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Tablestore is a fully managed NoSQL cloud database service. Tablestore enables storage of a massive amount of structured and semi-structured data. This notebook shows how to use functionality related to the Tablestore vector database. To use Tablestore, you must create an instance. Here are the creating instance instructions.

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Setup

pip install -qU  langchain-community tablestore

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Initialization

import getpass
import os

os.environ["end_point"] = getpass.getpass("Tablestore end_point:")
os.environ["instance_name"] = getpass.getpass("Tablestore instance_name:")
os.environ["access_key_id"] = getpass.getpass("Tablestore access_key_id:")
os.environ["access_key_secret"] = getpass.getpass("Tablestore access_key_secret:")
Create vector store. 
import tablestore
from langchain_community.embeddings import FakeEmbeddings
from langchain_community.vectorstores import TablestoreVectorStore
from langchain_core.documents import Document

test_embedding_dimension_size = 4
embeddings = FakeEmbeddings(size=test_embedding_dimension_size)

store = TablestoreVectorStore(
    embedding=embeddings,
    endpoint=os.getenv("end_point"),
    instance_name=os.getenv("instance_name"),
    access_key_id=os.getenv("access_key_id"),
    access_key_secret=os.getenv("access_key_secret"),
    vector_dimension=test_embedding_dimension_size,
    # metadata mapping is used to filter non-vector fields.
    metadata_mappings=[
        tablestore.FieldSchema(
            "type", tablestore.FieldType.KEYWORD, index=True, enable_sort_and_agg=True
        ),
        tablestore.FieldSchema(
            "time", tablestore.FieldType.LONG, index=True, enable_sort_and_agg=True
        ),
    ],
)

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Manage vector store

Create table and index. 
store.create_table_if_not_exist()
store.create_search_index_if_not_exist()
Add documents. 
store.add_documents(
    [
        Document(
            id="1", page_content="1 hello world", metadata={"type": "pc", "time": 2000}
        ),
        Document(
            id="2", page_content="abc world", metadata={"type": "pc", "time": 2009}
        ),
        Document(
            id="3", page_content="3 text world", metadata={"type": "sky", "time": 2010}
        ),
        Document(
            id="4", page_content="hi world", metadata={"type": "sky", "time": 2030}
        ),
        Document(
            id="5", page_content="hi world", metadata={"type": "sky", "time": 2030}
        ),
    ]
)

['1', '2', '3', '4', '5']
Delete document. 
store.delete(["3"])

True
Get documents.

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Query vector store

store.get_by_ids(["1", "3", "5"])

[Document(id='1', metadata={'embedding': '[1.3296732307905934, 0.0037521341868022385, 0.9821875819319514, 2.5644103644492393]', 'time': 2000, 'type': 'pc'}, page_content='1 hello world'),
 None,
 Document(id='5', metadata={'embedding': '[1.4558082172139821, -1.6441137122167426, -0.13113098640337423, -1.889685473174525]', 'time': 2030, 'type': 'sky'}, page_content='hi world')]
Similarity search. 
store.similarity_search(query="hello world", k=2)

[Document(id='1', metadata={'embedding': [1.3296732307905934, 0.0037521341868022385, 0.9821875819319514, 2.5644103644492393], 'time': 2000, 'type': 'pc'}, page_content='1 hello world'),
 Document(id='4', metadata={'embedding': [-0.3310144199800685, 0.29250046478723635, -0.0646862290377582, -0.23664360156781225], 'time': 2030, 'type': 'sky'}, page_content='hi world')]
Similarity search with filters. 
store.similarity_search(
    query="hello world",
    k=10,
    tablestore_filter_query=tablestore.BoolQuery(
        must_queries=[tablestore.TermQuery(field_name="type", column_value="sky")],
        should_queries=[tablestore.RangeQuery(field_name="time", range_from=2020)],
        must_not_queries=[tablestore.TermQuery(field_name="type", column_value="pc")],
    ),
)

[Document(id='5', metadata={'embedding': [1.4558082172139821, -1.6441137122167426, -0.13113098640337423, -1.889685473174525], 'time': 2030, 'type': 'sky'}, page_content='hi world'),
 Document(id='4', metadata={'embedding': [-0.3310144199800685, 0.29250046478723635, -0.0646862290377582, -0.23664360156781225], 'time': 2030, 'type': 'sky'}, page_content='hi world')]

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Usage for retrieval-augmented generation

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

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API reference

For detailed documentation of all TablestoreVectorStore features and configurations head to the API reference: python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.tablestore.TablestoreVectorStore.html
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