langchain-ai / langchain

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Issue: How can we specify multiple types when initialize an agent #14372

Closed zhengxingmao closed 11 months ago

zhengxingmao commented 11 months ago

Issue you'd like to raise.

Can the 'AgentType parameter of β€˜initialize_agent’ function only be of one type? How can I specify multiple types? I want to set agent type to 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION' and 'ZERO_SHOT_REACT_DESCRIPTION' at the same time ?

Suggestion:

Set agent type to 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION' and 'ZERO_SHOT_REACT_DESCRIPTION' at the same time when create a agent?

dosubot[bot] commented 11 months ago

πŸ€–

Hello @zhengxingmao!

I'm Dosu, an automated helper here to assist you with your queries and issues related to the LangChain repository. While we wait for a human maintainer, I'm here to help you navigate through any questions or bugs you might have, and even guide you on how to become a contributor.

Looking forward to assisting you!

Based on the current design of the initialize_agent function in LangChain, it's not possible to set the 'AgentType' parameter to both 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION' and 'ZERO_SHOT_REACT_DESCRIPTION' simultaneously. The 'AgentType' parameter is designed to accept a single value, not a list or tuple of multiple values.

Here is the relevant code snippet from the initialize_agent function:

if agent is None and agent_path is None:
    agent = AgentType.ZERO_SHOT_REACT_DESCRIPTION
if agent is not None:
    if agent not in AGENT_TO_CLASS:
        raise ValueError(
            f"Got unknown agent type: {agent}. "
            f"Valid types are: {AGENT_TO_CLASS.keys()}."
        )
    agent_cls = AGENT_TO_CLASS[agent]
    agent_kwargs = agent_kwargs or {}
    agent_obj = agent_cls.from_llm_and_tools(
        llm, tools, callback_manager=callback_manager, **agent_kwargs
    )

As you can see, the function checks if the provided 'agent' is in the 'AGENT_TO_CLASS' dictionary, and if it is, it uses the corresponding class to create an agent object. If the 'agent' is not in the dictionary, it raises a ValueError. This design does not allow for multiple 'AgentType' values to be processed simultaneously.

If you need to use both 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION' and 'ZERO_SHOT_REACT_DESCRIPTION' in your application, you would need to initialize two separate agents, one for each 'AgentType'.

I hope this clarifies your question. If you have further questions or need more help, feel free to ask.

Source Code

-Dosu

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zhengxingmao commented 11 months ago

@dosu-bot Thank you for your reply. If I have initialized two agents, when will I use 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION' and when will I use 'ZERO_SHOT_REACT_DESCRIPTION'.Could you please give me an example of creating two agents.

dosubot[bot] commented 11 months ago

πŸ€–

Sure, I can provide an example of how to initialize two separate agents with the 'AgentType' as 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION' and 'ZERO_SHOT_REACT_DESCRIPTION' respectively.

First, you need to import the necessary modules and classes. Then, you can create instances of the agents with the desired 'AgentType'. Here's an example:

from langchain.agents import Agent, AgentType

# Initialize an agent with 'AgentType' as 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION'
agent1 = Agent(AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION)

# Initialize another agent with 'AgentType' as 'ZERO_SHOT_REACT_DESCRIPTION'
agent2 = Agent(AgentType.ZERO_SHOT_REACT_DESCRIPTION)

As for when to use each type of agent:

You can find more details about these agent types in the agent_types.py file in the LangChain repository.

Regarding the parameters required to initialize an agent, you need a list of tools (functions that the agent will use to perform its tasks), an instance of a language model (that the agent will use to generate responses), and a string representing the agent type. Here's an example from the test_initialize.py file:

fake_llm = FakeLLM()
agent_executor = initialize_agent(
    [my_tool],  # type: ignore[list-item]
    fake_llm,
    "zero-shot-react-description",  # type: ignore[arg-type]
)

In this code, initialize_agent is called with a list containing my_tool, fake_llm, and "zero-shot-react-description" as arguments. The returned agent_executor is an instance of the agent with the specified type and tools.

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

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zhengxingmao commented 11 months ago

@dosu-bot After creating two types of agents, how do I automatically select the corresponding agents for future use? Could you please give me a complete example, including two types of agent creation and automatic agent recognition.

dosubot[bot] commented 11 months ago

πŸ€–

Based on your request, you can create two agents using the LangChain framework with different 'AgentType' and then automatically select the appropriate agent based on the type of task or input. Here's how you can do it:

First, let's create the two agents:

from langchain_experimental.openai_assistant import OpenAIAssistantRunnable

# Define the tools for the agents
tools = [{"type": "code_interpreter"}]

# Create the first agent with 'AgentType' as 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION'
structured_chat_agent = OpenAIAssistantRunnable.create_assistant(
    name="structured_chat_agent",
    instructions="You are a structured chat agent. Respond to user queries based on structured data.",
    tools=tools,
    model="gpt-4-1106-preview"
)

# Create the second agent with 'AgentType' as 'ZERO_SHOT_REACT_DESCRIPTION'
zero_shot_agent = OpenAIAssistantRunnable.create_assistant(
    name="zero_shot_agent",
    instructions="You are a zero-shot agent. Respond to user queries without any prior knowledge.",
    tools=tools,
    model="gpt-4-1106-preview"
)

Then, you can create a function that takes the task type as an argument and returns the appropriate agent:

def select_agent(task_type):
    if task_type == 'structured_chat':
        return structured_chat_agent
    elif task_type == 'zero_shot':
        return zero_shot_agent
    else:
        raise ValueError("Invalid task type. Please choose either 'structured_chat' or 'zero_shot'.")

# Usage
task_type = 'structured_chat'  # or 'zero_shot'
selected_agent = select_agent(task_type)
output = selected_agent.invoke({"content": "Your query here"})

Please note that the actual 'AgentType' values ('STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION' and 'ZERO_SHOT_REACT_DESCRIPTION') are not explicitly used in the provided context. The agent creation process in the context does not seem to differentiate between different agent types. The provided solution assumes that the difference in agent types is reflected in the 'instructions' argument when creating the agent. If the 'AgentType' values have a specific usage in your application, please provide more context.

As for the specific functionalities of the 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION' and 'ZERO_SHOT_REACT_DESCRIPTION' agent types in the LangChain framework:

  1. 'ZERO_SHOT_REACT_DESCRIPTION': This is a zero-shot agent that performs a reasoning step before acting. However, the specific details of how this reasoning step is performed or what it entails are not provided in the given context.

  2. 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION': This is a zero-shot react agent that is optimized for chat models. This agent is capable of invoking tools that have multiple inputs. This suggests that it can handle more complex interactions in a chat environment, possibly dealing with multiple topics or threads of conversation at once.

Here is the code snippet that defines these agent types:

class AgentType(str, Enum):
    ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description"
    """A zero shot agent that does a reasoning step before acting."""

    STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION = (
        "structured-chat-zero-shot-react-description"
    )
    """An zero-shot react agent optimized for chat models.

    This agent is capable of invoking tools that have multiple inputs.
    """

You can find more details in the libs/langchain/langchain/agents/agent_types.py file in the LangChain repository.

Please let me know if you need further clarification or have any other questions.

Sources


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Help me be more useful! Please leave a πŸ‘ if this is helpful and πŸ‘Ž if it is irrelevant.

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zhengxingmao commented 11 months ago

@dosu-bot # Usage task_type = 'structured_chat' # or 'zero_shot' selected_agent = select_agent(task_type) output = selected_agent.invoke({"content": "Your query here"}) This place is still manually selected, not automatic. Please implement the logic of automatic selection.

dosubot[bot] commented 11 months ago

πŸ€–

Based on the context provided, you can create two types of agents using the create_conversational_retrieval_agent function from the LangChain framework. Here's an example of how to create these agents:

from langchain_core.language_models import ChatOpenAI
from langchain.tools.base import BaseTool

# Initialize the language model and tools
llm = ChatOpenAI()
tools = [BaseTool()]

# Create an agent that remembers intermediate steps
agent_with_memory = create_conversational_retrieval_agent(
    llm=llm,
    tools=tools,
    remember_intermediate_steps=True,
)

# Create an agent that does not remember intermediate steps
agent_without_memory = create_conversational_retrieval_agent(
    llm=llm,
    tools=tools,
    remember_intermediate_steps=False,
)

This code creates two agents: one that remembers intermediate steps (agent_with_memory) and one that does not (agent_without_memory). The remember_intermediate_steps parameter determines whether the agent should remember intermediate steps or not.

To automatically select the appropriate agent based on the task or input, you would need to define a function or a set of rules that determine which agent to use. However, the context does not provide enough information to create such a function. You might need to consider factors such as the complexity of the task, the type of input, and the specific requirements of your application when designing this function.

Regarding the 'AgentType' values, the LangChain framework provides several options, including ZERO_SHOT_REACT_DESCRIPTION, REACT_DOCSTORE, SELF_ASK_WITH_SEARCH, CONVERSATIONAL_REACT_DESCRIPTION, CHAT_ZERO_SHOT_REACT_DESCRIPTION, CHAT_CONVERSATIONAL_REACT_DESCRIPTION, STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, OPENAI_FUNCTIONS, and OPENAI_MULTI_FUNCTIONS. Each of these types has specific use-cases, and you can choose the one that best fits your needs.

For more information, you can refer to the LangChain repository and the issue that discusses a similar topic.

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zhengxingmao commented 11 months ago

@dosu-bot

# Usage
task_type = 'structured_chat'  # or 'zero_shot'
selected_agent = select_agent(task_type)
output = selected_agent.invoke({"content": "Your query here"})

This place is still manually selected, not automatic. Please implement the logic of automatic selection.

dosubot[bot] commented 11 months ago

πŸ€–

Based on the context provided, it seems like you want to automate the process of selecting the appropriate agent based on the task or input. To achieve this, you could create a higher-level function or class that takes the task or input as an argument and returns the appropriate agent. However, the specific implementation would depend on the characteristics or properties of the tasks that determine the selection of the 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION' agent or 'ZERO_SHOT_REACT_DESCRIPTION' agent.

From the context, it appears that the 'ZERO_SHOT_REACT_DESCRIPTION' agent is suitable for tasks that require a single step of reasoning before producing an output, while the 'STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION' agent is more suitable for tasks that involve chat models and require the use of multiple inputs.

Here's a simple example of how you might implement this:

class AgentSelector:
    def __init__(self, structured_chat_agent, zero_shot_agent):
        self.structured_chat_agent = structured_chat_agent
        self.zero_shot_agent = zero_shot_agent

    def select_agent(self, task):
        # Add your logic here to determine the appropriate agent based on the task
        if self.is_structured_chat_task(task):
            return self.structured_chat_agent
        elif self.is_zero_shot_task(task):
            return self.zero_shot_agent
        else:
            raise ValueError("Invalid task type. Please choose either 'structured_chat' or 'zero_shot'.")

    def is_structured_chat_task(self, task):
        # Add your logic here to determine if the task is a structured chat task
        pass

    def is_zero_shot_task(self, task):
        # Add your logic here to determine if the task is a zero shot task
        pass

# Usage
agent_selector = AgentSelector(structured_chat_agent, zero_shot_agent)
selected_agent = agent_selector.select_agent(task)
output = selected_agent.invoke({"content": "Your query here"})

In this example, AgentSelector is a class that takes the available agents as arguments during initialization. The select_agent method takes a task as an argument and returns the appropriate agent based on the task. The is_structured_chat_task and is_zero_shot_task methods are placeholders for your logic to determine if a task is a structured chat task or a zero shot task, respectively.

Please note that this is a very basic example and you would need to replace the placeholder methods with your own logic to determine the appropriate agent based on the task. You might need to consider factors such as the complexity of the task, the required reasoning steps, the use of chat models, and the number of inputs required.

I hope this helps! If you need further assistance, please provide more information about how to determine the appropriate agent for a given task or input, and how to instantiate the agents.

Sources


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 πŸ‘ if this is helpful and πŸ‘Ž if it is irrelevant.

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

zhengxingmao commented 11 months ago

Thanks!