Issue: Passing context(previous question and answer) to the create_pandas_dataframe_agent function.

[akash97715]

Issue you'd like to raise.

Hello Team, We are finding a way to pass the context, previous question and answer to create_pandas_dataframe_agent. Can u please help me understand how i can pass the context(previous question and answer) to the create_pandas_dataframe_agent. it will be helpful if you have any such example implementation

Thanks, Akash

Suggestion:

No response

[dosubot[bot]]

🤖

Hey @akash97715, good to see you again! I hope you're doing well. Let's dive into your new issue.

Based on the information you've provided, it seems you want to pass the context (previous question and answer) to the create_pandas_dataframe_agent function in LangChain. Unfortunately, this function does not directly support passing the context. However, you can modify the function to include this feature.

You can add the previous question and answer as part of the prefix parameter. The prefix parameter is used to construct the prompt that is passed to the language model. By including the previous question and answer in the prefix, you can provide the language model with the context it needs to generate a response.

Here is an example of how you can modify the function call to include the previous question and answer:

previous_question = "What was the previous question?"
previous_answer = "This was the previous answer."

prefix = f"Previous Question: {previous_question}\nPrevious Answer: {previous_answer}\n"

agent = create_pandas_dataframe_agent(
    llm=llm,
    df=df,
    prefix=prefix,
    # other parameters...
)

In this example, the prefix includes the previous question and answer, followed by a newline character (\n). This will ensure that the language model has the context of the previous question and answer when generating a response.

For more details, you can refer to the source code of the create_pandas_dataframe_agent function in the LangChain codebase here.

I hope this helps! If you have any other questions, feel free to ask.

Best, Dosu

Sources

• DOC: Passing Context to Conversational Chain
• How to add conversational memory to pandas toolkit agent?
• libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py

---

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[akash97715]

Thanks a lot for quick response. Just wanted to know lets say if i want to pass previous 5 questions and answers to the create_pandas_dataframe_agent. how can i acheive that. is there a way i can pass the list of tuples of questions and answers?

[akash97715]

@dosu-bot Thanks a lot for quick response. Just wanted to know lets say if i want to pass previous 5 questions and answers to the create_pandas_dataframe_agent. how can i acheive that. is there a way i can pass the list of tuples of questions and answers?

[dosubot[bot]]

🤖

Based on the information provided, it seems that the create_pandas_dataframe_agent function in the LangChain framework does support passing multiple context entries (questions and answers) as an argument. However, the context entries need to be in the form of pandas dataframes, not a list of tuples.

The _get_prompt_and_tools and _get_functions_prompt_and_tools functions, which are called within create_pandas_dataframe_agent, check if the df argument is a list of pandas dataframes. If it is, they call the _get_multi_prompt or _get_functions_multi_prompt functions respectively, which are designed to handle multiple dataframes.

Here is the relevant code:

if isinstance(df, list):
    for item in df:
        if not isinstance(item, pd.DataFrame):
            raise ValueError(f"Expected pandas object, got {type(df)}")
    return _get_multi_prompt(
        df,
        prefix=prefix,
        suffix=suffix,
        input_variables=input_variables,
        include_df_in_prompt=include_df_in_prompt,
        number_of_head_rows=number_of_head_rows,
    )

and

if isinstance(df, list):
    for item in df:
        if not isinstance(item, pd.DataFrame):
            raise ValueError(f"Expected pandas object, got {type(df)}")
    return _get_functions_multi_prompt(
        df,
        prefix=prefix,
        suffix=suffix,
        include_df_in_prompt=include_df_in_prompt,
        number_of_head_rows=number_of_head_rows,
    )

You can find this code in the libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py file.

Unfortunately, I wasn't able to find how the _get_multi_prompt function handles multiple dataframes in the LangChain codebase. It's possible that this function concatenates the dataframes and generates a single prompt from them, but without more information, I can't say for sure.

If you want to pass a list of tuples of questions and answers to the create_pandas_dataframe_agent function, you would first need to convert each tuple into a pandas dataframe. Here's an example of how you might do this:

import pandas as pd

# Your list of tuples
qa_tuples = [
    ("Question 1", "Answer 1"),
    ("Question 2", "Answer 2"),
    ("Question 3", "Answer 3"),
    ("Question 4", "Answer 4"),
    ("Question 5", "Answer 5"),
]

# Convert each tuple to a dataframe
df_list = [pd.DataFrame([tup], columns=["Question", "Answer"]) for tup in qa_tuples]

# Pass the list of dataframes to the function
create_pandas_dataframe_agent(df_list)

I hope this helps! If you have any other questions or need further clarification, please let me know.

Sources

• libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py

---

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[akash97715]

@dosu-bot Please confirm the final call to the create_pandas_dataframe_agent can be done like this

qa_tuples = [
    ("Question 1", "Answer 1"),
    ("Question 2", "Answer 2"),
    ("Question 3", "Answer 3"),
    ("Question 4", "Answer 4"),
    ("Question 5", "Answer 5"),
]

# Convert each tuple to a dataframe
df_list = [pd.DataFrame([tup], columns=["Question", "Answer"]) for tup in qa_tuples]

agent = create_pandas_dataframe_agent(
    llm=llm,
    df=df,
    prefix=df_list ,
    # other parameters...
)

[dosubot[bot]]

🤖

Based on the information provided in the LangChain framework, the create_pandas_dataframe_agent function can indeed accept a list of pandas dataframes as the df parameter. However, the prefix parameter is expected to be a string, not a list of dataframes. The prefix parameter is used to specify a string that will be prepended to the prompt that is sent to the language model.

Here is the relevant part of the function definition:

def create_pandas_dataframe_agent(
    llm: BaseLanguageModel,
    df: Any,
    agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    callback_manager: Optional[BaseCallbackManager] = None,
    prefix: Optional[str] = None,
    suffix: Optional[str] = None,
    input_variables: Optional[List[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 = "force",
    agent_executor_kwargs: Optional[Dict[str, Any]] = None,
    include_df_in_prompt: Optional[bool] = True,
    number_of_head_rows: int = 5,
    extra_tools: Sequence[BaseTool] = (),
    **kwargs: Dict[str, Any],
) -> AgentExecutor:
    ...

In your case, you might want to concatenate all your dataframes into one and pass that as the df parameter. Here's how you could do it:

qa_tuples = [
    ("Question 1", "Answer 1"),
    ("Question 2", "Answer 2"),
    ("Question 3", "Answer 3"),
    ("Question 4", "Answer 4"),
    ("Question 5", "Answer 5"),
]

# Convert each tuple to a dataframe and concatenate them
df = pd.concat([pd.DataFrame([tup], columns=["Question", "Answer"]) for tup in qa_tuples])

agent = create_pandas_dataframe_agent(
    llm=llm,
    df=df,
    # other parameters...
)

This way, you're passing a single dataframe that contains all your questions and answers to the create_pandas_dataframe_agent function.

You can find more details about the create_pandas_dataframe_agent function in the LangChain repository.

Sources

• libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py

---

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[akash97715]

@dosu-bot Let me give u little context reg my usecase. I have a df with 20 columns and the columns contains some analytics records. i will be passing this dataframe with 20 columns in he df and asking the question to it, the llm will be responding with answer. Now i want the questions and the answers that is there in first run, i want to pass that as context when i will be calling the agent.run with another question. like that i want to pass the previous 5 questions and answers along with my df which contains 20 columns. Any idea how i can pass this previous 5 questions and answers along with my dataframe with 20 columns?

[dosubot[bot]]

🤖

Based on the context provided, it seems that you can use the input_variables parameter of the create_pandas_dataframe_agent function to pass the previous 5 questions and their corresponding answers, along with your dataframe containing 20 columns. This parameter accepts a list of strings, which are the names of the variables you want to include in the prompt.

However, the provided context does not show how to pass the previous 5 questions and their corresponding answers to the function. Therefore, you would need to create a mechanism to store and pass these questions and answers. This could be done by storing the questions and answers in a list or dictionary, and then passing this to the function.

The dataframe can be passed directly to the create_pandas_dataframe_agent function through the df parameter.

Here is an example of how you might do this:

# Assuming `df` is your DataFrame and `prev_qa` is a list of your previous questions and answers
agent_executor = create_pandas_dataframe_agent(
    llm=my_llm,
    df=df,
    input_variables=prev_qa,
    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    callback_manager=my_callback_manager,
    prefix=my_prefix,
    suffix=my_suffix,
    verbose=True,
    return_intermediate_steps=True,
    max_iterations=15,
    max_execution_time=None,
    early_stopping_method="force",
    agent_executor_kwargs=my_agent_executor_kwargs,
    include_df_in_prompt=True,
    number_of_head_rows=5,
    extra_tools=my_extra_tools,
)

Please note that the input_variables parameter is used to include additional variables in the prompt, but the actual mechanism of how these variables are included in the prompt is not shown in the provided context. Therefore, this answer is based on the assumption that the input_variables parameter can be used to include the previous questions and answers in the prompt.

The input_variables parameter is handled in the _get_prompt_and_tools and _get_functions_prompt_and_tools functions, which are called by the create_pandas_dataframe_agent function. If input_variables is not provided, it is initialized with default values. For a single dataframe, the default values are ["input", "agent_scratchpad"] and if include_df_in_prompt is True, "df_head" is added to the list. For multiple dataframes, the default values are ["input", "agent_scratchpad", "num_dfs"] and if include_df_in_prompt is True, "dfs_head" is added to the list. These variables are then passed to the ZeroShotAgent.create_prompt function as part of the prompt creation process.

In the _get_functions_prompt_and_tools function, if input_variables is provided, it raises a ValueError as input_variables is not supported at the moment.

You can find more details in the LangChain codebase.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• How to enable the memory mechanism when using create_pandas_dataframe_agent?
• Issue: Dataframe with conversation agent for chat models
• How to add conversational memory to pandas toolkit agent?
• libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py

---

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[akash97715]

@dosu-bot I am using the input variable like this attached in screenshot. I am not passing "stop", but it says i am using stop. Can u please clearify whats going wrong here

[dosubot[bot]]

🤖

Based on the information you've provided, it seems like the error message you're encountering might be due to the default behavior of the create_pandas_dataframe_agent function in the LangChain framework. If you do not provide a list of input_variables when calling this function, it will use a default list of input variables.

For the _get_single_prompt function, the default input_variables are ["input", "agent_scratchpad"], and if include_df_head is True, "df_head" is added to the list.

For the _get_multi_prompt function, the default input_variables are ["input", "agent_scratchpad", "num_dfs"], and if include_dfs_head is True, "dfs_head" is added to the list.

These functions are called by the _get_prompt_and_tools function, which is in turn called by the create_pandas_dataframe_agent function. If you do not provide a list of input_variables when calling create_pandas_dataframe_agent, the default list of input variables will be used.

Here are the relevant code snippets:

def _get_single_prompt(
    ...
    if input_variables is None:
        input_variables = ["input", "agent_scratchpad"]
        if include_df_head:
            input_variables += ["df_head"]
    ...
) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
    ...

def _get_multi_prompt(
    ...
    if input_variables is None:
        input_variables = ["input", "agent_scratchpad", "num_dfs"]
        if include_dfs_head:
            input_variables += ["dfs_head"]
    ...
) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
    ...

def _get_prompt_and_tools(
    ...
    if isinstance(df, list):
        ...
        return _get_multi_prompt(
            ...
            input_variables=input_variables,
            ...
        )
    else:
        ...
        return _get_single_prompt(
            ...
            input_variables=input_variables,
            ...
        )
    ...
) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
    ...

def create_pandas_dataframe_agent(
    ...
    if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
        prompt, base_tools = _get_prompt_and_tools(
            ...
            input_variables=input_variables,
            ...
        )
    ...
) -> AgentExecutor:
    ...

If you want to avoid the use of "stop" as an input variable, you should provide your own list of input_variables when calling create_pandas_dataframe_agent. You can find more details in the LangChain repository.

I hope this helps! If you have any other questions, feel free to ask.

Sources

• ValueError when using Azure and chat-zero-shot-react-description agent
• libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py
• libs/langchain/langchain/agents/agent_toolkits/pandas/init.py

---

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

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[akash97715]

@dosu-bot I am initializing the create_csv_agent as shown in below screenshot. I am using agent_type as ZERO_SORT(its by default). While eecuting the chain i see the final answer is been calculated only on 5 rows. if i use agenttype as OPENAI_FUNCTION, it works fine, just want to understand why the calculation is performed only on top 5 rows with ZERO_SHORT agent. let me know if i am missing something here.

[dosubot[bot]]

🤖

Based on the similar issues I found in the LangChain repository, it seems that the ZERO_SHORT agent by default only considers the top 5 rows of the dataframe. This is controlled by the variable 'number_of_head_rows' in the file langchain/agents/agent_toolkits/pandas/base.py. You can modify this variable to the number of rows you want the agent to consider. Here are the references to the similar issues:

• Issue: langchain pandas df agent, not taking full df in context
• Pandas / CSV agent only show partial results from dataframe head

As for the difference between ZERO_SHORT and OPENAI_FUNCTION agent types, the ZERO_SHORT agent performs a reasoning step before acting, trying to understand the context and the task at hand before making a decision or taking an action. On the other hand, the OPENAI_FUNCTION agent is optimized for using OpenAI functions. You can find more details in the libs/langchain/langchain/agents/agent_types.py file.

Unfortunately, I couldn't find specific information on how the ZERO_SHORT agent type handles dataframes in the LangChain framework. It's possible that the answer may be available elsewhere or I could have missed it. If you have more specific questions or need further clarification, feel free to ask.

Sources

• Issue: langchain pandas df agent, not taking full df in context
• Pandas / CSV agent only show partial results from dataframe head
• libs/langchain/langchain/agents/agent_types.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[akash97715]

@dosu-bot Thank you for the response, in the below screenshot i changed number_of_head_rows to 50, now agent is evaluating on 50 rows and returning me the response. Can u please help me here how can i configure the agent so that it will perform the calculations on the complete dataset and return the correct response. Or is this a bug already there with ZERO_SHORT agent type logged in langchain. Please let me know

[dosubot[bot]]

🤖

Based on the similar issue you're experiencing, it seems like the number_of_head_rows parameter in the create_csv_agent function is limiting the number of rows the agent is evaluating. This parameter is set to 50 in your case, which is why the agent is only evaluating the first 50 rows of your dataset.

To have the agent evaluate the entire dataset, you can try setting the number_of_head_rows parameter to None. This should theoretically allow the agent to process all rows in your dataset. Here's how you can modify your code:

agent = create_csv_agent(
    llm=llm,
    csv_path=csv_path,
    agent_type=AgentType.ZERO_SHOT,
    callback_manager=callback_manager,
    prefix=prefix,
    suffix=suffix,
    input_variables=input_variables,
    verbose=True,
    return_intermediate_steps=True,
    max_iterations=15,
    max_execution_time=None,
    early_stopping_method="force",
    agent_executor_kwargs=agent_executor_kwargs,
    include_df_in_prompt=True,
    number_of_head_rows=None,  # Set this to None
    extra_tools=extra_tools,
)

However, please note that processing a large dataset might take a significant amount of time and resources, depending on the size of your dataset and the capacity of your machine.

If this doesn't solve your issue, it might be helpful to provide more details about the dataset you're working with and the specific calculations you're trying to perform. This could help identify if there's a specific issue with the ZERO_SHORT agent type or if there's another way to achieve your desired outcome.

For more information, you can refer to the similar solved issue in the LangChain repository: Pandas / CSV agent only show partial results from dataframe head.

Sources

• Pandas / CSV agent only show partial results from dataframe head

---

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[akash97715]

@dosu-bot Team, keeping that to None, load the entire dataset, initializing agent has taken 5 minutes, hen i run the agent it says it exceeded the token limit. PFB for reference.

[dosubot[bot]]

🤖

Based on the error message you provided and the context of your issue, it seems like you're encountering a token limit error due to the large size of your dataset. This is a common issue when dealing with large datasets in language models, as they have a maximum token limit. When the number of tokens in your dataset exceeds this limit, you'll encounter an error.

There are a few ways to address this issue based on similar issues that have been solved in the LangChain repository:

1. Adjust parameters to limit the data included in the prompt: You can do this by decreasing the number of sample rows in table info and restricting the tables to include in the table info. Here is an example:

SQLDatabase.from_uri(
    conn_string,
    sample_rows_in_table_info=1, # default 3
    include_tables=["my_table_1"],  # will restrict to just using my_table_1
)

This solution was suggested in Issue: Token Limit Exceeded Error in SQL Database Agent.

2. Switch to a larger model: If you're using a smaller model, you might want to consider switching to a larger one that has a higher token limit. For example, you can switch to the gpt-3.5-turbo-16k-0613 model and then set agent_type=AgentType.OPENAI_FUNCTIONS in the create_sql_agent method to increase the token limit to 16k. Here is an example:

from langchain.agents.agent_types import AgentType

agent_executor = create_sql_agent(
    llm=OpenAI(temperature=0),
    toolkit=toolkit,
    verbose=True,
    agent_type=AgentType.OPENAI_FUNCTIONS
)

This solution was also suggested in Issue: Token Limit Exceeded Error in SQL Database Agent.

3. Implement a custom tool that splits the result into chunks: This can help ensure that each chunk fits within the token limit. Here is an example:

from langchain.text_splitter import TokenTextSplitter
from langchain.tools.sql_database.tool import QuerySQLDataBaseTool

def split_text(text, chunk_size, chunk_overlap=0):
    text_splitter = TokenTextSplitter(
        chunk_size=chunk_size, chunk_overlap=chunk_overlap
    )
    yield from text_splitter.split_text(text)

class QuerySQLDatabaseTool2(QuerySQLDataBaseTool):
    def _run(
        self,
        query: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> str:
        result = self.db.run_no_throw(query)
        return next(split_text(result, chunk_size=14_000))

This solution was also suggested in Issue: Token Limit Exceeded Error in SQL Database Agent.

4. Create a custom memory class that prunes the memory buffer: This can help prevent the openai.error.InvalidRequestError from occurring. Here's how you can do it:

class CustomConversationTokenBufferMemory(ConversationTokenBufferMemory):
    def clear(self):
        super().clear()
        buffer = self.chat_memory.messages
        curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
        while curr_buffer_length > self.max_token_limit:
            buffer.pop(0)
            curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)

Then, use this custom memory class when creating your agent:

agent_memory = CustomConversationTokenBufferMemory(k=1)

return AgentExecutor.from_agent_and_tools(
    agent=custom_agent,
    tools=tools,
    verbose=True,
    memory=agent_memory,
)

This solution was suggested in cannot clear memory when token limit exceeded.

As for the long initialization time, it's likely due to the large size of your dataset. The create_csv_agent function in the LangChain framework uses the pd.read_csv function from pandas to load the CSV file into a DataFrame, which can be time-consuming for large datasets. To improve performance, you could modify the create_csv_agent function to use the chunksize parameter when calling pd.read_csv, allowing the CSV file to be read in chunks of a specified size. This should help to reduce memory usage and potentially improve performance when processing large datasets.

I hope this helps! If you have any other questions or need further clarification, feel free to ask.

Sources

• Issue: Token Limit Exceeded Error in SQL Database Agent
• create_csv_agent prompt errors in latest version
• cannot clear memory when token limit exceeded
• libs/experimental/langchain_experimental/agents/agent_toolkits/csv/base.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.