Databricks Unity Catalog (UC)
此笔记本演示了如何将 UC 函数用作 LangChain 工具。
请参阅 Databricks 文档（AWS|Azure|GCP）以了解如何在 UC 中创建 SQL 或 Python 函数。请勿跳过函数和参数注释，这些注释对于 LLM 正确调用函数至关重要。
在此示例笔记本中，我们创建了一个执行任意代码的简单 Python 函数，并将其用作 LangChain 工具
CREATE FUNCTION main.tools.python_exec (
code STRING COMMENT 'Python code to execute. Remember to print the final result to stdout.'
)
RETURNS STRING
LANGUAGE PYTHON
COMMENT 'Executes Python code and returns its stdout.'
AS $$
import sys
from io import StringIO
stdout = StringIO()
sys.stdout = stdout
exec(code)
return stdout.getvalue()
$$
它在 Databricks SQL 数据仓库内的安全且隔离的环境中运行。
%pip install --upgrade --quiet databricks-sdk langchain-community mlflow
from langchain_community.chat_models.databricks import ChatDatabricks
llm = ChatDatabricks(endpoint="databricks-meta-llama-3-70b-instruct")
API 参考：ChatDatabricks
from langchain_community.tools.databricks import UCFunctionToolkit
tools = (
UCFunctionToolkit(
# You can find the SQL warehouse ID in its UI after creation.
warehouse_id="xxxx123456789"
)
.include(
# Include functions as tools using their qualified names.
# You can use "{catalog_name}.{schema_name}.*" to get all functions in a schema.
"main.tools.python_exec",
)
.get_tools()
)
API 参考：UCFunctionToolkit
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful assistant. Make sure to use tool for information.",
),
("placeholder", "{chat_history}"),
("human", "{input}"),
("placeholder", "{agent_scratchpad}"),
]
)
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "36939 * 8922.4"})
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3m
Invoking: `main__tools__python_exec` with `{'code': 'print(36939 * 8922.4)'}`
[0m[36;1m[1;3m{"format": "SCALAR", "value": "329584533.59999996\n", "truncated": false}[0m[32;1m[1;3mThe result of the multiplication 36939 * 8922.4 is 329,584,533.60.[0m
[1m> Finished chain.[0m
{'input': '36939 * 8922.4',
'output': 'The result of the multiplication 36939 * 8922.4 is 329,584,533.60.'}