langchain/docs/docs/integrations/retrievers/singlestoredb.ipynb

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    "# SingleStoreDB\n",
    "\n",
    ">[SingleStoreDB](https://singlestore.com/) is a high-performance distributed SQL database that supports deployment both in the [cloud](https://www.singlestore.com/cloud/) and on-premises. It provides vector storage, and vector functions including [dot_product](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/dot_product.html) and [euclidean_distance](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/euclidean_distance.html), thereby supporting AI applications that require text similarity matching. \n",
    "\n",
    "\n",
    "This notebook shows how to use a retriever that uses `SingleStoreDB`.\n"
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    "# Establishing a connection to the database is facilitated through the singlestoredb Python connector.\n",
    "# Please ensure that this connector is installed in your working environment.\n",
    "%pip install --upgrade --quiet  singlestoredb"
   ]
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   "source": [
    "## Create Retriever from vector store"
   ]
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   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "# We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
    "\n",
    "from langchain_community.document_loaders import TextLoader\n",
    "from langchain_community.vectorstores import SingleStoreDB\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "from langchain_text_splitters import CharacterTextSplitter\n",
    "\n",
    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
    "documents = loader.load()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "docs = text_splitter.split_documents(documents)\n",
    "\n",
    "embeddings = OpenAIEmbeddings()\n",
    "\n",
    "# Setup connection url as environment variable\n",
    "os.environ[\"SINGLESTOREDB_URL\"] = \"root:pass@localhost:3306/db\"\n",
    "\n",
    "# Load documents to the store\n",
    "docsearch = SingleStoreDB.from_documents(\n",
    "    docs,\n",
    "    embeddings,\n",
    "    table_name=\"notebook\",  # use table with a custom name\n",
    ")\n",
    "\n",
    "# create retriever from the vector store\n",
    "retriever = docsearch.as_retriever(search_kwargs={\"k\": 2})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0915db",
   "metadata": {},
   "source": [
    "## Search with retriever"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b605284d",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = retriever.invoke(\"What did the president say about Ketanji Brown Jackson\")\n",
    "print(docs[0].page_content)"
   ]
  }
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