Skip to main content
Kinetica is a database with integrated support for vector similarity search. It supports:
  • exact and approximate nearest neighbor search
  • L2 distance, inner product, and cosine distance
This notebook shows how to use a retriever based on Kinetica vector store (Kinetica). Please ensure that this connector is installed in your working environment: 
pip install -qU langchain-kinetica langchain-community langchain-openai
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key. 
import getpass
import os

from langchain_openai import OpenAIEmbeddings

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

embeddings = OpenAIEmbeddings()
You must set the database connection in the following environment variables. If you are using a virtual environment you can set them in the .env file of the project:
  • KINETICA_URL: Database connection URL (e.g. http://localhost:9191)
  • KINETICA_USER: Database user
  • KINETICA_PASSWD: Secure password.
from gpudb import GPUdb

from langchain_kinetica import KineticaSettings, KineticaVectorstore

kdbc = GPUdb.get_connection()

k_config = KineticaSettings(kdbc=kdbc)
k_config

2026-02-02 20:58:48.261 INFO     [GPUdb] Connected to Kinetica! (host=http://localhost:19191 api=7.2.3.3 server=7.2.3.5)

KineticaSettings(kdbc=<gpudb.gpudb.GPUdb object at 0x1141e7b90>, database='langchain', table='langchain_kinetica_embeddings', metric='l2')

​
Create the Vectorstore

from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter

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

# The Kinetica Module will try to create a table with the name of the collection.
# So, make sure that the collection name is unique and the user has the
# permission to create a table.

COLLECTION_NAME = "state_of_the_union_test"

vectorstore = KineticaVectorstore.from_documents(
    embedding=embeddings,
    documents=results,
    collection_name=COLLECTION_NAME,
    config=k_config,
    pre_delete_collection=True,
)

​
Search with retriever

from langchain_core.vectorstores import VectorStoreRetriever

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

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

print(results[0].page_content)

Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Edit this page on GitHub or file an issue.
Connect these docs to Claude, VSCode, and more via MCP for real-time answers.