Custom Tools and Prompts

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 run() and 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.

• 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 the run prompt can look like:

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:

- <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:

- 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.

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:

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)
```

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:

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:

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: <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.:

[...]

=====

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:

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:

==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.

agent.toolbox["image_generator"].description

'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.

agent.run("Create an image of a tree", return_code=True)

gives:

==Explanation from the agent==
I will use the following tool `image_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.

agent.run("Make an image of a house and a car", return_code=True)

returns

==Explanation from the agent== 
I will use the following tools `image_generator` to generate an image of a house and `image_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”:

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.

agent.run("Make an image of a house and a car", return_code=True)

Now we’re getting:

==Explanation from the agent==
I will use the following tools: `image_generator` to generate an image of a house, then `image_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:

agent.run("Create image: 'A house and car'", return_code=True)

==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")

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)

Similarly, one can overwrite the chat prompt template. Note that the chat mode always uses the following format for the exchanges:

Human: <<task>>

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)

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:

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 are accurate and fit the style of the curated set of tools. Next, let’s instantiate an agent with controlnet_transformer and upscaler:

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 an image_transformer tool which is hereby replaced with our custom tool.

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:

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.

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:

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")

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.

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)

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:

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:

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:

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:

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:

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):

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:

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.

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:

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")

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:

from gradio_tools import StableDiffusionPromptGeneratorTool

gradio_tool = StableDiffusionPromptGeneratorTool()

We pass that instance to the Tool.from_gradio method:

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:

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:

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.