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SingleStoreDB is a high-performance distributed SQL database that supports deployment both in the cloud and on-premises. It provides vector storage, and vector functions including dot_product and euclidean_distance, thereby supporting AI applications that require text similarity matching.

This notebook shows how to use a retriever that uses SingleStoreDB.

# Establishing a connection to the database is facilitated through the singlestoredb Python connector.
# Please ensure that this connector is installed in your working environment.
%pip install --upgrade --quiet singlestoredb

Create Retriever from vector store

import getpass
import os

# We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import SingleStoreDB
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

# Setup connection url as environment variable
os.environ["SINGLESTOREDB_URL"] = "root:pass@localhost:3306/db"

# Load documents to the store
docsearch = SingleStoreDB.from_documents(
table_name="notebook", # use table with a custom name

# create retriever from the vector store
retriever = docsearch.as_retriever(search_kwargs={"k": 2})

Search with retriever

result = retriever.invoke("What did the president say about Ketanji Brown Jackson")

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