PowerBI 工具包
本笔记本展示了一个代理与 Power BI 数据集
交互。代理正在回答有关数据集的更一般性问题,并且能够从错误中恢复。
请注意,由于该代理正在积极开发中,所有答案可能并不正确。它运行在 executequery 端点 上,该端点不允许删除。
注意事项:
- 它依赖于使用 azure.identity 包进行身份验证,可以通过
pip install azure-identity
安装。或者,您可以使用字符串形式的令牌创建 Power BI 数据集,而无需提供凭据。 - 您还可以提供一个用户名,以便在启用了 RLS 的数据集上进行模拟使用。
- 该工具包使用大型语言模型(LLM)根据问题创建查询,代理使用 LLM 进行整体执行。
- 测试主要使用
gpt-3.5-turbo-instruct
模型,codex 模型似乎表现不佳。
初始化
<!--IMPORTS:[{"imported": "PowerBIToolkit", "source": "langchain_community.agent_toolkits", "docs": "https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.powerbi.toolkit.PowerBIToolkit.html", "title": "PowerBI Toolkit"}, {"imported": "create_pbi_agent", "source": "langchain_community.agent_toolkits", "docs": "https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.powerbi.base.create_pbi_agent.html", "title": "PowerBI Toolkit"}, {"imported": "PowerBIDataset", "source": "langchain_community.utilities.powerbi", "docs": "https://python.langchain.com/api_reference/community/utilities/langchain_community.utilities.powerbi.PowerBIDataset.html", "title": "PowerBI Toolkit"}, {"imported": "ChatOpenAI", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html", "title": "PowerBI Toolkit"}]-->
from azure.identity import DefaultAzureCredential
from langchain_community.agent_toolkits import PowerBIToolkit, create_pbi_agent
from langchain_community.utilities.powerbi import PowerBIDataset
from langchain_openai import ChatOpenAI
fast_llm = ChatOpenAI(
temperature=0.5, max_tokens=1000, model_name="gpt-3.5-turbo", verbose=True
)
smart_llm = ChatOpenAI(temperature=0, max_tokens=100, model_name="gpt-4", verbose=True)
toolkit = PowerBIToolkit(
powerbi=PowerBIDataset(
dataset_id="<dataset_id>",
table_names=["table1", "table2"],
credential=DefaultAzureCredential(),
),
llm=smart_llm,
)
agent_executor = create_pbi_agent(
llm=fast_llm,
toolkit=toolkit,
verbose=True,
)
示例:描述一个表
agent_executor.run("Describe table1")
示例:对一个表进行简单查询
在这个示例中,代理实际上找出了正确的查询以获取表的行数。
agent_executor.run("How many records are in table1?")
示例:运行查询
agent_executor.run("How many records are there by dimension1 in table2?")
agent_executor.run("What unique values are there for dimensions2 in table2")
示例:添加你自己的少量示例提示
# fictional example
few_shots = """
Question: How many rows are in the table revenue?
DAX: EVALUATE ROW("Number of rows", COUNTROWS(revenue_details))
----
Question: How many rows are in the table revenue where year is not empty?
DAX: EVALUATE ROW("Number of rows", COUNTROWS(FILTER(revenue_details, revenue_details[year] <> "")))
----
Question: What was the average of value in revenue in dollars?
DAX: EVALUATE ROW("Average", AVERAGE(revenue_details[dollar_value]))
----
"""
toolkit = PowerBIToolkit(
powerbi=PowerBIDataset(
dataset_id="<dataset_id>",
table_names=["table1", "table2"],
credential=DefaultAzureCredential(),
),
llm=smart_llm,
examples=few_shots,
)
agent_executor = create_pbi_agent(
llm=fast_llm,
toolkit=toolkit,
verbose=True,
)
agent_executor.run("What was the maximum of value in revenue in dollars in 2022?")