def create_pandas_dataframe_agent(
llm:llm,
df:df,
#callback_manager: Optional[BaseCallbackManager] = None,
prefix: str=PREFIX,
suffix: str= SUFFIX,
input_variables:str= None,
verbose:bool=False,
return_intermediate_steps:bool=False,
#max_iterations: Optional[int]=15,
#max_execution_time: Optional[float]= None,
early_stopping_method:str="generate",
**kwargs: Any,
) -> AgentExecutor:
"""Construct a pandas agent from an LLM and dataframe."""
import pandas as pd
if not isinstance(df, pd.DataFrame):
raise ValueError(f"Expected pandas object, got {type(df)}")
if input_variables is None:
input_variables = ["df","input"]
tools = [PythonAstREPLTool(locals={"df": df})]
PREFIX = """
You are working with a pandas dataframe in Python. The name of the dataframe is `df`.
You should use the tools below to answer the question posed of you:"""
SUFFIX = """
This is the result of `print(df.head())`:
{df}
Begin!
{chat_history}
Question: {input}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=PREFIX,
suffix=SUFFIX,
input_variables=[ "df","input","chat_history"]
)
print(prompt)
partial_prompt = prompt.partial(df=str(df.head()))
print(f"\n{partial_prompt}")
doc_chain = load_qa_chain(llm, chain_type="map_reduce")
llm_chain = LLMChain(
llm=llm,
prompt=partial_prompt,
#callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(
llm_chain=llm_chain,
allowed_tools=tool_names,
#callback_manager=callback_manager,
**kwargs,
)
return AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
verbose=verbose,
return_intermediate_steps=return_intermediate_steps,
#max_iterations=max_iterations,
#max_execution_time=30,
#max_iterations=2,
early_stopping_method=early_stopping_method,
#callback_manager=callback_manager,
memory = memory
)
chain=create_pandas_dataframe_agent(llm=llm,
df=df,
input_variables= None,
verbose=True,
return_intermediate_steps=False,
early_stopping_method="generate",
max_iterations=2)
from langchain.agents import Tool
tools = [
Tool(
name='Knowledge Base',
func=chain.run,
description=(
'use this tool when answering general knowledge queries to get '
'more information about the topic'
),
)
]
from langchain.agents import initialize_agent
agent = initialize_agent(
agent='conversational-react-description',
tools=tools,
llm=llm,
verbose=False,
max_iterations=2,
early_stopping_method='generate',
memory=memory,
handle_parsing_errors=_handle_error,
# max_execution_time=30,
# agent_kwargs={"format_instructions": format_instructions},
)
query="give the names of in Zambia project"
# agent(query)
try:
response = agent(query)
except ValueError as e:
response = str(e)
if not response.startswith("Could not parse LLM output: `"):
raise e
response = response.removeprefix("Could not parse LLM output: `").removesuffix("`")
l
Here, I have used the pandas dataframe agent with appropriate tools and the outcome is absolutely correct but the execution time is too high. But, the actual problem is when I restart my flask server again, my code gets executed quite fast. I am using AzureOpenAI library. Also, I am facing an inconsistent response issue as well. When I run the same query again, it gives me the right output.
I am expecting a consistent response which is right and the execution time is also low.
