OpenGauss VectorStore 集成

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.

设置

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

凭证

Using your openGauss Credentials

初始化

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)

管理向量存储

向向量存储添加项目

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)

从向量存储删除项目

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

查询向量存储

一旦创建了向量存储并添加了相关文档，您很可能希望在链或智能体运行期间对其进行查询。

直接查询

可以按以下方式执行简单的相似度搜索：

• 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}]")

如果您想执行相似度搜索并获取对应分数，可以运行：

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}]")

转换为检索器进行查询

您还可以将向量存储转换为检索器，以便在链中更方便地使用。

• 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")

用于检索增强生成

有关如何将此向量存储用于检索增强生成 (RAG) 的指南，请参阅以下部分：

• Tutorials
• How-to: Question and answer with RAG
• Retrieval conceptual docs

配置

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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