
Custom Tools and Prompts

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

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

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 agents can run in [~Agent.run] and [~Agent.chat] mode.
Both the run and chat modes underlie the same logic. The language model powering the agent is conditioned on a long 
prompt and completes the prompt by generating the next tokens until the stop token is reached.
The only difference between the two modes is that during the chat mode the prompt is extended with 
previous user inputs and model generations. This allows the agent to have access to past interactions,
seemingly giving the agent some kind of memory.
Structure of the prompt
Let's take a closer look at how the prompt is structured to understand how it can be best customized.
The prompt is structured broadly into four parts.

Introduction: how the agent should behave, explanation of the concept of tools.

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.

A set of examples of tasks and their solution

Current example, and request for solution.

To better understand each part, let's look at a shortened version of how the run prompt can look like:
````text
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 a 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 the 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 introduction (the text before "Tools:") explains precisely how the model shall behave and what it should do.
This part most likely does not need to be customized as the agent shall always behave the same way.
The second part (the bullet points below "Tools") is dynamically added upon calling run or chat. There are 
exactly as many bullet points as there are tools in agent.toolbox and each bullet point consists of the name 
and description of the tool:
text
- <tool.name>: <tool.description>
Let's verify this quickly by loading the document_qa tool and printing out the name and description.

from transformers import load_tool
document_qa = load_tool("document-question-answering")
print(f"- {document_qa.name}: {document_qa.description}")

which gives:
text
- document_qa: This is a tool that answers a question about a 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.
We can see that the tool name is short and precise. The description includes two parts, the first explaining 
what the tool does and the second states what input arguments and return values are expected.
A good tool name and tool description are very important for the agent to correctly use it. Note that the only
information the agent has about the tool is its name and description, so one should make sure that both 
are precisely written and match the style of the existing tools in the toolbox. In particular make sure the description
mentions all the arguments expected by name in code-style, along with the expected type and a description of what they
are.

Check the naming and description of the curated Transformers tools to better understand what name and 
description a tool is expected to have. You can see all tools with the [Agent.toolbox] property.

The third part includes a set of curated examples that show the agent exactly what code it should produce
for what kind of user request. The large language models empowering the agent are extremely good at 
recognizing patterns in a prompt and repeating the pattern with new data. Therefore, it is very important
that the examples are written in a way that maximizes the likelihood of the agent to generating correct,
executable code in practice. 
Let's have a look at one example:
```text
Task: "Identify the oldest person in thedocument` 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)
`
The pattern the model is prompted to repeat has three parts: The task statement, the agent's explanation of 
what it intends to do, and finally the generated code. Every example that is part of the prompt has this exact 
pattern, thus making sure that the agent will reproduce exactly the same pattern when generating new tokens.
The prompt examples are curated by the Transformers team and rigorously evaluated on a set of 
problem statements
to ensure that the agent's prompt is as good as possible to solve real use cases of the agent.
The final part of the prompt corresponds to:
```text
Task: "Draw me a picture of rivers and lakes"
I will use the following

is a final and unfinished example that the agent is tasked to complete. The unfinished example
is dynamically created based on the actual user input. For the above example, the user ran:
py
agent.run("Draw me a picture of rivers and lakes")
The user input - a.k.a the task: "Draw me a picture of rivers and lakes" is cast into the 
prompt template: "Task:  \n\n I will use the following". This sentence makes up the final lines of the 
prompt the agent is conditioned on, therefore strongly influencing the agent to finish the example 
exactly in the same way it was previously done in the examples.
Without going into too much detail, the chat template has the same prompt structure with the 
examples having a slightly different style, e.g.:
````text
[]
=====
Human: Answer the question in the variable question about the image stored in the variable image.
Assistant: I will use the tool image_qa to answer the question on the input image.
py
answer = image_qa(text=question, image=image)
print(f"The answer is {answer}")
Human: I tried this code, it worked but didn't give me a good result. The question is in French
Assistant: In this case, the question needs to be translated first. I will use the tool translator to do this.
py
translated_question = translator(question=question, src_lang="French", tgt_lang="English")
print(f"The translated question is {translated_question}.")
answer = image_qa(text=translated_question, image=image)
print(f"The answer is {answer}")
=====
[]
`
Contrary, to the examples of the run prompt, each chat prompt example has one or more exchanges between the 
Human and the Assistant. Every exchange is structured similarly to the example of the run prompt. 
The user's input is appended to behind Human: and the agent is prompted to first generate what needs to be done 
before generating code. An exchange can be based on previous exchanges, therefore allowing the user to refer
to past exchanges as is done e.g. above by the user's input of "I tried this code" refers to the 
previously generated code of the agent.
Upon running .chat, the user's input or task is cast into an unfinished example of the form:
text
Human: <user-input>\n\nAssistant:
which the agent completes. Contrary to the run command, the chat command then appends the completed example
to the prompt, thus giving the agent more context for the next chat turn.
Great now that we know how the prompt is structured, let's see how we can customize it!
Writing good user inputs
While large language models are getting better and better at understanding users' intentions, it helps 
enormously to be as precise as possible to help the agent pick the correct task. What does it mean to be 
as precise as possible?
The agent sees a list of tool names and their description in its prompt. The more tools are added the 
more difficult it becomes for the agent to choose the correct tool and it's even more difficult to choose
the correct sequences of tools to run. Let's look at a common failure case, here we will only return 
the code to analyze it.

from transformers import HfAgent
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
agent.run("Show me a tree", return_code=True)

gives:
``text
==Explanation from the agent==
I will use the following tool:image_segmenter` to create a segmentation mask for the image.
==Code generated by the agent==
mask = image_segmenter(image, prompt="tree")

which is probably not what we wanted. Instead, it is more likely that we want an image of a tree to be generated.
To steer the agent more towards using a specific tool it can therefore be very helpful to use important keywords that 
are present in the tool's name and description. Let's have a look.
py
agent.toolbox["image_generator"].description
text
'This is a tool that creates an image according to a prompt, which is a text description. It takes an input named `prompt` which contains the image description and outputs an image.
The name and description make use of the keywords "image", "prompt", "create" and "generate". Using these words will most likely work better here. Let's refine our prompt a bit.
py
agent.run("Create an image of a tree", return_code=True)
gives:
``text
==Explanation from the agent==
I will use the following toolimage_generator` to generate an image of a tree.
==Code generated by the agent==
image = image_generator(prompt="tree")

Much better! That looks more like what we want. In short, when you notice that the agent struggles to 
correctly map your task to the correct tools, try looking up the most pertinent keywords of the tool's name
and description and try refining your task request with it.
Customizing the tool descriptions
As we've seen before the agent has access to each of the tools' names and descriptions. The base tools 
should have very precise names and descriptions, however, you might find that it could help to change the 
the description or name of a tool for your specific use case. This might become especially important 
when you've added multiple tools that are very similar or if you want to use your agent only for a certain 
domain, e.g. image generation and transformations.
A common problem is that the agent confuses image generation with image transformation/modification when 
used a lot for image generation tasks, e.g.
py
agent.run("Make an image of a house and a car", return_code=True)
returns
``text
==Explanation from the agent== 
I will use the following toolsimage_generatorto generate an image of a house andimage_transformer` to transform the image of a car into the image of a house.
==Code generated by the agent==
house_image = image_generator(prompt="A house")
car_image = image_generator(prompt="A car")
house_car_image = image_transformer(image=car_image, prompt="A house")

which is probably not exactly what we want here. It seems like the agent has a difficult time 
to understand the difference between image_generator and image_transformer and often uses the two together.
We can help the agent here by changing the tool name and description of image_transformer. Let's instead call it modifier
to disassociate it a bit from "image" and "prompt":
py
agent.toolbox["modifier"] = agent.toolbox.pop("image_transformer")
agent.toolbox["modifier"].description = agent.toolbox["modifier"].description.replace(
    "transforms an image according to a prompt", "modifies an image"
)
Now "modify" is a strong cue to use the new image processor which should help with the above prompt. Let's run it again.
py
agent.run("Make an image of a house and a car", return_code=True)
Now we're getting:
``text
==Explanation from the agent==
I will use the following tools:image_generatorto generate an image of a house, thenimage_generator` to generate an image of a car.
==Code generated by the agent==
house_image = image_generator(prompt="A house")
car_image = image_generator(prompt="A car")

which is definitely closer to what we had in mind! However, we want to have both the house and car in the same image. Steering the task more toward single image generation should help:
py
agent.run("Create image: 'A house and car'", return_code=True)
``text
==Explanation from the agent==
I will use the following tool:image_generator` to generate an image.
==Code generated by the agent==
image = image_generator(prompt="A house and car")

Agents are still brittle for many use cases, especially when it comes to 
slightly more complex use cases like generating an image of multiple objects.
Both the agent itself and the underlying prompt will be further improved in the coming 
months making sure that agents become more robust to a variety of user inputs.

Customizing the whole prompt
To give the user maximum flexibility, the whole prompt template as explained in above
can be overwritten by the user. In this case make sure that your custom prompt includes an introduction section, 
a tool section, an example section, and an unfinished example section. If you want to overwrite the run prompt template, 
you can do as follows:

template = """ [] """
agent = HfAgent(your_endpoint, run_prompt_template=template)

Please make sure to have the <<all_tools>> string and the <<prompt>> defined somewhere in the template so that the agent can be aware 
of the tools, it has available to it as well as correctly insert the user's prompt.

Similarly, one can overwrite the chat prompt template. Note that the chat mode always uses the following format for the exchanges:
```text
Human: <>
Assistant:

Therefore it is important that the examples of the custom chat prompt template also make use of this format.
You can overwrite the chat template at instantiation as follows.

template = """ [] """
agent = HfAgent(url_endpoint=your_endpoint, chat_prompt_template=template)

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.

In both cases, you can pass a repo ID instead of the prompt template if you would like to use a template hosted by someone in the community. The default prompts live in this repo as an example.
To upload your custom prompt on a repo on the Hub and share it with the community just make sure:
- to use a dataset repository
- to put the prompt template for the run command in a file named run_prompt_template.txt
- to put the prompt template for the chat command in a file named chat_prompt_template.txt
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,
  with 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 replace the existing image-transformation tool.

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

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 
text
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 are accurate and fit the style of the 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:
text
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 an image_transformer tool which is hereby replaced with our custom tool.

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, or you should adapt the prompt template to make sure all examples using that
tool are updated.

The upscaler tool was given the name image_upscaler which is not yet present in the default toolbox and is therefore 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()]))
text
- 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.

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

 
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)
``text
==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")

 
The new image processing tool is based on ControlNet which 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)
``text
==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)

 
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 at 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:
thon
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:
thon
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:
thon
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:
thon
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("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 (change the user name for your tool):
thon
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:
thon
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:text
==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                                                                                                                                            |
|------------------------------------------------------------------------------------------------------------------------------------------------------|
|  |

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

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:
thon
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")

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.

Leveraging gradio-tools
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:
thon
from gradio_tools import StableDiffusionPromptGeneratorTool
gradio_tool = StableDiffusionPromptGeneratorTool()

We pass that instance to the Tool.from_gradio method:
thon
from transformers import Tool
tool = Tool.from_gradio(gradio_tool)

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:
thon
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:
``text
==Explanation from the agent==
I will use the following  tools:StableDiffusionPromptGeneratorto improve the prompt, thenimage_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:

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.

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 and share what you have in mind.