docs: mirror SQLAlchemyTableRetriever docs to version 3.0

Author: Aarkin7

docs-website/versioned_docs/version-3.0/pipeline-components/retrievers.mdx

@@ -198,6 +198,7 @@ For details on how to initialize and use a Retriever in a pipeline, see the docu
 | [QdrantHybridRetriever](retrievers/qdranthybridretriever.mdx)                     | A Retriever based both on dense and sparse embeddings, compatible with the Qdrant Document Store.                               |
 | [SentenceWindowRetriever](retrievers/sentencewindowretriever.mdx)                   | Retrieves neighboring sentences around relevant sentences to get the full context.                                              |
 | [SnowflakeTableRetriever](retrievers/snowflaketableretriever.mdx)                   | Connects to a Snowflake database to execute an SQL query.                                                                       |
+| [SQLAlchemyTableRetriever](retrievers/sqlalchemytableretriever.mdx)                 | Connects to any SQLAlchemy-supported database and executes an SQL query.                                                        |
 | [SupabaseGroongaBM25Retriever](retrievers/supabasegroongabm25retriever.mdx)         | A full-text Retriever that fetches documents from the SupabaseGroongaDocumentStore using PGroonga search.                       |
 | [SupabasePgvectorEmbeddingRetriever](retrievers/supabasepgvectorembeddingretriever.mdx) | An embedding-based Retriever compatible with the SupabasePgvectorDocumentStore.                                             |
 | [SupabasePgvectorKeywordRetriever](retrievers/supabasepgvectorkeywordretriever.mdx) | A keyword-based Retriever that fetches documents matching a query from the SupabasePgvectorDocumentStore.                       |

docs-website/versioned_docs/version-3.0/pipeline-components/retrievers/sqlalchemytableretriever.mdx

@@ -0,0 +1,134 @@
+---
+title: "SQLAlchemyTableRetriever"
+id: sqlalchemytableretriever
+slug: "/sqlalchemytableretriever"
+description: "Connects to any SQLAlchemy-supported database and executes an SQL query."
+---
+
+# SQLAlchemyTableRetriever
+
+Connects to any SQLAlchemy-supported database and executes an SQL query.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Most common position in a pipeline** | Before a [`PromptBuilder`](../builders/promptbuilder.mdx) |
+| **Mandatory init variables** | `drivername`: SQLAlchemy driver name, for example `sqlite`, `postgresql+psycopg2`, `mysql+pymysql`, or `mssql+pyodbc`. For real database backends you will also need `host`, `port`, `database`, `username`, and `password`. |
+| **Mandatory run variables** | `query`: An SQL query to execute |
+| **Output variables** | `dataframe`: The query result as a Pandas DataFrame  <br /> <br />`table`: The same result rendered as a Markdown table  <br /> <br />`error`: Error message if the query failed, empty string otherwise |
+| **API reference** | [SQLAlchemy](/reference/integrations-sqlalchemy) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/sqlalchemy |
+| **Package name** | `sqlalchemy-haystack` |
+
+</div>
+
+## Overview
+
+`SQLAlchemyTableRetriever` is a backend-agnostic table retriever: it speaks to anything SQLAlchemy speaks to — PostgreSQL, MySQL, SQLite, MSSQL, and the long tail of dialects covered by third-party drivers. Give it a SQL query and it hands you back the result as both a Pandas DataFrame (under `dataframe`) and a ready-to-render Markdown table (under `table`), which is convenient for piping into a prompt. Results are capped at 10,000 rows.
+
+If the query fails, the component does not raise — it returns an empty DataFrame and puts the SQLAlchemy error string in the `error` output. That makes it safe to drop into a pipeline without wrapping the whole thing in a try/except.
+
+### Connection parameters
+
+The init arguments map directly to SQLAlchemy's URL parts:
+
+- `drivername` — the only strictly required one. Pick the driver that matches your backend, e.g. `postgresql+psycopg2`, `mysql+pymysql`, `sqlite`, `mssql+pyodbc`.
+- `host`, `port`, `database`, `username` — standard connection bits. Pass whatever your backend needs.
+- `password` — a Haystack [Secret](../../concepts/secret-management.mdx). Resolve it from an environment variable with `Secret.from_env_var("MY_DB_PASSWORD")`, or inline with `Secret.from_token("...")` (not recommended for anything other than local tinkering).
+
+For SQLite, `drivername="sqlite"` with `database=":memory:"` is enough — no host/user/password needed.
+
+### `init_script`
+
+Pass `init_script` to run one or more SQL statements once, in a single transaction, the first time the component is warmed up. Typical uses:
+
+- Seeding an in-memory SQLite database for demos or tests.
+- Creating temporary views or session-level settings before queries run.
+
+Each entry in the list is a single statement.
+
+## Usage
+
+Install the `sqlalchemy-haystack` package, plus the driver for your database:
+
+```shell
+pip install sqlalchemy-haystack
+# For PostgreSQL, also install a driver:
+pip install psycopg2-binary
+```
+
+### On its own
+
+A self-contained example using an in-memory SQLite database seeded via `init_script`:
+
+```python
+from haystack_integrations.components.retrievers.sqlalchemy import SQLAlchemyTableRetriever
+
+retriever = SQLAlchemyTableRetriever(
+    drivername="sqlite",
+    database=":memory:",
+    init_script=[
+        "CREATE TABLE employees (name TEXT, salary INTEGER)",
+        "INSERT INTO employees VALUES ('Ada', 90000), ('Linus', 85000), ('Grace', 95000)",
+    ],
+)
+
+result = retriever.run(query="SELECT name, salary FROM employees ORDER BY salary DESC")
+print(result["dataframe"])
+print(result["table"])
+```
+
+Connecting to a real backend looks the same — swap the driver and pass connection details:
+
+```python
+from haystack.utils import Secret
+from haystack_integrations.components.retrievers.sqlalchemy import SQLAlchemyTableRetriever
+
+retriever = SQLAlchemyTableRetriever(
+    drivername="postgresql+psycopg2",
+    host="db.example.com",
+    port=5432,
+    database="analytics",
+    username="readonly",
+    password=Secret.from_env_var("ANALYTICS_DB_PASSWORD"),
+)
+```
+
+### In a pipeline
+
+Use the retriever's Markdown `table` output as context for an LLM — for example, asking an LLM to summarize a query result:
+
+```python
+from haystack import Pipeline
+from haystack.utils import Secret
+from haystack.components.builders import ChatPromptBuilder
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.components.retrievers.sqlalchemy import SQLAlchemyTableRetriever
+
+retriever = SQLAlchemyTableRetriever(
+    drivername="postgresql+psycopg2",
+    host="db.example.com",
+    port=5432,
+    database="analytics",
+    username="readonly",
+    password=Secret.from_env_var("ANALYTICS_DB_PASSWORD"),
+)
+
+pipeline = Pipeline()
+pipeline.add_component(
+    "builder",
+    ChatPromptBuilder(
+        template=[ChatMessage.from_user("Describe this table: {{ table }}")],
+        required_variables="*",
+    ),
+)
+pipeline.add_component("db", retriever)
+pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o"))
+
+pipeline.connect("db.table", "builder.table")
+pipeline.connect("builder.prompt", "llm.messages")
+
+pipeline.run(data={"query": "SELECT employee, salary FROM employees LIMIT 10"})
+```

docs-website/versioned_sidebars/version-3.0-sidebars.json

@@ -624,6 +624,7 @@
             "pipeline-components/retrievers/qdrantsparseembeddingretriever",
             "pipeline-components/retrievers/sentencewindowretriever",
             "pipeline-components/retrievers/snowflaketableretriever",
+            "pipeline-components/retrievers/sqlalchemytableretriever",
             "pipeline-components/retrievers/supabasegroongabm25retriever",
             "pipeline-components/retrievers/supabasepgvectorembeddingretriever",
             "pipeline-components/retrievers/supabasepgvectorkeywordretriever",