langchain/docs/extras/integrations/vectorstores/singlestoredb.ipynb

SingleStoreDB

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 tutorial illustrates how to work with vector data in SingleStoreDB.

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# 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 singlestoredb

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import os
import getpass

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

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from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import SingleStoreDB
from langchain.document_loaders import TextLoader

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# Load text samples
loader = TextLoader("../../../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()

There are several ways to establish a connection 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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# Setup connection url as environment variable
os.environ["SINGLESTOREDB_URL"] = "root:pass@localhost:3306/db"

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

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query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)  # Find documents that correspond to the query
print(docs[0].page_content)

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