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

OceanbaseVectorStore | 🦜️🔗 LangChain

This notebook covers how to get started with the Oceanbase vector store.

To access Oceanbase vector stores you'll need to deploy a standalone OceanBase server: %docker run --name=ob433 -e MODE=mini -e OB_SERVER_IP=127.0.0.1 -p 2881:2881 -d quay.io/oceanbase/oceanbase-ce:4.3.3.1-101000012024102216 And install the langchain-oceanbase integration package. %pip install -qU "langchain-oceanbase" Check the connection to OceanBase and set the memory usage ratio for vector data:

from pyobvector import ObVecClient

tmp_client = ObVecClient()
tmp_client.perform_raw_text_sql("ALTER SYSTEM ob_vector_memory_limit_percentage = 30")

<sqlalchemy.engine.cursor.CursorResult at 0x12696f2a0>

Configure the API key of the embedded model. Here we use DashScopeEmbeddings as an example. When deploying Oceanbase with a Docker image as described above, simply follow the script below to set the host, port, user, password, and database name. For other deployment methods, set these parameters according to the actual situation. %pip install dashscope

import os

from langchain_community.embeddings import DashScopeEmbeddings
from langchain_oceanbase.vectorstores import OceanbaseVectorStore

DASHSCOPE_API = os.environ.get("DASHSCOPE_API_KEY", "")
connection_args = {
    "host": "127.0.0.1",
    "port": "2881",
    "user": "root@test",
    "password": "",
    "db_name": "test",
}

embeddings = DashScopeEmbeddings(
    model="text-embedding-v1", dashscope_api_key=DASHSCOPE_API
)

vector_store = OceanbaseVectorStore(
    embedding_function=embeddings,
    table_name="langchain_vector",
    connection_args=connection_args,
    vidx_metric_type="l2",
    drop_old=True,
)

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

from langchain_core.documents import Document

document_1 = Document(page_content="foo", metadata={"source": "https://foo.com"})
document_2 = Document(page_content="bar", metadata={"source": "https://bar.com"})
document_3 = Document(page_content="baz", metadata={"source": "https://baz.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(documents=[updated_document], ids=["1"])

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:

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}]")

* bar [{'source': 'https://bar.com'}]

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}]")

* [SIM=133.452299] bar [{'source': 'https://bar.com'}]

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

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

[Document(metadata={'source': 'https://bar.com'}, page_content='bar')]

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

For detailed documentation of all OceanbaseVectorStore features and configurations head to the API reference: https://python.langchain.com/docs/integrations/vectorstores/oceanbase

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

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