langchain/docs/modules/agents/tools/custom_tools.ipynb

Defining Custom Tools

When constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:

• name (str), is required and must be unique within a set of tools provided to an agent
• description (str), is optional but recommended, as it is used by an agent to determine tool use
• return_direct (bool), defaults to False
• args_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters.

There are two main ways to define a tool, we will cover both in the example below.

In [1]:

# Import things that are needed generically
from langchain import LLMMathChain, SerpAPIWrapper
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool

Initialize the LLM to use for the agent.

In [2]:

llm = ChatOpenAI(temperature=0)

Completely New Tools - String Input and Output

The simplest tools accept a single query string and return a string output. If your tool function requires multiple arguments, you might want to skip down to the StructuredTool section below.

There are two ways to do this: either by using the Tool dataclass, or by subclassing the BaseTool class.

Tool dataclass

The 'Tool' dataclass wraps functions that accept a single string input and returns a string output.

In [3]:

# Load the tool configs that are needed.
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
tools = [
    Tool.from_function(
        func=search.run,
        name = "Search",
        description="useful for when you need to answer questions about current events"
        # coroutine= ... <- you can specify an async method if desired as well
    ),
]

/Users/wfh/code/lc/lckg/langchain/chains/llm_math/base.py:50: UserWarning: Directly instantiating an LLMMathChain with an llm is deprecated. Please instantiate with llm_chain argument or using the from_llm class method.
  warnings.warn(

You can also define a custom `args_schema`` to provide more information about inputs.

In [4]:

from pydantic import BaseModel, Field

class CalculatorInput(BaseModel):
    question: str = Field()
        

tools.append(
    Tool.from_function(
        func=llm_math_chain.run,
        name="Calculator",
        description="useful for when you need to answer questions about math",
        args_schema=CalculatorInput
        # coroutine= ... <- you can specify an async method if desired as well
    )
)

In [5]:

# Construct the agent. We will use the default agent type here.
# See documentation for a full list of options.
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

In [6]:

agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")

Out [6]:

> Entering new AgentExecutor chain...
I need to find out Leo DiCaprio's girlfriend's name and her age
Action: Search
Action Input: "Leo DiCaprio girlfriend"
Observation: After rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his "age bracket" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.
Thought:I still need to find out his current girlfriend's name and age
Action: Search
Action Input: "Leo DiCaprio current girlfriend"
Observation: Just Jared on Instagram: “Leonardo DiCaprio & girlfriend Camila Morrone couple up for a lunch date!
Thought:Now that I know his girlfriend's name is Camila Morrone, I need to find her current age
Action: Search
Action Input: "Camila Morrone age"
Observation: 25 years
Thought:Now that I have her age, I need to calculate her age raised to the 0.43 power
Action: Calculator
Action Input: 25^(0.43)

> Entering new LLMMathChain chain...
25^(0.43)```text
25**(0.43)
```
...numexpr.evaluate("25**(0.43)")...

Answer: 3.991298452658078
> Finished chain.

Observation: Answer: 3.991298452658078
Thought:I now know the final answer
Final Answer: Camila Morrone's current age raised to the 0.43 power is approximately 3.99.

> Finished chain.

"Camila Morrone's current age raised to the 0.43 power is approximately 3.99."

Subclassing the BaseTool class

You can also directly subclass BaseTool. This is useful if you want more control over the instance variables or if you want to propagate callbacks to nested chains or other tools.

In [7]:

from typing import Optional, Type

from langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun

class CustomSearchTool(BaseTool):
    name = "custom_search"
    description = "useful for when you need to answer questions about current events"

    def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
        """Use the tool."""
        return search.run(query)
    
    async def _arun(self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("custom_search does not support async")
    
class CustomCalculatorTool(BaseTool):
    name = "Calculator"
    description = "useful for when you need to answer questions about math"
    args_schema: Type[BaseModel] = CalculatorInput

    def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
        """Use the tool."""
        return llm_math_chain.run(query)
    
    async def _arun(self, query: str,  run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("Calculator does not support async")

In [8]:

tools = [CustomSearchTool(), CustomCalculatorTool()]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

In [9]:

agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")

Out [9]:

> Entering new AgentExecutor chain...
I need to use custom_search to find out who Leo DiCaprio's girlfriend is, and then use the Calculator to raise her age to the 0.43 power.
Action: custom_search
Action Input: "Leo DiCaprio girlfriend"
Observation: After rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his "age bracket" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.
Thought:I need to find out the current age of Eden Polani.
Action: custom_search
Action Input: "Eden Polani age"
Observation: 19 years old
Thought:Now I can use the Calculator to raise her age to the 0.43 power.
Action: Calculator
Action Input: 19 ^ 0.43

> Entering new LLMMathChain chain...
19 ^ 0.43```text
19 ** 0.43
```
...numexpr.evaluate("19 ** 0.43")...

Answer: 3.547023357958959
> Finished chain.

Observation: Answer: 3.547023357958959
Thought:I now know the final answer.
Final Answer: 3.547023357958959

> Finished chain.

'3.547023357958959'

Using the tool decorator

To make it easier to define custom tools, a @tool decorator is provided. This decorator can be used to quickly create a Tool from a simple function. The decorator uses the function name as the tool name by default, but this can be overridden by passing a string as the first argument. Additionally, the decorator will use the function's docstring as the tool's description.

In [10]:

from langchain.tools import tool

@tool
def search_api(query: str) -> str:
    """Searches the API for the query."""
    return f"Results for query {query}"

search_api

You can also provide arguments like the tool name and whether to return directly.

In [12]:

@tool("search", return_direct=True)
def search_api(query: str) -> str:
    """Searches the API for the query."""
    return "Results"

In [13]:

search_api

Out [13]:

Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd66310>, coroutine=None)

You can also provide args_schema to provide more information about the argument

In [14]:

class SearchInput(BaseModel):
    query: str = Field(description="should be a search query")
        
@tool("search", return_direct=True, args_schema=SearchInput)
def search_api(query: str) -> str:
    """Searches the API for the query."""
    return "Results"

In [15]:

search_api

Out [15]:

Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class '__main__.SearchInput'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bcf0ee0>, coroutine=None)

Custom Structured Tools

If your functions require more structured arguments, you can use the StructuredTool class directly, or still subclass the BaseTool class.

StructuredTool dataclass

To dynamically generate a structured tool from a given function, the fastest way to get started is with StructuredTool.from_function().

In [10]:

import requests
from langchain.tools import StructuredTool

def post_message(url: str, body: dict, parameters: Optional[dict] = None) -> str:
    """Sends a POST request to the given url with the given body and parameters."""
    result = requests.post(url, json=body, params=parameters)
    return f"Status: {result.status_code} - {result.text}"

tool = StructuredTool.from_function(post_message)

Subclassing the BaseTool

The BaseTool automatically infers the schema from the _run method's signature.

In [11]:

from typing import Optional, Type

from langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun
            
class CustomSearchTool(BaseTool):
    name = "custom_search"
    description = "useful for when you need to answer questions about current events"

    def _run(self, query: str, engine: str = "google", gl: str = "us", hl: str = "en", run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
        """Use the tool."""
        search_wrapper = SerpAPIWrapper(params={"engine": engine, "gl": gl, "hl": hl})
        return search_wrapper.run(query)
    
    async def _arun(self, query: str,  engine: str = "google", gl: str = "us", hl: str = "en", run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("custom_search does not support async")



# You can provide a custom args schema to add descriptions or custom validation

class SearchSchema(BaseModel):
    query: str = Field(description="should be a search query")
    engine: str = Field(description="should be a search engine")
    gl: str = Field(description="should be a country code")
    hl: str = Field(description="should be a language code")

class CustomSearchTool(BaseTool):
    name = "custom_search"
    description = "useful for when you need to answer questions about current events"
    args_schema: Type[SearchSchema] = SearchSchema

    def _run(self, query: str, engine: str = "google", gl: str = "us", hl: str = "en", run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
        """Use the tool."""
        search_wrapper = SerpAPIWrapper(params={"engine": engine, "gl": gl, "hl": hl})
        return search_wrapper.run(query)
    
    async def _arun(self, query: str,  engine: str = "google", gl: str = "us", hl: str = "en", run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("custom_search does not support async")
    
    

Using the decorator

The tool decorator creates a structured tool automatically if the signature has multiple arguments.

In [12]:

import requests
from langchain.tools import tool

@tool
def post_message(url: str, body: dict, parameters: Optional[dict] = None) -> str:
    """Sends a POST request to the given url with the given body and parameters."""
    result = requests.post(url, json=body, params=parameters)
    return f"Status: {result.status_code} - {result.text}"

Modify existing tools

Now, we show how to load existing tools and modify them directly. In the example below, we do something really simple and change the Search tool to have the name Google Search.

In [13]:

from langchain.agents import load_tools

In [14]:

tools = load_tools(["serpapi", "llm-math"], llm=llm)

In [15]:

tools[0].name = "Google Search"

In [16]:

agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

In [17]:

agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")

Out [17]:

> Entering new AgentExecutor chain...
I need to find out Leo DiCaprio's girlfriend's name and her age.
Action: Google Search
Action Input: "Leo DiCaprio girlfriend"
Observation: After rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his "age bracket" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.
Thought:I still need to find out his current girlfriend's name and her age.
Action: Google Search
Action Input: "Leo DiCaprio current girlfriend age"
Observation: Leonardo DiCaprio has been linked with 19-year-old model Eden Polani, continuing the rumour that he doesn't date any women over the age of ...
Thought:I need to find out the age of Eden Polani.
Action: Calculator
Action Input: 19^(0.43)
Observation: Answer: 3.547023357958959
Thought:I now know the final answer.
Final Answer: The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55.

> Finished chain.

"The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55."

Defining the priorities among Tools

When you made a Custom tool, you may want the Agent to use the custom tool more than normal tools.

For example, you made a custom tool, which gets information on music from your database. When a user wants information on songs, You want the Agent to use the custom tool more than the normal Search tool. But the Agent might prioritize a normal Search tool.

This can be accomplished by adding a statement such as Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?' to the description.

An example is below.

In [18]:

# Import things that are needed generically
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.llms import OpenAI
from langchain import LLMMathChain, SerpAPIWrapper
search = SerpAPIWrapper()
tools = [
    Tool(
        name = "Search",
        func=search.run,
        description="useful for when you need to answer questions about current events"
    ),
    Tool(
        name="Music Search",
        func=lambda x: "'All I Want For Christmas Is You' by Mariah Carey.", #Mock Function
        description="A Music search engine. Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?'",
    )
]

agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

In [20]:

agent.run("what is the most famous song of christmas")

Out [20]:

> Entering new AgentExecutor chain...
 I should use a music search engine to find the answer
Action: Music Search
Action Input: most famous song of christmas'All I Want For Christmas Is You' by Mariah Carey. I now know the final answer
Final Answer: 'All I Want For Christmas Is You' by Mariah Carey.

> Finished chain.

"'All I Want For Christmas Is You' by Mariah Carey."

Using tools to return directly

Often, it can be desirable to have a tool output returned directly to the user, if it’s called. You can do this easily with LangChain by setting the return_direct flag for a tool to be True.

In [21]:

llm_math_chain = LLMMathChain(llm=llm)
tools = [
    Tool(
        name="Calculator",
        func=llm_math_chain.run,
        description="useful for when you need to answer questions about math",
        return_direct=True
    )
]

In [22]:

llm = OpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

In [23]:

agent.run("whats 2**.12")

Out [23]:

> Entering new AgentExecutor chain...
 I need to calculate this
Action: Calculator
Action Input: 2**.12Answer: 1.086734862526058

> Finished chain.

'Answer: 1.086734862526058'