Deprecating sql_database access for creating UC functions for agent t…

…ools
langchain-ai · Feb 12, 2025 · 8120b8f · 8120b8f
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diff --git 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
diff --git 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).
 
 Open Models
 -----------

diff --git a/libs/community/langchain_community/utilities/sql_database.py b/libs/community/langchain_community/utilities/sql_database.py
@@ -139,6 +139,14 @@ def from_uri(
         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.databricks.com/en/generative-ai/agent-framework/"
+        "structured-retrieval-tools.html#table-query-tool instead",
+        removal="1.0",
+    )
     def from_databricks(
         cls,
         catalog: str,