
Most common position in a pipeline	After a Chat Generator
Mandatory init variables	“tools”: A list of `Tools` that can be invoked
Mandatory run variables	“messages”: A list of `ChatMessage` objects from a Chat Generator containing tool calls
Output variables	“tool_messages”: A list of `ChatMessage` objects with tool role. Each `ChatMessage` objects wraps the result of a tool invocation.
API reference	Tools
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/tools/tool_invoker.py

Overview

A ToolInvoker is a component that processes ChatMessage objects containing tool calls. It invokes the corresponding tools and returns the results as a list of ChatMessage objects. Each tool is defined with a name, description, parameters, and a function that performs the task. The ToolInvoker manages these tools and handles the invocation process.

You can pass multiple tools to the ToolInvoker component, and it will automatically choose the right tool to call based on tool calls produced by a Language Model.

The ToolInvoker has two additionally helpful parameters:

convert_result_to_json_string: Use json.dumps (when True) or str (when False) to convert the result into a string.
raise_on_failure: If True, it will raise an exception in case of errors. If False, it will return a ChatMessage object with error=True and a description of the error in result. Use this, for example, when you want to keep the Language Model running in a loop and fixing its errors.

📘
ChatMessage and Tool Data Classes
Follow the links to learn more about ChatMessage and Tool data classes.

Usage

On its own

from haystack.dataclasses import ChatMessage, ToolCall, Tool
from haystack.components.tools import ToolInvoker

# Tool definition
def dummy_weather_function(city: str):
    return f"The weather in {city} is 20 degrees."
parameters = {"type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"]}
tool = Tool(name="weather_tool",
            description="A tool to get the weather",
            function=dummy_weather_function,
            parameters=parameters)
            
# Usually, the ChatMessage with tool_calls is generated by a Language Model
# Here, we create it manually for demonstration purposes
tool_call = ToolCall(
    tool_name="weather_tool",
    arguments={"city": "Berlin"}
)
message = ChatMessage.from_assistant(tool_calls=[tool_call])

# ToolInvoker initialization and run
invoker = ToolInvoker(tools=[tool])
result = invoker.run(messages=[message])

print(result)

>>  {
>>      'tool_messages': [
>>          ChatMessage(
>>              _role=<ChatRole.TOOL: 'tool'>,
>>              _content=[
>>                  ToolCallResult(
>>                      result='"The weather in Berlin is 20 degrees."',
>>                      origin=ToolCall(
>>                          tool_name='weather_tool',
>>                          arguments={'city': 'Berlin'},
>>                          id=None
>>                      )
>>                  )
>>              ],
>>              _meta={}
>>          )
>>      ]
>>  }

In a pipeline

The following code snippet shows how to process a user query about the weather. First, we define a Tool for fetching weather data, then we initialize a ToolInvoker to execute this tool, while using an OpenAIChatGenerator to generate responses. A ConditionalRouter is used in this pipeline to route messages based on whether they contain tool calls. The pipeline connects these components, processes a user message asking for the weather in Berlin, and outputs the result.

from haystack.dataclasses import Tool, ChatMessage
from haystack.components.tools import ToolInvoker
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.routers import ConditionalRouter
from haystack import Pipeline
from typing import List  # Ensure List is imported

# Define a dummy weather tool
import random

def dummy_weather(location: str):
    return {"temp": f"{random.randint(-10, 40)} °C",
            "humidity": f"{random.randint(0, 100)}%"}

weather_tool = Tool(
    name="weather",
    description="A tool to get the weather",
    function=dummy_weather,
    parameters={
        "type": "object",
        "properties": {"location": {"type": "string"}},
        "required": ["location"],
    },
)

# Initialize the ToolInvoker with the weather tool
tool_invoker = ToolInvoker(tools=[weather_tool])

# Initialize the ChatGenerator
chat_generator = OpenAIChatGenerator(model="gpt-4o-mini", tools=[weather_tool])

# Define routing conditions
routes = [
    {
        "condition": "{{replies[0].tool_calls | length > 0}}",
        "output": "{{replies}}",
        "output_name": "there_are_tool_calls",
        "output_type": List[ChatMessage],  # Use direct type
    },
    {
        "condition": "{{replies[0].tool_calls | length == 0}}",
        "output": "{{replies}}",
        "output_name": "final_replies",
        "output_type": List[ChatMessage],  # Use direct type
    },
]

# Initialize the ConditionalRouter
router = ConditionalRouter(routes, unsafe=True)

# Create the pipeline
pipeline = Pipeline()
pipeline.add_component("generator", chat_generator)
pipeline.add_component("router", router)
pipeline.add_component("tool_invoker", tool_invoker)

# Connect components
pipeline.connect("generator.replies", "router")
pipeline.connect("router.there_are_tool_calls", "tool_invoker.messages")  # Correct connection

# Example user message
user_message = ChatMessage.from_user("What is the weather in Berlin?")

# Run the pipeline
result = pipeline.run({"messages": [user_message]})

# Print the result
print(result)

{
   "tool_invoker":{
      "tool_messages":[
         "ChatMessage(_role=<ChatRole.TOOL":"tool"">",
         "_content="[
            "ToolCallResult(result=""{'temp': '33 °C', 'humidity': '79%'}",
            "origin=ToolCall(tool_name=""weather",
            "arguments="{
               "location":"Berlin"
            },
            "id=""call_pUVl8Cycssk1dtgMWNT1T9eT"")",
            "error=False)"
         ],
         "_name=None",
         "_meta="{
            
         }")"
      ]
   }
}

Additional References

🧑‍🍳 Cookbooks:

Overview

📘ChatMessage and Tool Data Classes

Usage

On its own

In a pipeline

Additional References

📘
ChatMessage and Tool Data Classes