> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1782332329-96d87c7.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# ZeusDB integration
> Integrate with the ZeusDB vector store using LangChain Python.
> [ZeusDB](https://www.zeusdb.com) is a vector database written in Rust. It supports product quantization, persistent storage, and logging for operational use.
The following sections show how to use ZeusDB with LangChain.
***
## Setup
Install the ZeusDB LangChain integration package from PyPI:
```python theme={null}
pip install -qU langchain-zeusdb
```
Setup in Jupyter Notebooks
```python theme={null}
pip install -qU langchain-zeusdb
```
***
## Getting started
This example uses OpenAIEmbeddings, which requires an OpenAI API key: [Get your OpenAI API key here](https://platform.openai.com/api-keys)
If you prefer, you can also use this package with any other embedding provider (Hugging Face, Cohere, custom functions, etc.).
Install the LangChain OpenAI integration package from PyPI:
```python theme={null}
pip install -qU langchain-openai
# Use this command if inside Jupyter Notebooks
#pip install -qU langchain-openai
```
#### Please choose an option below for your OpenAI key integration
*Option 1: 🔑 Enter your API key each time*
Use getpass in Jupyter to securely input your key for the current session:
```python theme={null}
import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
```
*Option 2: 🗂️ Use a .env file*
Keep your key in a local .env file and load it automatically with python-dotenv
```python theme={null}
from dotenv import load_dotenv
load_dotenv() # reads .env and sets OPENAI_API_KEY
```
🎉 Nicely done! You are good to go.
***
## Initialization
```python theme={null}
# Import required Packages and Classes
from langchain_zeusdb import ZeusDBVectorStore
from langchain_openai import OpenAIEmbeddings
from zeusdb import VectorDatabase
```
```python theme={null}
# Initialize embeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
# Create ZeusDB index
vdb = VectorDatabase()
index = vdb.create(index_type="hnsw", dim=1536, space="cosine")
# Create vector store
vector_store = ZeusDBVectorStore(zeusdb_index=index, embedding=embeddings)
```
***
## Manage vector store
### 2.1 add items to vector store
```python theme={null}
from langchain_core.documents import Document
document_1 = Document(
page_content="ZeusDB is a high-performance vector database",
metadata={"source": "https://docs.zeusdb.com"},
)
document_2 = Document(
page_content="Product Quantization reduces memory usage significantly",
metadata={"source": "https://docs.zeusdb.com"},
)
document_3 = Document(
page_content="ZeusDB integrates seamlessly with LangChain",
metadata={"source": "https://docs.zeusdb.com"},
)
documents = [document_1, document_2, document_3]
vector_store.add_documents(documents=documents, ids=["1", "2", "3"])
```
### 2.2 update items in vector store
```python theme={null}
updated_document = Document(
page_content="ZeusDB now supports advanced Product Quantization with 4x-256x compression",
metadata={"source": "https://docs.zeusdb.com", "updated": True},
)
vector_store.add_documents([updated_document], ids=["1"])
```
### 2.3 delete items from vector store
```python theme={null}
vector_store.delete(ids=["3"])
```
***
## Query vector store
### 3.1 query directly
Performing a simple similarity search:
```python theme={null}
results = vector_store.similarity_search(query="high performance database", k=2)
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
```
If you want to execute a similarity search and receive the corresponding scores:
```python theme={null}
results = vector_store.similarity_search_with_score(query="memory optimization", k=2)
for doc, score in results:
print(f"* [SIM={score:.3f}] {doc.page_content} [{doc.metadata}]")
```
### 3.2 query by turning into retriever
You can also transform the vector store into a retriever for easier usage in your chains:
```python theme={null}
retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 2})
retriever.invoke("vector database features")
```
***
## ZeusDB-Specific features
### 4.1 Memory-Efficient setup with product quantization
For large datasets, use Product Quantization to reduce memory usage:
```python theme={null}
# Create memory-optimized vector store
quantization_config = {"type": "pq", "subvectors": 8, "bits": 8, "training_size": 10000}
vdb_quantized = VectorDatabase()
quantized_index = vdb_quantized.create(
index_type="hnsw", dim=1536, quantization_config=quantization_config
)
quantized_vector_store = ZeusDBVectorStore(
zeusdb_index=quantized_index, embedding=embeddings
)
print(f"Created quantized store: {quantized_index.info()}")
```
### 4.2 persistence
Save and load your vector store to disk:
How to Save your vector store
```python theme={null}
# Save the vector store
vector_store.save_index("my_zeusdb_index.zdb")
```
How to Load your vector store
```python theme={null}
# Load the vector store
loaded_store = ZeusDBVectorStore.load_index(
path="my_zeusdb_index.zdb", embedding=embeddings
)
print(f"Loaded store with {loaded_store.get_vector_count()} vectors")
```
***
## Usage for retrieval-augmented generation
For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:
* [Retrieval docs](/oss/python/langchain/retrieval)
* [Build a RAG app with LangChain](/oss/python/langchain/rag)
* [Agentic RAG](/oss/python/langgraph/agentic-rag)
***
## API reference
For detailed documentation of all `ZeusDBVectorStore` features and configurations head to [ZeusDB Docs](https://docs.zeusdb.com/en/latest/vector_database/integrations/langchain.html).
***
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[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/vectorstores/zeusdb.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).