TiDB Cloud, is a comprehensive Database-as-a-Service (DBaaS) solution, that provides dedicated and serverless options. TiDB Serverless is now integrating a built-in vector search into the MySQL landscape. With this enhancement, you can seamlessly develop AI applications using TiDB Serverless without the need for a new database or additional technical stacks. Be among the first to experience it by joining the waitlist for the private beta at https://tidb.cloud/ai.
This notebook introduces how to use TiDBLoader to load data from TiDB in langchain.

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Prerequisites

Before using the TiDBLoader, we will install the following dependencies: 
%pip install --upgrade --quiet langchain
Then, we will configure the connection to a TiDB. In this notebook, we will follow the standard connection method provided by TiDB Cloud to establish a secure and efficient database connection. 
import getpass

# copy from tidb cloud console，replace it with your own
tidb_connection_string_template = "mysql+pymysql://<USER>:<PASSWORD>@<HOST>:4000/<DB>?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true"
tidb_password = getpass.getpass("Input your TiDB password:")
tidb_connection_string = tidb_connection_string_template.replace(
    "<PASSWORD>", tidb_password
)

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Load Data from TiDB

Here’s a breakdown of some key arguments you can use to customize the behavior of the TiDBLoader:
  • query (str): This is the SQL query to be executed against the TiDB database. The query should select the data you want to load into your Document objects. For instance, you might use a query like "SELECT * FROM my_table" to fetch all data from my_table.
  • page_content_columns (Optional[List[str]]): Specifies the list of column names whose values should be included in the page_content of each Document object. If set to None (the default), all columns returned by the query are included in page_content. This allows you to tailor the content of each document based on specific columns of your data.
  • metadata_columns (Optional[List[str]]): Specifies the list of column names whose values should be included in the metadata of each Document object. By default, this list is empty, meaning no metadata will be included unless explicitly specified. This is useful for including additional information about each document that doesn’t form part of the main content but is still valuable for processing or analysis. 
from sqlalchemy import Column, Integer, MetaData, String, Table, create_engine

# Connect to the database
engine = create_engine(tidb_connection_string)
metadata = MetaData()
table_name = "test_tidb_loader"

# Create a table
test_table = Table(
    table_name,
    metadata,
    Column("id", Integer, primary_key=True),
    Column("name", String(255)),
    Column("description", String(255)),
)
metadata.create_all(engine)


with engine.connect() as connection:
    transaction = connection.begin()
    try:
        connection.execute(
            test_table.insert(),
            [
                {"name": "Item 1", "description": "Description of Item 1"},
                {"name": "Item 2", "description": "Description of Item 2"},
                {"name": "Item 3", "description": "Description of Item 3"},
            ],
        )
        transaction.commit()
    except:
        transaction.rollback()
        raise

from langchain_community.document_loaders import TiDBLoader

# Setup TiDBLoader to retrieve data
loader = TiDBLoader(
    connection_string=tidb_connection_string,
    query=f"SELECT * FROM {table_name};",
    page_content_columns=["name", "description"],
    metadata_columns=["id"],
)

# Load data
documents = loader.load()

# Display the loaded documents
for doc in documents:
    print("-" * 30)
    print(f"content: {doc.page_content}\nmetada: {doc.metadata}")

------------------------------
content: name: Item 1
description: Description of Item 1
metada: {'id': 1}
------------------------------
content: name: Item 2
description: Description of Item 2
metada: {'id': 2}
------------------------------
content: name: Item 3
description: Description of Item 3
metada: {'id': 3}

test_table.drop(bind=engine)