deepset-ai--haystack/docs-website/reference_versioned_docs/version-2.26/integrations-api/sqlalchemy.md

title, id, description, slug

title	id	description	slug
SQLAlchemy	integrations-sqlalchemy	SQLAlchemy integration for Haystack	/integrations-sqlalchemy

haystack_integrations.components.retrievers.sqlalchemy.sqlalchemy_table_retriever

SQLAlchemyTableRetriever

Connects to any SQLAlchemy-supported database and executes a SQL query.

Returns results as a Pandas DataFrame and an optional Markdown-formatted table string. Supports any database backend that SQLAlchemy supports, including PostgreSQL, MySQL, SQLite, and MSSQL.

Usage example:

from haystack_integrations.components.retrievers.sqlalchemy import SQLAlchemyTableRetriever

retriever = SQLAlchemyTableRetriever(drivername="sqlite", database=":memory:")
retriever.warm_up()
result = retriever.run(query="SELECT 1 AS value")
print(result["dataframe"])
print(result["table"])

init

__init__(
    drivername: str,
    username: str | None = None,
    password: Secret | None = None,
    host: str | None = None,
    port: int | None = None,
    database: str | None = None,
    init_script: list[str] | None = None,
) -> None

Initialize SQLAlchemyTableRetriever.

Parameters:

• drivername (str) – The SQLAlchemy driver name (e.g., "sqlite", "postgresql+psycopg2").
• username (str | None) – Database username.
• password (Secret | None) – Database password as a Haystack Secret.
• host (str | None) – Database host.
• port (int | None) – Database port.
• database (str | None) – Database name or path (e.g., ":memory:" for SQLite in-memory).
• init_script (list[str] | None) – Optional list of SQL statements executed once on warm_up() (e.g., to create tables or insert seed data). Each statement should be a separate string in the list.

warm_up

warm_up() -> None

Initialize the database engine and execute init_script if provided.

Called automatically by run() on first invocation if not already warmed up.

to_dict

to_dict() -> dict[str, Any]

Serialize the component to a dictionary.

Returns:

• dict[str, Any] – Dictionary with serialized data.

from_dict

from_dict(data: dict[str, Any]) -> SQLAlchemyTableRetriever

Deserialize the component from a dictionary.

Parameters:

• data (dict[str, Any]) – Dictionary to deserialize from.

Returns:

• SQLAlchemyTableRetriever – Deserialized component.

run

run(query: str) -> dict[str, Any]

Execute a SQL query and return the results.

Parameters:

• query (str) – The SQL query to execute.

Returns:

• dict[str, Any] – A dictionary with:

• dataframe: A Pandas DataFrame with the query results.

• table: A Markdown-formatted string of the results.

• error: An error message if the query failed, otherwise an empty string.