openGauss VectorStore

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​

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

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:

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

Configuration​

Connection Settings​

Supported Combinations​

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

API reference​

For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/api_reference/en/latest/vectorstores/opengauss.OpenGuass.html

Related​

• Vector store conceptual guide
• Vector store how-to guides