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Databricks Unity Catalog (UC)

本笔记本展示了如何将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,这对LLMs正确调用函数至关重要。

在这个示例笔记本中,我们创建了一个简单的Python函数,该函数执行任意代码,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仓库内的安全隔离环境中运行。

%pip install --upgrade --quiet databricks-sdk langchain-community mlflow
<!--IMPORTS:[{"imported": "ChatDatabricks", "source": "langchain_community.chat_models.databricks", "docs": "https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html", "title": "Databricks Unity Catalog (UC)"}]-->
from langchain_community.chat_models.databricks import ChatDatabricks

llm = ChatDatabricks(endpoint="databricks-meta-llama-3-70b-instruct")
<!--IMPORTS:[{"imported": "UCFunctionToolkit", "source": "langchain_community.tools.databricks", "docs": "https://python.langchain.com/api_reference/community/tools/langchain_community.tools.databricks.tool.UCFunctionToolkit.html", "title": "Databricks Unity Catalog (UC)"}]-->
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()
)
<!--IMPORTS:[{"imported": "AgentExecutor", "source": "langchain.agents", "docs": "https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html", "title": "Databricks Unity Catalog (UC)"}, {"imported": "create_tool_calling_agent", "source": "langchain.agents", "docs": "https://python.langchain.com/api_reference/langchain/agents/langchain.agents.tool_calling_agent.base.create_tool_calling_agent.html", "title": "Databricks Unity Catalog (UC)"}, {"imported": "ChatPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html", "title": "Databricks Unity Catalog (UC)"}]-->
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"})


> Entering new AgentExecutor chain...

Invoking: `main__tools__python_exec` with `{'code': 'print(36939 * 8922.4)'}`


{"format": "SCALAR", "value": "329584533.59999996\n", "truncated": false}The result of the multiplication 36939 * 8922.4 is 329,584,533.60.

> Finished chain.
{'input': '36939 * 8922.4',
'output': 'The result of the multiplication 36939 * 8922.4 is 329,584,533.60.'}

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