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			109 lines
		
	
	
		
			2.4 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
			
		
		
	
	
			109 lines
		
	
	
		
			2.4 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
{
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 "cells": [
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  {
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   "cell_type": "markdown",
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   "id": "683953b3",
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   "metadata": {},
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   "source": [
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    "# Milvus\n",
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    "\n",
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    "This notebook shows how to use functionality related to the Milvus vector database.\n",
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    "\n",
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    "To run, you should have a Milvus instance up and running: https://milvus.io/docs/install_standalone-docker.md"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 1,
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   "id": "aac9563e",
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
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    "from langchain.text_splitter import CharacterTextSplitter\n",
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    "from langchain.vectorstores import Milvus\n",
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    "from langchain.document_loaders import TextLoader"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 2,
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   "id": "a3c3999a",
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "from langchain.document_loaders import TextLoader\n",
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    "loader = TextLoader('../../../state_of_the_union.txt')\n",
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    "documents = loader.load()\n",
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    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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    "docs = text_splitter.split_documents(documents)\n",
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    "\n",
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    "embeddings = OpenAIEmbeddings()"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "id": "dcf88bdf",
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "vector_db = Milvus.from_documents(\n",
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    "    docs,\n",
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    "    embeddings,\n",
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    "    connection_args={\"host\": \"127.0.0.1\", \"port\": \"19530\"},\n",
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    ")"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "id": "a8c513ab",
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "docs = vector_db.similarity_search(query)"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "id": "fc516993",
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   "metadata": {},
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   "outputs": [],
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   "source": [
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    "docs[0]"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "id": "a359ed74",
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   "metadata": {},
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   "outputs": [],
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   "source": []
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  }
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 ],
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 "metadata": {
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  "kernelspec": {
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   "display_name": "Python 3 (ipykernel)",
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   "language": "python",
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   "name": "python3"
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  },
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  "language_info": {
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   "codemirror_mode": {
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    "name": "ipython",
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    "version": 3
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   },
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   "file_extension": ".py",
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   "mimetype": "text/x-python",
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   "name": "python",
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   "nbconvert_exporter": "python",
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   "pygments_lexer": "ipython3",
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   "version": "3.9.1"
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  }
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 },
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 "nbformat": 4,
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 "nbformat_minor": 5
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}
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