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community: support Databricks Unity Catalog functions as LangChain tools (#22555)
This PR adds support for using Databricks Unity Catalog functions as LangChain tools, which runs inside a Databricks SQL warehouse. * An example notebook is provided.
This commit is contained in:
Xiangrui Meng
GitHub
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docs/docs/integrations/tools/databricks.ipynb
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168
docs/docs/integrations/tools/databricks.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Databricks Unity Catalog (UC)\n",
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"\n",
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"This notebook shows how to use UC functions as LangChain tools.\n",
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"\n",
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"See Databricks documentation ([AWS](https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-sql-function.html)|[Azure](https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-create-sql-function)|[GCP](https://docs.gcp.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-sql-function.html)) to learn how to create SQL or Python functions in UC. Do not skip function and parameter comments, which are critical for LLMs to call functions properly.\n",
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"\n",
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"In this example notebook, we create a simple Python function that executes arbitary code and use it as a LangChain tool:\n",
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"\n",
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"```sql\n",
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"CREATE FUNCTION main.tools.python_exec (\n",
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" code STRING COMMENT 'Python code to execute. Remember to print the final result to stdout.'\n",
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")\n",
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"RETURNS STRING\n",
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"LANGUAGE PYTHON\n",
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"COMMENT 'Executes Python code and returns its stdout.'\n",
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"AS $$\n",
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" import sys\n",
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" from io import StringIO\n",
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" stdout = StringIO()\n",
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" sys.stdout = stdout\n",
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" exec(code)\n",
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" return stdout.getvalue()\n",
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"$$\n",
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"```\n",
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"\n",
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"It runs in a secure and isolated environment within a Databricks SQL warehouse."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet databricks-sdk langchain-community langchain-openai"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-3.5-turbo\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.tools.databricks import UCFunctionToolkit\n",
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"\n",
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"tools = (\n",
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" UCFunctionToolkit(\n",
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" # You can find the SQL warehouse ID in its UI after creation.\n",
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" warehouse_id=\"xxxx123456789\"\n",
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" )\n",
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" .include(\n",
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" # Include functions as tools using their qualified names.\n",
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" # You can use \"{catalog_name}.{schema_name}.*\" to get all functions in a schema.\n",
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" \"main.tools.python_exec\",\n",
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" )\n",
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" .get_tools()\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"You are a helpful assistant. Make sure to use tool for information.\",\n",
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" ),\n",
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" (\"placeholder\", \"{chat_history}\"),\n",
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" (\"human\", \"{input}\"),\n",
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" (\"placeholder\", \"{agent_scratchpad}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"agent = create_tool_calling_agent(llm, tools, prompt)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `main__tools__python_exec` with `{'code': 'print(36939 * 8922.4)'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m{\"format\": \"SCALAR\", \"value\": \"329584533.59999996\\n\", \"truncated\": false}\u001b[0m\u001b[32;1m\u001b[1;3mThe result of the multiplication 36939 * 8922.4 is 329,584,533.60.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'input': '36939 * 8922.4',\n",
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" 'output': 'The result of the multiplication 36939 * 8922.4 is 329,584,533.60.'}"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
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"agent_executor.invoke({\"input\": \"36939 * 8922.4\"})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "llm",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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