LangGraph Agents - Help Needed

[mraguth]

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Example Code

Here my agent executor is using python repl.

# Define the prompt
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", template),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
        ("human", "{input}"),
        MessagesPlaceholder("chat_history", optional=True),
    ]
)

repl = PythonAstREPLTool(
    locals={"df": df},
    name="python_repl",
    description="Runs Python code and returns the output of the final line",
    args_schema=PythonInputs,
    prompt = prompt,
)


tools = [repl, retriever_tool]
agent = create_openai_functions_agent(
    llm=llm, prompt=prompt, tools=tools
)
agent_executor = AgentExecutor(
    agent=agent, 
    tools=tools, 
    max_iterations=10, 
    early_stopping_method="force",    ##### Changed from generate due to Generate error.
    allow_dangerous_code=True,
    allow_dangerous_requests=True,
) | (lambda x: x["output"])

Description

I like to move my simple langchain agent_executor to LangGraph Agent. Code I used for Agent_Executor is above cell. Based on this example, can you help me in creating a single LangGraph agent to take the df dataframe and produce the output based on Human 'Input'?

System Info

Name: langgraph
Version: 0.2.27

[dosubot[bot]]

I found a similar unsolved discussion that might be relevant to your question: Request for Integrating AgentExecutor Functionality into LangGraph Reasoner Node for Python REPL Queries ^[1].

To create a LangGraph agent that processes a DataFrame (df) and produces output based on human input, you can follow these steps:

1. Define the Tools: Use the PythonAstREPLTool to execute Python code with the DataFrame and any other tools you need.

2. Create the LangGraph Agent: Use the create_react_agent function to set up the agent with the defined tools.

3. Set Up the Workflow: Define a StateGraph to manage the workflow, ensuring that the agent processes the input and executes the necessary tools.

Here's a basic outline of how you can set this up:

from langgraph.prebuilt import create_react_agent
from langchain_core.tools import Tool
from langchain_experimental.utilities import PythonAstREPLTool

# Define the Python REPL tool
repl_tool = PythonAstREPLTool(
    locals={"df": df},
    name="python_repl",
    description="Runs Python code and returns the output of the final line",
)

# Define other tools if needed
tools = [repl_tool, retriever_tool]

# Create the LangGraph agent
langgraph_agent_executor = create_react_agent(llm, tools)

# Define the workflow
def process_input(input_data):
    messages = langgraph_agent_executor.invoke({"messages": [("human", input_data)]})
    return messages["messages"][-1].content

# Example usage
input_data = "Your input here"
output = process_input(input_data)
print(output)

This setup uses the create_react_agent function to create a LangGraph agent that can process the DataFrame and human input, executing the necessary tools to produce the desired output. Adjust the tools and workflow as needed to fit your specific requirements.

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu. I am getting message from above code like this .... Could you please provide the dataset or specify which dataset you are referring to? How I proceed in this?

[dosubot[bot]]

To address the message you're receiving, it seems like the LangGraph agent is expecting a dataset to be specified or provided. Here are a few steps you can take to resolve this:

1. Ensure DataFrame is Available: Make sure that the DataFrame df is properly initialized and available in the local scope where the PythonAstREPLTool is being used. This is crucial for the tool to access and manipulate the DataFrame.

2. Check Tool Configuration: Verify that the PythonAstREPLTool is correctly configured with access to the DataFrame. This involves passing the DataFrame in the locals parameter, like so:

   repl_tool = PythonAstREPLTool(
       locals={"df": df},
       name="python_repl",
       description="Runs Python code and returns the output of the final line",
   )

3. Input Data: Ensure that the input data you are providing to the agent includes any necessary context or information about the dataset. If the agent's logic requires specifying a dataset, make sure this is included in the input message.

4. Modify Prompt or Logic: If the agent's prompt or logic is designed to ask for a dataset, you might need to adjust it to handle cases where the dataset is already provided. This could involve modifying the prompt template or the logic within the agent to recognize when the DataFrame is already available.

By ensuring the DataFrame is correctly set up and accessible, and by adjusting the input or logic as needed, you should be able to resolve the issue and proceed with using the LangGraph agent.

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu This is the message "It seems that I cannot access local files directly. If you provide the data or specify the file path, I can help you with the code to count the rows in your DataFrame."

[dosubot[bot]]

To address the message you're receiving, it seems like the LangGraph agent is unable to access local files directly. You can modify the setup to allow the agent to work with a specified file path. Here's how you can adjust your code to load a DataFrame from a file path and count the rows:

1. Modify the Code to Accept a File Path: You can create a function to load the DataFrame from a specified file path.

from pathlib import Path
import pandas as pd

# Define a function to load a DataFrame from a specified file path
def load_dataframe(file_path):
    return pd.read_csv(file_path)

# Example usage: specify the path to your CSV file
file_path = Path("your_file.csv")  # Replace "your_file.csv" with your actual file name
df = load_dataframe(file_path)

# Now you can count the rows in the DataFrame
row_count = len(df)
print(f"The number of rows in the DataFrame is: {row_count}")

2. Ensure the DataFrame is Available to the Agent: Make sure the DataFrame df is properly initialized and available in the local scope where the PythonAstREPLTool is being used.

3. Adjust the Agent's Logic: If the agent's logic requires specifying a dataset, ensure that the input message includes the necessary context or information about the dataset.

By following these steps, you should be able to provide the necessary data to the agent and proceed with counting the rows in your DataFrame ^[1].

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu. I a getting this error "AttributeError: 'ellipsis' object has no attribute 'bind_tools'"

[dosubot[bot]]

The error message "AttributeError: 'ellipsis' object has no attribute 'bind_tools'" suggests that there might be a placeholder (...) in your code where an actual object or method is expected. This often happens when a piece of code is incomplete or when a placeholder is mistakenly left in the code.

Here are a few steps to troubleshoot and resolve this issue:

1. Check for Placeholders: Look through your code for any instances of ... (ellipsis) that might be used as placeholders. Replace them with the appropriate objects or method calls.

2. Verify Object Initialization: Ensure that all objects, especially those that should have a bind_tools method, are properly initialized. For example, if you're working with a chat model or agent that should have this method, make sure it's correctly set up.

3. Review Method Calls: Double-check that you're calling bind_tools on the correct object. The object should be an instance of a class that supports the bind_tools method, such as a chat model compatible with the Fireworks tool-calling API in LangChain.

By addressing these points, you should be able to resolve the AttributeError and ensure that your code is correctly utilizing the bind_tools method.

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu Is it possible to build LangGraph with multiple agents that use the same PythonAstREPLTool but operate on different datasets? If so, could you provide a sample code snippet demonstrating this?

[dosubot[bot]]

I wasn't able to find a specific example in the LangChain documentation that demonstrates using PythonAstREPLTool with different datasets in separate agents. However, you can achieve this by configuring each agent with its own instance of PythonAstREPLTool, each initialized with a different dataset. Here's a conceptual example of how you might set this up:

from langchain_experimental.utilities import PythonAstREPLTool
from langgraph.prebuilt import create_react_agent

# Define datasets
df1 = ...  # Your first dataset
df2 = ...  # Your second dataset

# Define Python REPL tools for each dataset
repl_tool_1 = PythonAstREPLTool(
    locals={"df": df1},
    name="python_repl_1",
    description="Runs Python code on dataset 1 and returns the output of the final line",
)

repl_tool_2 = PythonAstREPLTool(
    locals={"df": df2},
    name="python_repl_2",
    description="Runs Python code on dataset 2 and returns the output of the final line",
)

# Create agents for each tool
agent_1 = create_react_agent(llm, [repl_tool_1])
agent_2 = create_react_agent(llm, [repl_tool_2])

# Define workflows for each agent
def process_input_agent_1(input_data):
    messages = agent_1.invoke({"messages": [("human", input_data)]})
    return messages["messages"][-1].content

def process_input_agent_2(input_data):
    messages = agent_2.invoke({"messages": [("human", input_data)]})
    return messages["messages"][-1].content

# Example usage
input_data_1 = "Your input for dataset 1"
output_1 = process_input_agent_1(input_data_1)
print(f"Output for dataset 1: {output_1}")

input_data_2 = "Your input for dataset 2"
output_2 = process_input_agent_2(input_data_2)
print(f"Output for dataset 2: {output_2}")

In this setup, each agent is configured with a PythonAstREPLTool that operates on a different dataset. You can then invoke each agent separately with the appropriate input for its dataset. Adjust the logic and tools as needed to fit your specific requirements.

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu. It looks great. How I create a langgraph from these two agents and compile ask a question? please give me a sample code.

[dosubot[bot]]

To create a LangGraph from the two agents and compile it to ask a question, you can use the StateGraph to define the workflow involving both agents. Here's a sample code snippet demonstrating how you can set this up:

from langgraph.graph import StateGraph, END

# Define the state as a list of messages
class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], operator.add]

# Create a new StateGraph
workflow = StateGraph(AgentState)

# Add nodes for each agent
workflow.add_node("agent_1", process_input_agent_1)
workflow.add_node("agent_2", process_input_agent_2)

# Set the entry point to the first agent
workflow.set_entry_point("agent_1")

# Add conditional edges to switch between agents or end the workflow
workflow.add_conditional_edges(
    "agent_1",
    lambda state: "continue" if some_condition(state) else "end",
    {
        "continue": "agent_2",
        "end": END,
    },
)

workflow.add_conditional_edges(
    "agent_2",
    lambda state: "end",
    {
        "end": END,
    },
)

# Compile the workflow
app = workflow.compile()

# Example usage: ask a question
input_data = {
    "messages": [
        HumanMessage(content="Your question here")
    ]
}

# Process the input through the compiled application
for output in app.stream(input_data):
    print(output)

In this setup:

• StateGraph: Manages the workflow, with each node representing an agent.
• Nodes: agent_1 and agent_2 are added as nodes, each processing input with their respective datasets.
• Conditional Edges: Define the flow between agents based on conditions. Adjust some_condition to fit your logic.
• Compile and Execute: The workflow is compiled and executed by streaming inputs through the application.

Adjust the logic and conditions as needed to fit your specific requirements.

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu... validation error. Can you help me?

ValidationError Traceback (most recent call last)
Cell In[199], line 9
2 input_data = {
3 "messages": [
4 HumanMessage(content="""how many rows in the dataframe?""")
5 ]
6 }
8 # Process the input through the compiled application
----> 9 for output in app.stream(input_data):
10 print(output)

File ~\AppData\Roaming\Python\Python312\site-packages\langgraph\pregel_init_.py:1290, in Pregel.stream(self, input, config, stream_mode, output_keys, interrupt_before, interrupt_after, debug, subgraphs)
1279 # Similarly to Bulk Synchronous Parallel / Pregel model
1280 # computation proceeds in steps, while there are channel updates
1281 # channel updates from step N are only visible in step N+1
1282 # channels are guaranteed to be immutable for the duration of the step,
1283 # with channel updates applied only at the transition between steps
1284 while loop.tick(
1285 input_keys=self.input_channels,
1286 interrupt_before=interrupt_before_,
1287 interrupt_after=interrupt_after_,
1288 manager=run_manager,
1289 ):
-> 1290 for _ in runner.tick(
...
Input should be a valid string [type=string_type, input_value={'messages': [HumanMessag... response_metadata={})]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.9/v/string_type

[dosubot[bot]]

The ValidationError you're encountering indicates that the app.stream method expects a string input, but it seems you're providing a dictionary instead. The stream method in LangGraph requires a string input, as shown in the example:

chunks = []
for chunk in model.stream("what color is the sky?"):
    chunks.append(chunk)
    print(chunk.content, end="|", flush=True)

To resolve this, you should pass a string directly to the app.stream method. If you want to include a message, you can extract the content from your input_data dictionary and pass it as a string. Here's how you can adjust your code:

# Extract the content from the input_data dictionary
input_string = input_data["messages"][0].content

# Process the input through the compiled application
for output in app.stream(input_string):
    print(output)

This should align the input format with what the stream method expects ^[1].

To continue talking to Dosu, mention @dosu.