{ "cells": [ { "cell_type": "markdown", "id": "2b9582dc", "metadata": {}, "source": [ "# SingleStoreDB\n", ">[SingleStoreDB](https://singlestore.com/) is a high-performance distributed SQL database that supports deployment both in the [cloud](https://www.singlestore.com/cloud/) and on-premises. It provides vector storage, and vector functions including [dot_product](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/dot_product.html) and [euclidean_distance](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/euclidean_distance.html), thereby supporting AI applications that require text similarity matching. \n", "\n", "This tutorial illustrates how to [work with vector data in SingleStoreDB](https://docs.singlestore.com/managed-service/en/developer-resources/functional-extensions/working-with-vector-data.html)." ] }, { "cell_type": "code", "execution_count": null, "id": "e4a61a4d", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Establishing a connection to the database is facilitated through the singlestoredb Python connector.\n", "# Please ensure that this connector is installed in your working environment.\n", "!pip install singlestoredb" ] }, { "cell_type": "code", "execution_count": null, "id": "39a0132a", "metadata": {}, "outputs": [], "source": [ "import os\n", "import getpass\n", "\n", "# We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.\n", "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")" ] }, { "cell_type": "code", "execution_count": null, "id": "6104fde8", "metadata": {}, "outputs": [], "source": [ "from langchain.embeddings.openai import OpenAIEmbeddings\n", "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain.vectorstores import SingleStoreDB\n", "from langchain.document_loaders import TextLoader" ] }, { "cell_type": "code", "execution_count": null, "id": "7b45113c", "metadata": {}, "outputs": [], "source": [ "# Load text samples\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": "markdown", "id": "535b2687", "metadata": {}, "source": [ "There are several ways to establish a [connection](https://singlestoredb-python.labs.singlestore.com/generated/singlestoredb.connect.html) to the database. You can either set up environment variables or pass named parameters to the `SingleStoreDB constructor`. Alternatively, you may provide these parameters to the `from_documents` and `from_texts` methods." ] }, { "cell_type": "code", "execution_count": null, "id": "d0b316bf", "metadata": {}, "outputs": [], "source": [ "# Setup connection url as environment variable\n", "os.environ[\"SINGLESTOREDB_URL\"] = \"root:pass@localhost:3306/db\"\n", "\n", "# Load documents to the store\n", "docsearch = SingleStoreDB.from_documents(\n", " docs,\n", " embeddings,\n", " table_name=\"notebook\", # use table with a custom name\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "0eaa4297", "metadata": {}, "outputs": [], "source": [ "query = \"What did the president say about Ketanji Brown Jackson\"\n", "docs = docsearch.similarity_search(query) # Find documents that correspond to the query\n", "print(docs[0].page_content)" ] }, { "cell_type": "code", "execution_count": null, "id": "86efff90", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 5 }