diff --git a/cookbook/README.md b/cookbook/README.md
index 2b90e07c791..6b18e508af7 100644
--- a/cookbook/README.md
+++ b/cookbook/README.md
@@ -21,7 +21,6 @@ Notebook | Description
[code-analysis-deeplake.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/code-analysis-deeplake.ipynb) | Analyze its own code base with the help of gpt and activeloop's deep lake.
[custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval.ipynb) | Build a custom agent that can interact with ai plugins by retrieving tools and creating natural language wrappers around openapi endpoints.
[custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb) | Build a custom agent with plugin retrieval functionality, utilizing ai plugins from the `plugnplai` directory.
-[databricks_sql_db.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/databricks_sql_db.ipynb) | Connect to databricks runtimes and databricks sql.
[deeplake_semantic_search_over_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/deeplake_semantic_search_over_chat.ipynb) | Perform semantic search and question-answering over a group chat using activeloop's deep lake with gpt4.
[elasticsearch_db_qa.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/elasticsearch_db_qa.ipynb) | Interact with elasticsearch analytics databases in natural language and build search queries via the elasticsearch dsl API.
[extraction_openai_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/extraction_openai_tools.ipynb) | Structured Data Extraction with OpenAI Tools
diff --git a/cookbook/databricks_sql_db.ipynb b/cookbook/databricks_sql_db.ipynb
deleted file mode 100644
index 78fba6b914e..00000000000
--- a/cookbook/databricks_sql_db.ipynb
+++ /dev/null
@@ -1,273 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "707d13a7",
- "metadata": {},
- "source": [
- "# Databricks\n",
- "\n",
- "This notebook covers how to connect to the [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain.\n",
- "It is broken into 3 parts: installation and setup, connecting to Databricks, and examples."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0076d072",
- "metadata": {},
- "source": [
- "## Installation and Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "739b489b",
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install databricks-sql-connector"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "73113163",
- "metadata": {},
- "source": [
- "## Connecting to Databricks\n",
- "\n",
- "You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the `SQLDatabase.from_databricks()` method.\n",
- "\n",
- "### Syntax\n",
- "```python\n",
- "SQLDatabase.from_databricks(\n",
- " catalog: str,\n",
- " schema: str,\n",
- " host: Optional[str] = None,\n",
- " api_token: Optional[str] = None,\n",
- " warehouse_id: Optional[str] = None,\n",
- " cluster_id: Optional[str] = None,\n",
- " engine_args: Optional[dict] = None,\n",
- " **kwargs: Any)\n",
- "```\n",
- "### Required Parameters\n",
- "* `catalog`: The catalog name in the Databricks database.\n",
- "* `schema`: The schema name in the catalog.\n",
- "\n",
- "### Optional Parameters\n",
- "There following parameters are optional. When executing the method in a Databricks notebook, you don't need to provide them in most of the cases.\n",
- "* `host`: The Databricks workspace hostname, excluding 'https://' part. Defaults to 'DATABRICKS_HOST' environment variable or current workspace if in a Databricks notebook.\n",
- "* `api_token`: The Databricks personal access token for accessing the Databricks SQL warehouse or the cluster. Defaults to 'DATABRICKS_TOKEN' environment variable or a temporary one is generated if in a Databricks notebook.\n",
- "* `warehouse_id`: The warehouse ID in the Databricks SQL.\n",
- "* `cluster_id`: The cluster ID in the Databricks Runtime. If running in a Databricks notebook and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the cluster the notebook is attached to.\n",
- "* `engine_args`: The arguments to be used when connecting Databricks.\n",
- "* `**kwargs`: Additional keyword arguments for the `SQLDatabase.from_uri` method."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b11c7e48",
- "metadata": {},
- "source": [
- "## Examples"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "8102bca0",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Connecting to Databricks with SQLDatabase wrapper\n",
- "from langchain_community.utilities import SQLDatabase\n",
- "\n",
- "db = SQLDatabase.from_databricks(catalog=\"samples\", schema=\"nyctaxi\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "9dd36f58",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Creating a OpenAI Chat LLM wrapper\n",
- "from langchain_openai import ChatOpenAI\n",
- "\n",
- "llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5b5c5f1a",
- "metadata": {},
- "source": [
- "### SQL Chain example\n",
- "\n",
- "This example demonstrates the use of the [SQL Chain](https://python.langchain.com/en/latest/modules/chains/examples/sqlite.html) for answering a question over a Databricks database."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "36f2270b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from langchain_community.utilities import SQLDatabaseChain\n",
- "\n",
- "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "4e2b5f25",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
- "What is the average duration of taxi rides that start between midnight and 6am?\n",
- "SQLQuery:\u001b[32;1m\u001b[1;3mSELECT AVG(UNIX_TIMESTAMP(tpep_dropoff_datetime) - UNIX_TIMESTAMP(tpep_pickup_datetime)) as avg_duration\n",
- "FROM trips\n",
- "WHERE HOUR(tpep_pickup_datetime) >= 0 AND HOUR(tpep_pickup_datetime) < 6\u001b[0m\n",
- "SQLResult: \u001b[33;1m\u001b[1;3m[(987.8122786304605,)]\u001b[0m\n",
- "Answer:\u001b[32;1m\u001b[1;3mThe average duration of taxi rides that start between midnight and 6am is 987.81 seconds.\u001b[0m\n",
- "\u001b[1m> Finished chain.\u001b[0m\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'The average duration of taxi rides that start between midnight and 6am is 987.81 seconds.'"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "db_chain.run(\n",
- " \"What is the average duration of taxi rides that start between midnight and 6am?\"\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e496d5e5",
- "metadata": {},
- "source": [
- "### SQL Database Agent example\n",
- "\n",
- "This example demonstrates the use of the [SQL Database Agent](/docs/integrations/tools/sql_database) for answering questions over a Databricks database."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "9918e86a",
- "metadata": {},
- "outputs": [],
- "source": [
- "from langchain.agents import create_sql_agent\n",
- "from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
- "\n",
- "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
- "agent = create_sql_agent(llm=llm, toolkit=toolkit, verbose=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "c484a76e",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
- "\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
- "Action Input: \u001b[0m\n",
- "Observation: \u001b[38;5;200m\u001b[1;3mtrips\u001b[0m\n",
- "Thought:\u001b[32;1m\u001b[1;3mI should check the schema of the trips table to see if it has the necessary columns for trip distance and duration.\n",
- "Action: schema_sql_db\n",
- "Action Input: trips\u001b[0m\n",
- "Observation: \u001b[33;1m\u001b[1;3m\n",
- "CREATE TABLE trips (\n",
- "\ttpep_pickup_datetime TIMESTAMP, \n",
- "\ttpep_dropoff_datetime TIMESTAMP, \n",
- "\ttrip_distance FLOAT, \n",
- "\tfare_amount FLOAT, \n",
- "\tpickup_zip INT, \n",
- "\tdropoff_zip INT\n",
- ") USING DELTA\n",
- "\n",
- "/*\n",
- "3 rows from trips table:\n",
- "tpep_pickup_datetime\ttpep_dropoff_datetime\ttrip_distance\tfare_amount\tpickup_zip\tdropoff_zip\n",
- "2016-02-14 16:52:13+00:00\t2016-02-14 17:16:04+00:00\t4.94\t19.0\t10282\t10171\n",
- "2016-02-04 18:44:19+00:00\t2016-02-04 18:46:00+00:00\t0.28\t3.5\t10110\t10110\n",
- "2016-02-17 17:13:57+00:00\t2016-02-17 17:17:55+00:00\t0.7\t5.0\t10103\t10023\n",
- "*/\u001b[0m\n",
- "Thought:\u001b[32;1m\u001b[1;3mThe trips table has the necessary columns for trip distance and duration. I will write a query to find the longest trip distance and its duration.\n",
- "Action: query_checker_sql_db\n",
- "Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
- "Observation: \u001b[31;1m\u001b[1;3mSELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
- "Thought:\u001b[32;1m\u001b[1;3mThe query is correct. I will now execute it to find the longest trip distance and its duration.\n",
- "Action: query_sql_db\n",
- "Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
- "Observation: \u001b[36;1m\u001b[1;3m[(30.6, '0 00:43:31.000000000')]\u001b[0m\n",
- "Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
- "Final Answer: The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.\u001b[0m\n",
- "\n",
- "\u001b[1m> Finished chain.\u001b[0m\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.'"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "agent.run(\"What is the longest trip distance and how long did it take?\")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/docs/integrations/providers/databricks.md b/docs/docs/integrations/providers/databricks.md
index a45e8e3e03e..2cf4961971a 100644
--- a/docs/docs/integrations/providers/databricks.md
+++ b/docs/docs/integrations/providers/databricks.md
@@ -103,14 +103,7 @@ See [MLflow LangChain Integration](/docs/integrations/providers/mlflow_tracking)
SQLDatabase
-----------
-You can connect to Databricks SQL using the SQLDatabase wrapper of LangChain.
-```
-from langchain.sql_database import SQLDatabase
-
-db = SQLDatabase.from_databricks(catalog="samples", schema="nyctaxi")
-```
-
-See [Databricks SQL Agent](https://docs.databricks.com/en/large-language-models/langchain.html#databricks-sql-agent) for how to connect Databricks SQL with your LangChain Agent as a powerful querying tool.
+To connect to Databricks SQL or query structured data, see the [Databricks structured retriever tool documentation](https://docs.databricks.com/en/generative-ai/agent-framework/structured-retrieval-tools.html#table-query-tool) and to create an agent using the above created SQL UDF see [Databricks UC Integration](https://docs.unitycatalog.io/ai/integrations/langchain/).
Open Models
-----------
diff --git a/libs/community/langchain_community/utilities/sql_database.py b/libs/community/langchain_community/utilities/sql_database.py
index d6ecc4ca008..2b6b84ca4a1 100644
--- a/libs/community/langchain_community/utilities/sql_database.py
+++ b/libs/community/langchain_community/utilities/sql_database.py
@@ -139,6 +139,14 @@ class SQLDatabase:
return cls(create_engine(database_uri, **_engine_args), **kwargs)
@classmethod
+ @deprecated(
+ "0.3.18",
+ message="For performing structured retrieval using Databricks SQL, "
+ "see the latest best practices and recommended APIs at "
+ "https://docs.unitycatalog.io/ai/integrations/langchain/ " # noqa: E501
+ "instead",
+ removal="1.0",
+ )
def from_databricks(
cls,
catalog: str,