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Kinetica Vectorstore API

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 the Kinetica vector store (Kinetica).

This needs an instance of Kinetica which can easily be setup using the instructions given here - installation instruction.

# Pip install necessary package
%pip install --upgrade --quiet  langchain-openai langchain-community
%pip install gpudb
%pip install --upgrade --quiet  tiktoken

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
## Loading Environment Variables
from dotenv import load_dotenv

load_dotenv()
False
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import (
    DistanceStrategy,
    Kinetica,
    KineticaSettings,
)
from langchain_openai import OpenAIEmbeddings


embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
# Kinetica needs the connection to the database.
# This is how to set it up.
HOST = os.getenv("KINETICA_HOST", "http://127.0.0.1:9191")
USERNAME = os.getenv("KINETICA_USERNAME", "admin")
PASSWORD = os.getenv("KINETICA_PASSWORD", "Kinetica1!")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")


def create_config() -> KineticaSettings:
    return KineticaSettings(host=HOST, username=USERNAME, password=PASSWORD)
from uuid import uuid4

from langchain_core.documents import Document

document_1 = Document(
    page_content="I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
    metadata={"source": "tweet"},
)

document_2 = Document(
    page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
    metadata={"source": "news"},
)

document_3 = Document(
    page_content="Building an exciting new project with LangChain - come check it out!",
    metadata={"source": "tweet"},
)

document_4 = Document(
    page_content="Robbers broke into the city bank and stole $1 million in cash.",
    metadata={"source": "news"},
)

document_5 = Document(
    page_content="Wow! That was an amazing movie. I can't wait to see it again.",
    metadata={"source": "tweet"},
)

document_6 = Document(
    page_content="Is the new iPhone worth the price? Read this review to find out.",
    metadata={"source": "website"},
)

document_7 = Document(
    page_content="The top 10 soccer players in the world right now.",
    metadata={"source": "website"},
)

document_8 = Document(
    page_content="LangGraph is the best framework for building stateful, agentic applications!",
    metadata={"source": "tweet"},
)

document_9 = Document(
    page_content="The stock market is down 500 points today due to fears of a recession.",
    metadata={"source": "news"},
)

document_10 = Document(
    page_content="I have a bad feeling I am going to get deleted :(",
    metadata={"source": "tweet"},
)

documents = [
    document_1,
    document_2,
    document_3,
    document_4,
    document_5,
    document_6,
    document_7,
    document_8,
    document_9,
    document_10,
]
uuids = [str(uuid4()) for _ in range(len(documents))]
API Reference:Document

Similarity Search with Euclidean Distance (Default)​

# 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 = "langchain_example"
connection = create_config()

db = Kinetica(
    connection,
    embeddings,
    collection_name=COLLECTION_NAME,
)

db.add_documents(documents=documents, ids=uuids)
['05e5a484-0273-49d1-90eb-1276baca31de',
 'd98b808f-dc0b-4328-bdbf-88f6b2ab6040',
 'ba0968d4-e344-4285-ae0f-f5199b56f9d6',
 'a25393b8-6539-45b5-993e-ea16d01941ec',
 '804a37e3-1278-4b60-8b02-36b159ee8c1a',
 '9688b594-3dc6-41d2-a937-babf8ff24c2f',
 '40f7b8fe-67c7-489a-a5a5-7d3965e33bba',
 'b4fc1376-c113-41e9-8f16-f9320517bedd',
 '4d94d089-fdde-442b-84ab-36d9fe0670c8',
 '66fdb79d-49ce-4b06-901a-fda6271baf2a']
# query = "What did the president say about Ketanji Brown Jackson"
# docs_with_score = db.similarity_search_with_score(query)
print()
print("Similarity Search")
results = db.similarity_search(
    "LangChain provides abstractions to make working with LLMs easy",
    k=2,
    filter={"source": "tweet"},
)
for res in results:
    print(f"* {res.page_content} [{res.metadata}]")
    
print()
print("Similarity search with score")
results = db.similarity_search_with_score(
    "Will it be hot tomorrow?", k=1, filter={"source": "news"}
)
for res, score in results:
    print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")

Similarity Search
* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]

Similarity search with score
* [SIM=0.945397] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]

Working with vectorstore​

Above, we created a vectorstore from scratch. However, often times we want to work with an existing vectorstore. In order to do that, we can initialize it directly.

store = Kinetica(
    collection_name=COLLECTION_NAME,
    config=connection,
    embedding_function=embeddings,
)

Add documents​

We can add documents to the existing vectorstore.

store.add_documents([Document(page_content="foo")])
['68c4c679-c4d9-4f2d-bf01-f6c4f2181503']
docs_with_score = db.similarity_search_with_score("foo")
docs_with_score[0]
(Document(metadata={}, page_content='foo'), 0.0015394920483231544)
docs_with_score[1]
(Document(metadata={'source': 'tweet'}, page_content='Building an exciting new project with LangChain - come check it out!'),
 1.2609431743621826)

Overriding a vectorstore​

If you have an existing collection, you override it by doing from_documents and setting pre_delete_collection = True

db = Kinetica.from_documents(
    documents=documents,
    embedding=embeddings,
    collection_name=COLLECTION_NAME,
    config=connection,
    pre_delete_collection=True,
)
docs_with_score = db.similarity_search_with_score("foo")
docs_with_score[0]
(Document(metadata={'source': 'tweet'}, page_content='Building an exciting new project with LangChain - come check it out!'),
 1.260920763015747)

Using a VectorStore as a Retriever​

retriever = store.as_retriever()
print(retriever)
tags=['Kinetica', 'OpenAIEmbeddings'] vectorstore=<langchain_community.vectorstores.kinetica.Kinetica object at 0x7a48142b2230> search_kwargs={}

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