langchain/docs/versioned_docs/version-0.2.x/integrations/vectorstores/tigris.ipynb

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    "# Tigris\n",
    "\n",
    "> [Tigris](https://tigrisdata.com) is an open-source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications.\n",
    "> `Tigris` eliminates the infrastructure complexity of managing, operating, and synchronizing multiple tools, allowing you to focus on building great applications instead."
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook guides you how to use Tigris as your VectorStore"
   ]
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   "source": [
    "**Pre requisites**\n",
    "1. An OpenAI account. You can sign up for an account [here](https://platform.openai.com/)\n",
    "2. [Sign up for a free Tigris account](https://console.preview.tigrisdata.cloud). Once you have signed up for the Tigris account, create a new project called `vectordemo`. Next, make a note of the *Uri* for the region you've created your project in, the **clientId** and **clientSecret**. You can get all this information from the **Application Keys** section of the project."
   ]
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   "source": [
    "Let's first install our dependencies:"
   ]
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   "source": [
    "%pip install --upgrade --quiet  tigrisdb openapi-schema-pydantic langchain-openai tiktoken"
   ]
  },
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   "source": [
    "We will load the `OpenAI` api key and `Tigris` credentials in our environment"
   ]
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   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
    "os.environ[\"TIGRIS_PROJECT\"] = getpass.getpass(\"Tigris Project Name:\")\n",
    "os.environ[\"TIGRIS_CLIENT_ID\"] = getpass.getpass(\"Tigris Client Id:\")\n",
    "os.environ[\"TIGRIS_CLIENT_SECRET\"] = getpass.getpass(\"Tigris Client Secret:\")"
   ]
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   "source": [
    "from langchain_community.document_loaders import TextLoader\n",
    "from langchain_community.vectorstores import Tigris\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "from langchain_text_splitters import CharacterTextSplitter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Tigris vector store\n",
    "Let's import our test dataset:"
   ]
  },
  {
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   "source": [
    "loader = TextLoader(\"../../../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()"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "vector_store = Tigris.from_documents(docs, embeddings, index_name=\"my_embeddings\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "found_docs = vector_store.similarity_search(query)\n",
    "print(found_docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity Search with score (vector distance)"
   ]
  },
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   "outputs": [],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "result = vector_store.similarity_search_with_score(query)\n",
    "for doc, score in result:\n",
    "    print(f\"document={doc}, score={score}\")"
   ]
  }
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