Which differences are between .with_structured_output & .bind_tools

[Nachoeigu]

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Example Code

from langchain_openai import ChatOpenAI

Description

Hi everyone,

I find unclear the distinction between .with_structured_output and bind_tools methods.

When one is better than the other? In which scenario is best practice to consider one over the other? I mean, I know that with bind_tools the model can call N tools, but if you use structured_output also you can obtain the same results if you specify the response in the Pydantic object.

I research the documentation but I couldn´t find a good answer to this. I think it should be added in the doc the key differences between one and the other.

System Info

langchain

[dosubot[bot]]

Hey @Nachoeigu! 👋 I'm here to assist you with any questions or issues you have while waiting for a human maintainer. Let's dive into this together!

The .with_structured_output and .bind_tools methods in LangChain serve different purposes and are used in different scenarios.

.with_structured_output

The .with_structured_output method is used to wrap a model so that it returns outputs formatted to match a given schema. This method is useful when you need the model's output to adhere to a specific structure, such as a JSON schema or a function-calling format. This method is available on ChatModels capable of tool calling.

Usage Scenario:

• When you need the model's output to be in a specific structured format.
• When you want to ensure that the output can be easily parsed and used in downstream applications.

Example:

from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_anthropic import ChatAnthropic

class Joke(BaseModel):
    setup: str = Field(description="The setup of the joke")
    punchline: str = Field(description="The punchline to the joke") 

model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0)
structured_llm = model.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats. Make sure to call the Joke function.")

.bind_tools

The .bind_tools method is used to bind tool-like objects to a chat model. This method assumes that the model is compatible with the OpenAI tool-calling API and allows the model to call these tools during its execution.

Usage Scenario:

• When you want to enhance the model's capabilities by allowing it to call external tools.
• When you need the model to interact with other functions or APIs dynamically during its execution.

Example:

def bind_tools(
    self,
    tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
    *,
    tool_choice: Optional[
        Union[dict, str, Literal["auto", "none", "required", "any"], bool]
    ] = None,
    **kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
    formatted_tools = [convert_to_openai_tool(tool)["function"] for tool in tools]
    formatted_tool_choice = "required" if tool_choice == "any" else tool_choice
    return super().bind(
        available_tools=formatted_tools, tool_choice=formatted_tool_choice, **kwargs
    )

When to Use Each Method

• Use .with_structured_output when you need the model's output to conform to a specific structure, making it easier to parse and use in subsequent processes.
• Use .bind_tools when you want the model to have the ability to call external tools or functions, thereby extending its functionality and making it more interactive.

In summary, .with_structured_output is best for ensuring structured output, while .bind_tools is best for integrating external tools into the model's workflow ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[Nachoeigu]

@dosu This is the unique difference? Because, if I want to call an external function I can achieve the same with structured output and then parsing the structured output and based on each condition call one function or other. I mean, I don´t find any example where bind_tools is more useful than with_structured_output, and the same with with_structured_output over bind_tools

[dosubot[bot]]

Yes, the unique difference between .with_structured_output and .bind_tools is that .with_structured_output ensures structured output while .bind_tools allows calling external tools.

• .with_structured_output: Ensures that the output conforms to a specific schema, either a dictionary or a Pydantic model. This is useful when you need the output to be in a specific format for further processing or validation.

• .bind_tools: Binds external tools to the language model, allowing it to call these tools during its execution. This is useful when you need the language model to interact with external systems or perform specific actions that require external tools.

Example where .with_structured_output is more useful:

When you need the output to be in a specific format for further processing or validation, such as generating a structured JSON response.

from langchain_community.chat_models import ChatEdenAI
from pydantic import BaseModel

class MySchema(BaseModel):
    name: str
    age: int

chat = ChatEdenAI(provider="openai", model="gpt-4")
structured_chat = chat.with_structured_output(MySchema)

messages = [HumanMessage(content="Tell me about John Doe")]
result = structured_chat.invoke(messages)
print(result)  # Output will be an instance of MySchema

Example where .bind_tools is more useful:

When you need the language model to interact with external systems or perform specific actions that require external tools, such as calling an API.

from langchain_community.chat_models import ChatEdenAI
from langchain_core.tools import BaseTool

class MyTool(BaseTool):
    def run(self, input: str) -> str:
        return f"Processed {input}"

chat = ChatEdenAI(provider="openai", model="gpt-4")
tool_bound_chat = chat.bind_tools([MyTool()])

messages = [HumanMessage(content="Use the tool to process this input")]
result = tool_bound_chat.invoke(messages)
print(result)  # Output will include the result from MyTool

In summary, use .with_structured_output when you need structured output for further processing, and use .bind_tools when you need the model to call external tools dynamically ^[1][2].

To continue talking to Dosu, mention @dosu.

[michael-newsrx]

How does one stop ChatHuggingFace from treating a supplied schema as a tool call?

[vinaysharma8]

@dosu. I understand that there is a difference between the .with_structured_output and .bind_tools, But how should i use both when i have the following requirement:
1. I am creating a langgraph agent which should give a structured output defined in a pydatic object.
2. This agent have some tools.

Issue I am facing :
I have used both .with_structured_output and .bind_tools to get structured output and bind the defined tools, Now what is happening is agent is giving me structured output but not making any tool_calls.

[abdulsaboor3211]

did you find any answer to this problem? I am facing the same problem.

[ysr-plus-ultra]

check source code of with_structured_output.
there's wrapper(converter) that create tool from pydantic object.

pydantic_tool = ... #converter from .with_structured_output() source code
llm = ... #your llm
llm.bind(
    tools=[pydantic_tool, your_tool1, your_tool2])

I didn't check this work. but think you got the idea

[cognicto]

can we use bind_tools with structured output ? I need the model to call external tool as well as provide structured output.

[medric-del]

any update here ?

[ejubber20]

Also looking to use both here. In my case, my agent must keep track of a state object with a lot of fields and update those fields based on the user's input, so I'm using with_structured_output. However, I also want the agent to be able to call a tool that retrieves information from my database.

[ysr-plus-ultra]

in case of BaseChatOpenAI, .with_structured_output uses bind tools internally.