Advanced usage
Custom Response
Advanced usage
自定义响应
PandasAI 提供了以自定义方式处理聊天响应的灵活性。默认情况下，PandasAI 包含一个 ResponseParser 类，可以扩展该类以根据您的需求修改响应输出。
您可以选择提供一个自定义解析器，例如 StreamlitResponse，像这样配置对象：
示例用法
import os
import pandas as pd
from pandasai import SmartDatalake
from pandasai.responses.response_parser import ResponseParser
# This class overrides default behaviour how dataframe is returned
# By Default PandasAI returns the SmartDataFrame
class PandasDataFrame(ResponseParser):
def __init__(self, context) -> None:
super().__init__(context)
def format_dataframe(self, result):
# Returns Pandas Dataframe instead of SmartDataFrame
return result["value"]
employees_df = pd.DataFrame(
{
"EmployeeID": [1, 2, 3, 4, 5],
"Name": ["John", "Emma", "Liam", "Olivia", "William"],
"Department": ["HR", "Sales", "IT", "Marketing", "Finance"],
}
)
salaries_df = pd.DataFrame(
{
"EmployeeID": [1, 2, 3, 4, 5],
"Salary": [5000, 6000, 4500, 7000, 5500],
}
)
# By default, unless you choose a different LLM, it will use BambooLLM.
# You can get your free API key signing up at https://pandabi.ai (you can also configure it in your .env file)
os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"
agent = SmartDatalake(
[employees_df, salaries_df],
config={"llm": llm, "verbose": True, "response_parser": PandasDataFrame},
)
response = agent.chat("Return a dataframe of name against salaries")
# Returns the response as Pandas DataFrame
Streamlit 示例
import os
import pandas as pd
from pandasai import SmartDatalake
from pandasai.responses.streamlit_response import StreamlitResponse
employees_df = pd.DataFrame(
{
"EmployeeID": [1, 2, 3, 4, 5],
"Name": ["John", "Emma", "Liam", "Olivia", "William"],
"Department": ["HR", "Sales", "IT", "Marketing", "Finance"],
}
)
salaries_df = pd.DataFrame(
{
"EmployeeID": [1, 2, 3, 4, 5],
"Salary": [5000, 6000, 4500, 7000, 5500],
}
)
# By default, unless you choose a different LLM, it will use BambooLLM.
# You can get your free API key signing up at https://pandabi.ai (you can also configure it in your .env file)
os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"
agent = SmartDatalake(
[employees_df, salaries_df],
config={"verbose": True, "response_parser": StreamlitResponse},
)
agent.chat("Plot salaries against name")
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