts: List[str]) -> None:
     docsearch.add_texts(["foo"])
     output = docsearch.similarity_search("foo", k=2)
     assert output == TEST_RESULT
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -187,7 +187,7 @@ def test_singlestoredb_euclidean_distance(texts: List[str]) -> None:
     docsearch.add_texts(["foo"])
     output = docsearch.similarity_search("foo", k=2)
     assert output == TEST_RESULT
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -207,7 +207,7 @@ def test_singlestoredb_vector_index_1(texts: List[str]) -> None:
     docsearch.add_texts(["foo"])
     output = docsearch.similarity_search("foo", k=2)
     assert output == TEST_RESULT
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -227,7 +227,7 @@ def test_singlestoredb_vector_index_2(texts: List[str]) -> None:
     docsearch.add_texts(["foo"])
     output = docsearch.similarity_search("foo", k=1)
     output[0].page_content == "foo"
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -247,7 +247,7 @@ def test_singlestoredb_vector_index_large() -> None:
     )
     output = docsearch.similarity_search("foo", k=1)
     assert output[0].page_content == "foo"
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -269,7 +269,7 @@ def test_singlestoredb_from_existing(texts: List[str]) -> None:
     )
     output = docsearch2.similarity_search("foo", k=1)
     assert output == TEST_SINGLE_RESULT
-    drop(table_name)
+    docsearch2.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -286,7 +286,7 @@ def test_singlestoredb_from_documents(texts: List[str]) -> None:
     )
     output = docsearch.similarity_search("foo", k=1)
     assert output == TEST_SINGLE_WITH_METADATA_RESULT
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -309,7 +309,7 @@ def test_singlestoredb_add_texts_to_existing(texts: List[str]) -> None:
     docsearch.add_texts(["foo"])
     output = docsearch.similarity_search("foo", k=2)
     assert output == TEST_RESULT
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -329,7 +329,7 @@ def test_singlestoredb_filter_metadata(texts: List[str]) -> None:
     )
     output = docsearch.similarity_search("foo", k=1, filter={"index": 2})
     assert output == [Document(page_content="baz", metadata={"index": 2})]
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -352,7 +352,7 @@ def test_singlestoredb_filter_metadata_2(texts: List[str]) -> None:
     assert output == [
         Document(page_content="foo", metadata={"index": 0, "category": "budget"})
     ]
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -377,7 +377,7 @@ def test_singlestoredb_filter_metadata_3(texts: List[str]) -> None:
     assert output == [
         Document(page_content="bar", metadata={"index": 1, "category": "budget"})
     ]
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -398,7 +398,7 @@ def test_singlestoredb_filter_metadata_4(texts: List[str]) -> None:
     )
     output = docsearch.similarity_search("foo", k=1, filter={"category": "vacation"})
     assert output == []
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -437,7 +437,7 @@ def test_singlestoredb_filter_metadata_5(texts: List[str]) -> None:
             },
         )
     ]
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -468,7 +468,7 @@ def test_singlestoredb_filter_metadata_6(texts: List[str]) -> None:
             metadata={"index": 1, "category": "budget", "is_good": True},
         )
     ]
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -499,7 +499,7 @@ def test_singlestoredb_filter_metadata_7(texts: List[str]) -> None:
             metadata={"index": 2, "category": "budget", "score": 2.5},
         )
     ]
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -523,7 +523,7 @@ def test_singlestoredb_as_retriever(texts: List[str]) -> None:
             page_content="bar",
         ),
     ]
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -546,7 +546,7 @@ def test_singlestoredb_add_image(texts: List[str]) -> None:
     docsearch.add_images(temp_files)
     output = docsearch.similarity_search("foo", k=1)
     assert output[0].page_content in temp_files
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -571,7 +571,7 @@ def test_singestoredb_add_image2() -> None:
     docsearch.add_images(image_uris)
     output = docsearch.similarity_search("horse", k=1)
     assert "horse" in output[0].page_content
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -610,7 +610,7 @@ def test_singlestoredb_text_only_search(snow_rain_docs: List[Document]) -> None:
         "Blanketing the countryside in a soft, pristine layer,"
         in output[0].page_content
     )
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -667,7 +667,7 @@ def test_singlestoredb_filter_by_vector_search1(snow_rain_docs: List[Document])
         "High in the mountains, the rain transformed into a delicate"
         in output[0].page_content
     )
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -697,7 +697,7 @@ def test_singlestoredb_filter_by_vector_search2(snow_rain_docs: List[Document])
         "Amidst the bustling cityscape, the rain fell relentlessly"
         in output[0].page_content
     )
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -725,7 +725,7 @@ def test_singlestoredb_weighted_sum_search_unsupported_strategy(
         )
     except ValueError as e:
         assert "Search strategy WEIGHTED_SUM is not" in str(e)
-    drop(table_name)
+    docsearch.drop()
 
 
 @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
@@ -752,4 +752,49 @@ def test_singlestoredb_weighted_sum_search(snow_rain_docs: List[Document]) -> No
     assert (
         "Atop the rugged peaks, snow fell with an unyielding" in output[0].page_content
     )
+    docsearch.drop()
+
+
+@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
+def test_insert(snow_rain_docs: List[Document]) -> None:
+    table_name = "test_singlestoredb_insert"
     drop(table_name)
+    embeddings = IncrementalEmbeddings()
+    docsearch = SingleStoreDB(
+        embeddings,
+        table_name=table_name,
+        host=TEST_SINGLESTOREDB_URL,
+    )
+    ids = docsearch.add_documents(snow_rain_docs, return_ids=True)
+    assert len(ids) == len(snow_rain_docs)
+    for i, id1 in enumerate(ids):
+        for j, id2 in enumerate(ids):
+            if i != j:
+                assert id1 != id2
+    docsearch.drop()
+
+
+@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed")
+def test_delete(snow_rain_docs: List[Document]) -> None:
+    table_name = "test_singlestoredb_delete"
+    drop(table_name)
+    embeddings = IncrementalEmbeddings()
+    docsearch = SingleStoreDB(
+        embeddings,
+        table_name=table_name,
+        host=TEST_SINGLESTOREDB_URL,
+    )
+    ids = docsearch.add_documents(snow_rain_docs, return_ids=True)
+    output = docsearch.similarity_search(
+        "rainstorm in parched desert",
+        k=3,
+        filter={"count": "1"},
+    )
+    assert len(output) == 2
+    docsearch.delete(ids)
+    output = docsearch.similarity_search(
+        "rainstorm in parched desert",
+        k=3,
+    )
+    assert len(output) == 0
+    docsearch.drop()

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

@@ -84,6 +84,7 @@ def test_compatible_vectorstore_documentation() -> None:
         "Rockset",
         "ScaNN",
         "SemaDB",
+        "SingleStoreDB",
         "SupabaseVectorStore",
         "SurrealDBStore",
         "TileDB",