OpenGauss VectorStore integration

Integrate with the OpenGauss VectorStore using LangChain Python.

This notebook covers how to get started with the openGauss VectorStore. openGauss is a high-performance relational database with native vector storage and retrieval capabilities. This integration enables ACID-compliant vector operations within LangChain applications, combining traditional SQL functionality with modern AI-driven similarity search. vector store.

Setup

Launch openGauss Container

docker run --name opengauss \
  -d \
  -e GS_PASSWORD='MyStrongPass@123' \
  -p 8888:5432 \
  opengauss/opengauss-server:latest

Install langchain-opengauss

pip install langchain-opengauss

System Requirements:

• openGauss ≥ 7.0.0
• Python ≥ 3.8
• psycopg2-binary

Credentials

Using your openGauss Credentials

Initialization

from langchain_opengauss import OpenGauss, OpenGaussSettings

# Configure with schema validation
config = OpenGaussSettings(
    table_name="test_langchain",
    embedding_dimension=384,
    index_type="HNSW",
    distance_strategy="COSINE",
)
vector_store = OpenGauss(embedding=embeddings, config=config)

Manage vector store

Add items to vector store

from langchain_core.documents import Document

document_1 = Document(page_content="foo", metadata={"source": "https://example.com"})

document_2 = Document(page_content="bar", metadata={"source": "https://example.com"})

document_3 = Document(page_content="baz", metadata={"source": "https://example.com"})

documents = [document_1, document_2, document_3]

vector_store.add_documents(documents=documents, ids=["1", "2", "3"])

Update items in vector store

updated_document = Document(
    page_content="qux", metadata={"source": "https://another-example.com"}
)

# If the id is already exist, will update the document
vector_store.add_documents(document_id="1", document=updated_document)

Delete items from vector store

vector_store.delete(ids=["3"])

Query vector store

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

Query directly

Performing a simple similarity search can be done as follows:

• TODO: Edit and then run code cell to generate output

results = vector_store.similarity_search(
    query="thud", k=1, filter={"source": "https://another-example.com"}
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

If you want to execute a similarity search and receive the corresponding scores you can run:

results = vector_store.similarity_search_with_score(
    query="thud", k=1, filter={"source": "https://example.com"}
)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

Query by turning into retriever

You can also transform the vector store into a retriever for easier usage in your chains.

• TODO: Edit and then run code cell to generate output

retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 1})
retriever.invoke("thud")

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
• Build a RAG app with LangChain
• Agentic RAG

Configuration

Connection settings

Parameter	Default	Description
host	localhost	Database server address
port	8888	Database connection port
user	gaussdb	Database username
password	-	Complex password string
database	postgres	Default database name
min_connections	1	Connection pool minimum size
max_connections	5	Connection pool maximum size
table_name	langchain_docs	Name of the table for storing vector data and metadata
index_type	IndexType.HNSW	Vector index algorithm type. Options: HNSW or IVFFLAT\nDefault is HNSW.
vector_type	VectorType.vector	Type of vector representation to use. Default is Vector.
distance_strategy	DistanceStrategy.COSINE	Vector similarity metric to use for retrieval. Options: euclidean (L2 distance), cosine (angular distance, ideal for text embeddings), manhattan (L1 distance for sparse data), negative_inner_product (dot product for normalized vectors).\n Default is cosine.
embedding_dimension	1536	Dimensionality of the vector embeddings.

Supported combinations

Vector Type	Dimensions	Index Types	Supported Distance Strategies
vector	≤2000	HNSW/IVFFLAT	COSINE/EUCLIDEAN/MANHATTAN/INNER_PROD

Performance optimization

Index tuning guidelines

HNSW Parameters:

• m: 16-100 (balance between recall and memory)
• ef_construction: 64-1000 (must be > 2*m)

IVFFLAT Recommendations:

import math

lists = min(
    int(math.sqrt(total_rows)) if total_rows > 1e6 else int(total_rows / 1000),
    2000,  # openGauss maximum
)

Connection pooling

OpenGaussSettings(min_connections=3, max_connections=20)

Limitations

• bit and sparsevec vector types currently in development
• Maximum vector dimensions: 2000 for vector type

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