transformers/docs/source/en/custom_tools.mdx

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# Custom Tools and Prompts

<Tip>

If you are not aware of what tools and agents are in the context of transformers, we recommend you read the
[Transformers Agents](transformers_agents) page first.

</Tip>

<Tip warning={true}>

Transformers Agent is an experimental API which is subject to change at any time. Results returned by the agents
can vary as the APIs or underlying models are prone to change.

</Tip>

Creating and using custom tools and prompts is paramount to empowering the agent and having it perform new tasks.
In this guide we'll take a look at:

- How to customize the prompt
- How to use custom tools
- How to create custom tools

## Customizing the prompt

As explained in [Transformers Agents](transformers_agents) agents can run in [`~Agent.run`] and [`~Agent.chat`] mode.
Both the run and chat mode underlie the same logic. The language model powering the agent is conditioned on a long prompt 
and simply asked to complete the prompt by generating next tokens until the stop token is reached.
The only difference between the `run` and `chat` mode is that during the `chat` mode the prompt is extended with 
previous user inputs and model generations, which seemingly gives the agent a memory and allows it to refer to 
past interactions.

Let's take a closer look into how the prompt is structured to understand how it can be best customized.
The prompt is structured broadly into four parts.

- 1. Introduction: how the agent should behave, explanation of the concept of tools.
- 2. Description of all the tools. This is defined by a `<<all_tools>>` token that is dynamically replaced at runtime with the tools defined/chosen by the user.
- 3. A set of examples of tasks and their solution
- 4. Current example, and request for solution.

To better understand each part, let's look at a shortened version of how such a prompt can look like in practice.

```
I will ask you to perform a task, your job is to come up with a series of simple commands in Python that will perform the task.
[...]
You can print intermediate results if it makes sense to do so.

Tools:
- document_qa: This is a tool that answers a question about an document (pdf). It takes an input named `document` which should be the document containing the information, as well as a `question` that is the question about the document. It returns a text that contains the answer to the question.
- image_captioner: This is a tool that generates a description of an image. It takes an input named `image` which should be the image to caption, and returns a text that contains the description in English.
[...]

Task: "Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French."

I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.

Answer:
```py
translated_question = translator(question=question, src_lang="French", tgt_lang="English")
print(f"The translated question is {translated_question}.")
answer = image_qa(image=image, question=translated_question)
print(f"The answer is {answer}")
```

Task: "Identify the oldest person in the `document` and create an image showcasing the result as a banner."

I will use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.

Answer:
```py
answer = document_qa(document, question="What is the oldest person?")
print(f"The answer is {answer}.")
image = image_generator("A banner showing " + answer)
```

[...]

Task: "Draw me a picture of rivers and lakes"

I will use the following
```

The first part explains precisely how the model shall behave and what it should do. This part 
most likely does not need to be customized.

TODO(PVP) - explain better how the .description and .name influence the prompt

### Customizing the tool descriptions

The performance of the agent is directly linked to the prompt itself. We structure the prompt so that it works well 
with what we intend for the agent to do; but for maximum customization we also offer the ability to specify a different prompt when instantiating the agent.

### Customizing the single-execution prompt

In order to specify a custom single-execution prompt, one would so the following:

```py
template = """ [...] """

agent = HfAgent(your_endpoint, run_prompt_template=template)
```

<Tip>

Please make sure to have the `<<all_tools>>` string defined somewhere in the `template` so that the agent can be aware 
of the tools it has available to it.

</Tip>

#### Chat-execution prompt

In order to specify a custom single-execution prompt, one would so the following:

```
template = """ [...] """

agent = HfAgent(
	url_endpoint=your_endpoint,
	token=your_hf_token,
	chat_prompt_template=template
)
```

<Tip>

Please make sure to have the `<<all_tools>>` string defined somewhere in the `template` so that the agent can be 
aware of the tools it has available to it.

</Tip>

## Using custom tools

In this section, we'll be leveraging two existing custom tools that are specific to image generation:

- We replace [huggingface-tools/image-transformation](https://huggingface.co/spaces/huggingface-tools/image-transformation),
  with [diffusers/controlnet-canny-tool](https://huggingface.co/spaces/diffusers/controlnet-canny-tool) 
  to allow for more image modifications.
- We add a new tool for image upscaling to the default toolbox: 
  [diffusers/latent-upscaler-tool](https://huggingface.co/spaces/diffusers/latent-upscaler-tool) replace the existing image-transformation tool.

We'll start by loading the custom tools with the convenient [`load_tool`] function:

```py
from transformers import load_tool

controlnet_transformer = load_tool("diffusers/controlnet-canny-tool")
upscaler = load_tool("diffusers/latent-upscaler-tool")
```

Upon adding custom tools to an agent, the tools' descriptions and names are automatically
included in the agents' prompts. Thus, it is imperative that custom tools have
a well-written description and name in order for the agent to understand how to use them.
Let's take a look at the description and name of `controlnet_transformer`:

```py
print(f"Description: '{controlnet_transformer.description}'")
print(f"Name: '{controlnet_transformer.name}'")
```

gives 
```
Description: 'This is a tool that transforms an image with ControlNet according to a prompt. 
It takes two inputs: `image`, which should be the image to transform, and `prompt`, which should be the prompt to use to change it. It returns the modified image.'
Name: 'image_transformer'
```

The name and description is accurate and fits the style of the [curated set of tools](./transformers_agents#a-curated-set-of-tools).
Next, let's instantiate an agent with `controlnet_transformer` and `upscaler`:

```py
tools = [controlnet_transformer, upscaler]
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=tools)
```

This command should give you the following info:

```
image_transformer has been replaced by <transformers_modules.diffusers.controlnet-canny-tool.bd76182c7777eba9612fc03c0
8718a60c0aa6312.image_transformation.ControlNetTransformationTool object at 0x7f1d3bfa3a00> as provided in `additional_tools`
```

The set of curated tools already has a `image_transformer` tool which is hereby replaced with our custom tool.

<Tip>

Overwriting existing tools can be beneficial if we want to use a custom tool exactly for the same task as an existing tool 
because the agent is well-versed in using the specific task. Beware that the custom tool should follow the exact same API 
as the overwritten tool in this case.

</Tip>

The upscaler tool was given the name `image_upscaler` which is not yet present in the default toolbox and is therefore is simply added to the list of tools.
You can always have a look at the toolbox that is currently available to the agent via the `agent.toolbox` attribute:

```py
print("\n".join([f"- {a}" for a in agent.toolbox.keys()]))
```

```
- document_qa
- image_captioner
- image_qa
- image_segmenter
- transcriber
- summarizer
- text_classifier
- text_qa
- text_reader
- translator
- image_transformer
- text_downloader
- image_generator
- video_generator
- image_upscaler
```

Note how `image_upscaler` is now part of the agents' toolbox.

Let's now try out the new tools! We will re-use the image we generated in (Transformers Agents Quickstart)[./transformers_agents#single-execution-run].

```py
from diffusers.utils import load_image

image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png"
)
```

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png" width=200> 

Let's transform the image into a beautiful winter landscape:

```py
image = agent.run("Transform the image: 'A frozen lake and snowy forest'", image=image)
```

```
==Explanation from the agent==
I will use the following  tool: `image_transformer` to transform the image.


==Code generated by the agent==
image = image_transformer(image, prompt="A frozen lake and snowy forest")
```

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes_winter.png" width=200> 

The new image processing tool is based on ControlNet which is can make very strong modifications to the image.
By default the image processing tool returns an image of size 512x512 pixels. Let's see if we can upscale it.

```py
image = agent.run("Upscale the image", image)
```

```
==Explanation from the agent==
I will use the following  tool: `image_upscaler` to upscale the image.


==Code generated by the agent==
upscaled_image = image_upscaler(image)
```

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes_winter_upscale.png" width=400> 

The agent automatically mapped our prompt "Upscale the image" to the just added upscaler tool purely based on the description and name of the upscaler tool 
and was able to correctly run it.

Next, let's have a look into how you can create a new custom tool.

### Adding new tools

In this section we show how to create a new tool that can be added to the agent.

#### Creating a new tool

We'll first start by creating a tool. We'll add the not-so-useful yet fun task of fetching the model on the Hugging Face
Hub with the most downloads for a given task.

We can do that with the following code:

```python
from huggingface_hub import list_models

task = "text-classification"

model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
print(model.id)
```

For the task `text-classification`, this returns `'facebook/bart-large-mnli'`, for `translation` it returns `'t5-base`.

How do we convert this to a tool that the agent can leverage? All tools depend on the superclass `Tool` that holds the
main attributes necessary. We'll create a class that inherits from it:

```python
from transformers import Tool


class HFModelDownloadsTool(Tool):
    pass
```

This class has a few needs:
- An attribute `name`, which corresponds to the name of the tool itself. To be in tune with other tools which have a
  performative name, we'll name it `model_download_counter`.
- An attribute `description`, which will be used to populate the prompt of the agent.
- `inputs` and `outputs` attributes. Defining this will help the python interpreter make educated choices about types,
  and will allow for a gradio-demo to be spawned when we push our tool to the Hub. They're both a list of expected
  values, which can be `text`, `image`, or `audio`.
- A `__call__` method which contains the inference code. This is the code we've played with above!

Here's what our class looks like now:

```python
from transformers import Tool
from huggingface_hub import list_models


class HFModelDownloadsTool(Tool):
    name = "model_download_counter"
    description = (
        "This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. "
        "It takes the name of the category (such as text-classification, depth-estimation, etc), and "
        "returns the name of the checkpoint."
    )

    inputs = ["text"]
    outputs = ["text"]

    def __call__(self, task: str):
        model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
        return model.id
```

We now have our tool handy. Save it in a file and import it from your main script. Let's name this file
`model_downloads.py`, so the resulting import code looks like this:

```python
from model_downloads import HFModelDownloadsTool

tool = HFModelDownloadsTool()
```

In order to let others benefit from it and for simpler initialization, we recommend pushing it to the Hub under your 
namespace. To do so, just call `push_to_hub` on the `tool` variable:

```python
tool.push_to_hub("lysandre/hf-model-downloads")
```

You now have your code on the Hub! Let's take a look at the final step, which is to have the agent use it.

#### Having the agent use the tool

We now have our tool that lives on the Hub which can be instantiated as such:

```python
from transformers import load_tool

tool = load_tool("lysandre/hf-model-downloads")
```

In order to use it in the agent, simply pass it in the `additional_tools` parameter of the agent initialization method:

```python
from transformers import HfAgent

agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=[tool])

agent.run(
    "Can you read out loud the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?"
)
```
which outputs the following:
```
==Code generated by the agent==
model = model_download_counter(task="text-to-video")
print(f"The model with the most downloads is {model}.")
audio_model = text_reader(model)


==Result==
The model with the most downloads is damo-vilab/text-to-video-ms-1.7b.
```

and generates the following audio.

| **Audio**                                                                                                                                            |
|------------------------------------------------------------------------------------------------------------------------------------------------------|
| <audio controls><source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/damo.wav" type="audio/wav"/> |


<Tip>

Depending on the LLM, some are quite brittle and require very exact prompts in order to work well. Having a well-defined
description of the tool is paramount to having it be leveraged by the agent.

</Tip>

### Replacing existing tools

Replacing existing tools can be done simply by assigning a new item to the agent's toolbox. Here's how one would do so:

```python
from transformers import HfAgent, load_tool

agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
agent.toolbox["image-transformation"] = load_tool("diffusers/controlnet-canny-tool")
```

<Tip>

Beware when replacing tools with others! This will also adjust the agent's prompt. This can be good if you have a better
prompt suited for the task, but it can also result in your tool being selected way more than others or for other
tools to be selected instead of the one you have defined.

</Tip>

## Leveraging gradio-tools

[gradio-tools](https://github.com/freddyaboulton/gradio-tools) is a powerful library that allows using Hugging
Face Spaces as tools. It supports many existing Spaces as well as custom Spaces to be designed with it.

We offer support for `gradio_tools` by using the `Tool.from_gradio` method. For example, we want to take
advantage of the `StableDiffusionPromptGeneratorTool` tool offered in the `gradio-tools` toolkit so as to
improve our prompts and generate better images.

We first import the tool from `gradio_tools` and instantiate it:

```python
from gradio_tools import StableDiffusionPromptGeneratorTool

gradio_tool = StableDiffusionPromptGeneratorTool()
```

We pass that instance to the `Tool.from_gradio` method:

```python
from transformers import Tool

tool = Tool.from_gradio(gradio_tools)
```

Now we can manage it exactly as we would a usual custom tool. We leverage it to improve our prompt
` a rabbit wearing a space suit`:

```python
from transformers import HfAgent

agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder", additional_tools=[tool])

agent.run("Generate an image of the `prompt` after improving it.", prompt="A rabbit wearing a space suit")
```

The model adequately leverages the tool:
```
==Explanation from the agent==
I will use the following  tools: `StableDiffusionPromptGenerator` to improve the prompt, then `image_generator` to generate an image according to the improved prompt.


==Code generated by the agent==
improved_prompt = StableDiffusionPromptGenerator(prompt)
print(f"The improved prompt is {improved_prompt}.")
image = image_generator(improved_prompt)
```

Before finally generating the image:

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png">

<Tip warning={true}>

gradio-tools requires *textual* inputs and outputs, even when working with different modalities. This implementation
works with image and audio objects. The two are currently incompatible, but will rapidly become compatible as we
work to improve the support.

</Tip>

## Future compatibility with Langchain

We love Langchain and think it has a very compelling suite of tools. In order to handle these tools,
Langchain requires *textual* inputs and outputs, even when working with different modalities.
This is often the serialized version (i.e., saved to disk) of the objects.

This difference means that multi-modality isn't handled between transformers-agents and langchain.
We aim for this limitation to be resolved in future versions, and welcome any help from avid langchain
users to help us achieve this compatibility.

We would love to have better support. If you would like to help, please 
[open an issue](https://github.com/huggingface/transformers/issues/new) and share what you have in mind.