> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # OceanbaseVectorStore integration > Integrate with the OceanbaseVectorStore using LangChain Python. This notebook covers how to get started with the Oceanbase vector store. ## Setup 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: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from pyobvector import ObVecClient tmp_client = ObVecClient() tmp_client.perform_raw_text_sql("ALTER SYSTEM ob_vector_memory_limit_percentage = 30") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} ``` ## Initialization 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 pip install dashscope The `langchain-community` package is no longer maintained. Examples that import from `langchain_community` may be outdated or broken. Use with caution. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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, ) ``` ## Manage vector store ### Add items to vector store * TODO: Edit and then run code cell to generate output ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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"]) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} ['1', '2', '3'] ``` ### Update items in vector store ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} updated_document = Document( page_content="qux", metadata={"source": "https://another-example.com"} ) vector_store.add_documents(documents=[updated_document], ids=["1"]) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} ['1'] ``` ### Delete items from vector store ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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}]") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} * bar [{'source': 'https://bar.com'}] ``` If you want to execute a similarity search and receive the corresponding scores you can run: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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}]") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} * [SIM=133.452299] bar [{'source': 'https://bar.com'}] ``` ### Query by turning into retriever You can also transform the vector store into a retriever for easier usage in your chains. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} retriever = vector_store.as_retriever(search_kwargs={"k": 1}) retriever.invoke("thud") ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [Document(metadata={'source': 'https://bar.com'}, page_content='bar')] ``` ## 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](/oss/python/langchain/retrieval) * [Build a RAG app with LangChain](/oss/python/langchain/rag) * [Agentic RAG](/oss/python/langgraph/agentic-rag) *** ## API reference For detailed documentation of all `OceanbaseVectorStore` features and configurations head to the API reference: [python.langchain.com/docs/integrations/vectorstores/oceanbase](https://python.langchain.com/docs/integrations/vectorstores/oceanbase) ***
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