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Showing content from https://python.langchain.com/docs/integrations/vectorstores/opengauss below:

openGauss VectorStore | 🦜️🔗 LangChain

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.

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

pip install langchain-opengauss

System Requirements:

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

Using your openGauss Credentials

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


config = OpenGaussSettings(
    table_name="test_langchain",
    embedding_dimension=384,
    index_type="HNSW",
    distance_strategy="COSINE",
)
vector_store = OpenGauss(embedding=embeddings, config=config)

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"])

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


vector_store.add_documents(document_id="1", document=updated_document)

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

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.

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}]")

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")

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

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,  
)

OpenGaussSettings(min_connections=3, max_connections=20)

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

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

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

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