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152 lines
3.4 KiB
Plaintext
152 lines
3.4 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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">[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",
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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": null,
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"id": "a62cff8a-bcf7-4e33-bbbc-76999c2e3e20",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install pymilvus"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7a0f9e02-8eb0-4aef-b11f-8861360472ee",
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"metadata": {},
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"source": [
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"We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
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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": "8b6ed9cd-81b9-46e5-9c20-5aafca2844d0",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"OpenAI API Key: ········\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
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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": 3,
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"id": "aac9563e",
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"metadata": {
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"tags": []
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},
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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": 4,
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"id": "a3c3999a",
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"metadata": {
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"tags": []
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},
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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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"tags": []
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},
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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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"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": "3.10.6"
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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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