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4adabd33ac
Added a notebook with examples of the creation of a retriever from the SingleStoreDB vector store, and further usage. Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
121 lines
3.6 KiB
Plaintext
121 lines
3.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab66dd43",
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"metadata": {},
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"source": [
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"# SingleStoreDB\n",
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"\n",
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">[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",
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"\n",
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"\n",
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"This notebook shows how to use a retriever that uses `SingleStoreDB`.\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": "51b49135-a61a-49e8-869d-7c1d76794cd7",
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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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"# 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": "markdown",
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"id": "aaf80e7f",
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"metadata": {},
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"source": [
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"## Create Retriever from 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": "bcb3c8c2",
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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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"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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"\n",
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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\n",
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"\n",
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"loader = TextLoader(\"../../modules/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()\n",
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"\n",
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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=\"notebook\", # use table with a custom name\n",
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")\n",
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"\n",
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"# create retriever from the vector store\n",
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"retriever = docsearch.as_retriever(search_kwargs={\"k\": 2})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fc0915db",
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"metadata": {},
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"source": [
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"## Search with retriever"
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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": 13,
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"id": "b605284d",
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"metadata": {},
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"outputs": [],
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"source": [
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"result = retriever.get_relevant_documents(\"What did the president say about Ketanji Brown Jackson\")\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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"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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