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119 lines
3.0 KiB
Markdown
119 lines
3.0 KiB
Markdown
---
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title: "SQLAlchemy"
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id: integrations-sqlalchemy
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description: "SQLAlchemy integration for Haystack"
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slug: "/integrations-sqlalchemy"
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---
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## haystack_integrations.components.retrievers.sqlalchemy.sqlalchemy_table_retriever
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### SQLAlchemyTableRetriever
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Connects to any SQLAlchemy-supported database and executes a SQL query.
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Returns results as a Pandas DataFrame and an optional Markdown-formatted table string.
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Supports any database backend that SQLAlchemy supports, including PostgreSQL, MySQL,
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SQLite, and MSSQL.
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### Usage example:
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```python
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from haystack_integrations.components.retrievers.sqlalchemy import SQLAlchemyTableRetriever
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retriever = SQLAlchemyTableRetriever(drivername="sqlite", database=":memory:")
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retriever.warm_up()
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result = retriever.run(query="SELECT 1 AS value")
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print(result["dataframe"])
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print(result["table"])
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```
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#### __init__
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```python
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__init__(
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drivername: str,
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username: str | None = None,
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password: Secret | None = None,
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host: str | None = None,
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port: int | None = None,
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database: str | None = None,
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init_script: list[str] | None = None,
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) -> None
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```
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Initialize SQLAlchemyTableRetriever.
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**Parameters:**
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- **drivername** (<code>str</code>) – The SQLAlchemy driver name (e.g., `"sqlite"`,
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`"postgresql+psycopg2"`).
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- **username** (<code>str | None</code>) – Database username.
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- **password** (<code>Secret | None</code>) – Database password as a Haystack `Secret`.
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- **host** (<code>str | None</code>) – Database host.
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- **port** (<code>int | None</code>) – Database port.
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- **database** (<code>str | None</code>) – Database name or path (e.g., `":memory:"` for SQLite in-memory).
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- **init_script** (<code>list\[str\] | None</code>) – Optional list of SQL statements executed once on `warm_up()`
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(e.g., to create tables or insert seed data). Each statement should be a
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separate string in the list.
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#### warm_up
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```python
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warm_up() -> None
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```
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Initialize the database engine and execute `init_script` if provided.
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Called automatically by `run()` on first invocation if not already warmed up.
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#### to_dict
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```python
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to_dict() -> dict[str, Any]
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```
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Serialize the component to a dictionary.
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**Returns:**
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- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
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#### from_dict
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```python
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from_dict(data: dict[str, Any]) -> SQLAlchemyTableRetriever
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```
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Deserialize the component from a dictionary.
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**Parameters:**
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- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
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**Returns:**
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- <code>SQLAlchemyTableRetriever</code> – Deserialized component.
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#### run
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```python
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run(query: str) -> dict[str, Any]
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```
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Execute a SQL query and return the results.
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**Parameters:**
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- **query** (<code>str</code>) – The SQL query to execute.
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**Returns:**
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- <code>dict\[str, Any\]</code> – A dictionary with:
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- `dataframe`: A Pandas DataFrame with the query results.
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- `table`: A Markdown-formatted string of the results.
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- `error`: An error message if the query failed, otherwise an empty string.
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