langchain/docs/modules/indexes/vectorstores/examples/tigris.ipynb

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   "source": [
    "# Tigris\n",
    "\n",
    "> [Tigris](htttps://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",
   "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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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "!pip install tigrisdb openapi-schema-pydantic openai tiktoken"
   ],
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   }
  },
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   "cell_type": "markdown",
   "source": [
    "We will load the `OpenAI` api key and `Tigris` credentials in our environment"
   ],
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import os\n",
    "import getpass\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.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.vectorstores import Tigris\n",
    "from langchain.document_loaders import TextLoader"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Initialize Tigris vector store\n",
    "Let's import our test dataset:"
   ],
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   "cell_type": "code",
   "execution_count": null,
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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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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "vector_store = Tigris.from_documents(docs, embeddings, index_name=\"my_embeddings\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Similarity Search"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "found_docs = vector_store.similarity_search(query)\n",
    "print(found_docs)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Similarity Search with score (vector distance)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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}\")"
   ],
   "metadata": {
    "collapsed": false
   }
  }
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