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- Added `SingleStoreDB` vector store, which is a wrapper over the SingleStore DB database, that can be used as a vector storage and has an efficient similarity search. - Added integration tests for the vector store - Added jupyter notebook with the example @dev2049 --------- Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
140 lines
4.2 KiB
Plaintext
140 lines
4.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "2b9582dc",
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"metadata": {},
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"source": [
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"# SingleStoreDB vector search\n",
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"[SingleStore DB](https://singlestore.com) is a high-performance distributed database that supports deployment both in the [cloud](https://www.singlestore.com/cloud/) and on-premises. For a significant duration, it has provided support for vector functions such as [dot_product](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/dot_product.html), thereby positioning itself as an ideal solution for AI applications that require text similarity matching. \n",
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"This tutorial illustrates how to utilize the features of the SingleStore DB Vector Store."
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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": "e4a61a4d",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# Establishing a connection to the database is facilitated through the singlestoredb Python connector.\n",
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"# Please ensure that this connector is installed in your working environment.\n",
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"!pip install singlestoredb"
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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": "39a0132a",
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"metadata": {},
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"outputs": [],
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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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"# We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.\n",
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"os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')\n"
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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": "6104fde8",
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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 SingleStoreDB\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": null,
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"id": "7b45113c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load text samples \n",
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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": "markdown",
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"id": "535b2687",
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"metadata": {},
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"source": [
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"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."
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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": "d0b316bf",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Setup connection url as environment variable\n",
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"os.environ['SINGLESTOREDB_URL'] = 'root:pass@localhost:3306/db'\n",
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"\n",
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"# Load documents to the store\n",
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"docsearch = SingleStoreDB.from_documents(\n",
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" docs,\n",
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" embeddings,\n",
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" table_name = \"noteook\", # use table with a custom name \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": "0eaa4297",
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = docsearch.similarity_search(query) # Find documents that correspond to the query\n",
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"print(docs[0].page_content)"
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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": "86efff90",
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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": "3.9.2"
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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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