mirror of
https://github.com/deepset-ai/haystack.git
synced 2026-01-01 17:47:19 +00:00
129 lines
4.8 KiB
Plaintext
129 lines
4.8 KiB
Plaintext
---
|
||
title: "ComponentTool"
|
||
id: componenttool
|
||
slug: "/componenttool"
|
||
description: "This wrapper allows using Haystack components to be used as tools by LLMs."
|
||
---
|
||
|
||
# ComponentTool
|
||
|
||
This wrapper allows using Haystack components to be used as tools by LLMs.
|
||
|
||
<div className="key-value-table">
|
||
|
||
| | |
|
||
| --- | --- |
|
||
| **Mandatory init variables** | `component`: The Haystack component to wrap |
|
||
| **API reference** | [Tools](/reference/tools-api) |
|
||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/tools/component_tool.py |
|
||
|
||
</div>
|
||
|
||
## Overview
|
||
|
||
`ComponentTool` is a Tool that wraps Haystack components, allowing them to be used as tools by LLMs. ComponentTool automatically generates LLM-compatible tool schemas from component input sockets, which are derived from the component's `run` method signature and type hints.
|
||
|
||
It does input type conversion and offers support for components with run methods that have the following input types:
|
||
|
||
- Basic types (str, int, float, bool, dict)
|
||
- Dataclasses (both simple and nested structures)
|
||
- Lists of basic types (such as List[str])
|
||
- Lists of dataclasses (such as List[Document])
|
||
- Parameters with mixed types (such as List[Document], str...)
|
||
|
||
### Parameters
|
||
|
||
- `component` is mandatory and needs to be a Haystack component, either an existing one or a custom component.
|
||
- `name` is optional and defaults to the name of the component written in snake case, for example, "serper_dev_web_search" for SerperDevWebSearch.
|
||
- `description` is optional and defaults to the component’s docstring. It’s the description that explains to the LLM what the tool can be used for.
|
||
|
||
## Usage
|
||
|
||
Install the additional dependencies `docstring-parser` and `jsonschema` package to use the `ComponentTool`:
|
||
|
||
```shell
|
||
pip install docstring-parser jsonschema
|
||
```
|
||
|
||
### In a pipeline
|
||
|
||
You can create a `ComponentTool` from an existing `SerperDevWebSearch` component and let an `OpenAIChatGenerator` use it as a tool in a pipeline.
|
||
|
||
```python
|
||
from haystack import component, Pipeline
|
||
from haystack.tools import ComponentTool
|
||
from haystack.components.websearch import SerperDevWebSearch
|
||
from haystack.utils import Secret
|
||
from haystack.components.tools.tool_invoker import ToolInvoker
|
||
from haystack.components.generators.chat import OpenAIChatGenerator
|
||
from haystack.dataclasses import ChatMessage
|
||
|
||
## Create a SerperDev search component
|
||
search = SerperDevWebSearch(api_key=Secret.from_env_var("SERPERDEV_API_KEY"), top_k=3)
|
||
|
||
## Create a tool from the component
|
||
tool = ComponentTool(
|
||
component=search,
|
||
name="web_search", # Optional: defaults to "serper_dev_web_search"
|
||
description="Search the web for current information on any topic" # Optional: defaults to component docstring
|
||
)
|
||
|
||
## Create pipeline with OpenAIChatGenerator and ToolInvoker
|
||
pipeline = Pipeline()
|
||
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini", tools=[tool]))
|
||
pipeline.add_component("tool_invoker", ToolInvoker(tools=[tool]))
|
||
|
||
## Connect components
|
||
pipeline.connect("llm.replies", "tool_invoker.messages")
|
||
|
||
message = ChatMessage.from_user("Use the web search tool to find information about Nikola Tesla")
|
||
|
||
## Run pipeline
|
||
result = pipeline.run({"llm": {"messages": [message]}})
|
||
|
||
print(result)
|
||
```
|
||
|
||
### With the Agent Component
|
||
|
||
You can use `ComponentTool` with the [Agent](../pipeline-components/agents-1/agent.mdx) component. Internally, the `Agent` component includes a `ToolInvoker` and the ChatGenerator of your choice to execute tool calls and process tool results.
|
||
|
||
```python
|
||
from haystack.components.generators.chat import OpenAIChatGenerator
|
||
from haystack.dataclasses import ChatMessage
|
||
from haystack.tools import ComponentTool
|
||
from haystack.components.agents import Agent
|
||
from haystack.components.websearch import SerperDevWebSearch
|
||
from typing import List
|
||
|
||
## Create a SerperDev search component
|
||
search = SerperDevWebSearch(api_key=Secret.from_env_var("SERPERDEV_API_KEY"), top_k=3)
|
||
|
||
## Create a tool from the component
|
||
search_tool = ComponentTool(
|
||
component=search,
|
||
name="web_search", # Optional: defaults to "serper_dev_web_search"
|
||
description="Search the web for current information on any topic" # Optional: defaults to component docstring
|
||
)
|
||
|
||
## Agent Setup
|
||
agent = Agent(
|
||
chat_generator=OpenAIChatGenerator(),
|
||
tools=[search_tool],
|
||
exit_conditions=["text"]
|
||
)
|
||
|
||
## Run the Agent
|
||
agent.warm_up()
|
||
response = agent.run(messages=[ChatMessage.from_user("Find information about Nikola Tesla")])
|
||
|
||
## Output
|
||
print(response["messages"][-1].text)
|
||
```
|
||
|
||
## Additional References
|
||
|
||
🧑🍳 Cookbook: [Build a GitHub Issue Resolver Agent](https://haystack.deepset.ai/cookbook/github_issue_resolver_agent)
|
||
|
||
📓 Tutorial: [Build a Tool-Calling Agent](https://haystack.deepset.ai/tutorials/43_building_a_tool_calling_agent)
|