> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Kinetica vectorstore integration

> Integrate with the Kinetica VectorStore API vector store using LangChain Python.

[Kinetica](https://www.kinetica.com/) 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](https://www.kinetica.com/developer-edition/).

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# Pip install necessary package
pip install -qU langchain-kinetica
```

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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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(model="text-embedding-3-large")
```

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.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# Kinetica needs the connection to the database.
# Set these environment variables:
from gpudb import GPUdb

from langchain_kinetica import KineticaSettings, KineticaVectorstore

kdbc = GPUdb.get_connection()
k_config = KineticaSettings(kdbc=kdbc)
k_config
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
2026-02-02 21:28:34.745 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 0x1170ae270>, database='langchain', table='langchain_kinetica_embeddings', metric='l2')
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from uuid import uuid4

from langchain_core.documents import Document

document_1 = Document(
    page_content="I had chocolate 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))]
uuids
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
['ddad79f1-141d-44f6-8f50-72e5c0f1ee16',
    '10819fa9-794b-4fde-934a-aabd453781c8',
    '3ce641d5-8c6b-4dcb-90fe-a3c19b3132ff',
    '9db5c865-389f-481c-aea2-440b8437e22c',
    '74dd4d80-a371-4c41-8254-7981d375274d',
    '74d7571e-f8c5-4001-9979-e99996ec2ce5',
    '3a3eb718-f2b9-4186-8c2e-34a1e18ebb3b',
    '59a88b08-f8c6-4cf5-b485-9485a4a8ffd0',
    'd84ad1c8-ec01-4d13-b61a-ef4b08abb485',
    'c9ab8f4f-e566-465f-a85d-ee05780714ea']
```

## Similarity search with euclidean distance (Default)

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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
COLLECTION_NAME = "langchain_example"

vectorstore = KineticaVectorstore(
    config=k_config,
    embedding_function=embeddings,
    collection_name=COLLECTION_NAME,
    pre_delete_collection=True,
)

vectorstore.add_documents(documents=documents, ids=uuids)

print()
print("Similarity Search")
results = vectorstore.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 = vectorstore.similarity_search_with_score(
    "Will it be hot tomorrow?", k=1, emb_filter={"source": "news"}
)
for res, score in results:
    print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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=0.945353] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]
```

## Working with vectorstore

### Adding documents

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.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
vectorstore = KineticaVectorstore(
    config=k_config,
    embedding_function=embeddings,
    collection_name=COLLECTION_NAME,
)

# We can add documents to the existing vectorstore.
vectorstore.add_documents([Document(page_content="foo")])

docs_with_score = vectorstore.similarity_search_with_score("foo")

print(f"First result: {docs_with_score[0]}")
print(f"Second result: {docs_with_score[1]}")
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
First result: (Document(metadata={}, page_content='foo'), 0.0014664357295259833)
Second result: (Document(metadata={'source': 'tweet'}, page_content='Building an exciting new project with LangChain - come check it out!'), 1.260981559753418)
```

### Overriding a vectorstore

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

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
vectorstore = KineticaVectorstore.from_documents(
    documents=documents,
    embedding=embeddings,
    collection_name=COLLECTION_NAME,
    config=k_config,
    pre_delete_collection=True,
)

docs_with_score = vectorstore.similarity_search_with_score("foo")
docs_with_score[0]
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
(Document(metadata={'source': 'tweet'}, page_content='Building an exciting new project with LangChain - come check it out!'),
    1.2609236240386963)
```

### Using a VectorStore as a retriever

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_core.vectorstores.base import VectorStoreRetriever

retriever: VectorStoreRetriever = vectorstore.as_retriever()
retriever
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
VectorStoreRetriever(tags=['KineticaVectorstore', 'OpenAIEmbeddings'], vectorstore=<langchain_kinetica.vectorstores.KineticaVectorstore object at 0x1139cfaa0>, search_kwargs={})
```

***

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