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

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  {
   "cell_type": "markdown",
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   "source": [
    "# Milvus\n",
    "\n",
    ">[Milvus](https://milvus.io/docs/overview.md) is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.\n",
    "\n",
    "This notebook shows how to use functionality related to the Milvus vector database.\n",
    "\n",
    "To run, you should have a [Milvus instance up and running](https://milvus.io/docs/install_standalone-docker.md)."
   ]
  },
  {
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   "execution_count": null,
   "id": "a62cff8a-bcf7-4e33-bbbc-76999c2e3e20",
   "metadata": {
    "tags": []
   },
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   "source": [
    "!pip install pymilvus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a0f9e02-8eb0-4aef-b11f-8861360472ee",
   "metadata": {},
   "source": [
    "We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b6ed9cd-81b9-46e5-9c20-5aafca2844d0",
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    "tags": []
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    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "OpenAI API Key: ········\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import getpass\n",
    "\n",
    "os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aac9563e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.vectorstores import Milvus\n",
    "from langchain.document_loaders import TextLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3c3999a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.document_loaders import TextLoader\n",
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcf88bdf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vector_db = Milvus.from_documents(\n",
    "    docs,\n",
    "    embeddings,\n",
    "    connection_args={\"host\": \"127.0.0.1\", \"port\": \"19530\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8c513ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = vector_db.similarity_search(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc516993",
   "metadata": {},
   "outputs": [],
   "source": [
    "docs[0]"
   ]
  }
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