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Add new documentation page for advanced agent usage (huggingface#33265)
* Add new documentation page for advanced agent usage
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<!--Copyright 2024 The HuggingFace Team. All rights reserved. | ||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||
the License. You may obtain a copy of the License at | ||
http://www.apache.org/licenses/LICENSE-2.0 | ||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||
specific language governing permissions and limitations under the License. | ||
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be | ||
rendered properly in your Markdown viewer. | ||
--> | ||
# Agents, supercharged - Multi-agents, External tools, and more | ||
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[[open-in-colab]] | ||
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### What is an agent? | ||
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> [!TIP] | ||
> If you're new to `transformers.agents`, make sure to first read the main [agents documentation](./agents). | ||
In this page we're going to highlight several advanced uses of `transformers.agents`. | ||
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## Multi-agents | ||
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Multi-agent has been introduced in Microsoft's framework [Autogen](https://huggingface.co/papers/2308.08155). | ||
It simply means having several agents working together to solve your task instead of only one. | ||
It empirically yields better performance on most benchmarks. The reason for this better performance is conceptually simple: for many tasks, rather than using a do-it-all system, you would prefer to specialize units on sub-tasks. Here, having agents with separate tool sets and memories allows to achieve efficient specialization. | ||
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You can easily build hierarchical multi-agent systems with `transformers.agents`. | ||
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To do so, encapsulate the agent in a [`ManagedAgent`] object. This object needs arguments `agent`, `name`, and a `description`, which will then be embedded in the manager agent's system prompt to let it know how to call this managed agent, as we also do for tools. | ||
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Here's an example of making an agent that managed a specitif web search agent using our [`DuckDuckGoSearchTool`]: | ||
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```py | ||
from transformers.agents import ReactCodeAgent, HfApiEngine, DuckDuckGoSearchTool, ManagedAgent | ||
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llm_engine = HfApiEngine() | ||
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web_agent = ReactCodeAgent(tools=[DuckDuckGoSearchTool()], llm_engine=llm_engine) | ||
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managed_web_agent = ManagedAgent( | ||
agent=web_agent, | ||
name="web_search", | ||
description="Runs web searches for you. Give it your query as an argument." | ||
) | ||
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manager_agent = ReactCodeAgent( | ||
tools=[], llm_engine=llm_engine, managed_agents=[managed_web_agent] | ||
) | ||
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manager_agent.run("Who is the CEO of Hugging Face?") | ||
``` | ||
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> [!TIP] | ||
> For an in-depth example of an efficient multi-agent implementation, see [how we pushed our multi-agent system to the top of the GAIA leaderboard](https://huggingface.co/blog/beating-gaia). | ||
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## Use tools from gradio or LangChain | ||
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### Use gradio-tools | ||
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[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. | ||
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Transformers supports `gradio_tools` with the [`Tool.from_gradio`] method. For example, let's use the [`StableDiffusionPromptGeneratorTool`](https://github.com/freddyaboulton/gradio-tools/blob/main/gradio_tools/tools/prompt_generator.py) from `gradio-tools` toolkit for improving prompts to generate better images. | ||
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Import and instantiate the tool, then pass it to the `Tool.from_gradio` method: | ||
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```python | ||
from gradio_tools import StableDiffusionPromptGeneratorTool | ||
from transformers import Tool, load_tool, CodeAgent | ||
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gradio_prompt_generator_tool = StableDiffusionPromptGeneratorTool() | ||
prompt_generator_tool = Tool.from_gradio(gradio_prompt_generator_tool) | ||
``` | ||
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Now you can use it just like any other tool. For example, let's improve the prompt `a rabbit wearing a space suit`. | ||
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```python | ||
image_generation_tool = load_tool('huggingface-tools/text-to-image') | ||
agent = CodeAgent(tools=[prompt_generator_tool, image_generation_tool], llm_engine=llm_engine) | ||
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agent.run( | ||
"Improve this prompt, then generate an image of it.", prompt='A rabbit wearing a space suit' | ||
) | ||
``` | ||
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The model adequately leverages the tool: | ||
```text | ||
======== New task ======== | ||
Improve this prompt, then generate an image of it. | ||
You have been provided with these initial arguments: {'prompt': 'A rabbit wearing a space suit'}. | ||
==== Agent is executing the code below: | ||
improved_prompt = StableDiffusionPromptGenerator(query=prompt) | ||
while improved_prompt == "QUEUE_FULL": | ||
improved_prompt = StableDiffusionPromptGenerator(query=prompt) | ||
print(f"The improved prompt is {improved_prompt}.") | ||
image = image_generator(prompt=improved_prompt) | ||
==== | ||
``` | ||
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Before finally generating the image: | ||
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png"> | ||
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> [!WARNING] | ||
> gradio-tools require *textual* inputs and outputs even when working with different modalities like image and audio objects. Image and audio inputs and outputs are currently incompatible. | ||
### Use LangChain tools | ||
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We love Langchain and think it has a very compelling suite of tools. | ||
To import a tool from LangChain, use the `from_langchain()` method. | ||
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Here is how you can use it to recreate the intro's search result using a LangChain web search tool. | ||
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```python | ||
from langchain.agents import load_tools | ||
from transformers import Tool, ReactCodeAgent | ||
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search_tool = Tool.from_langchain(load_tools(["serpapi"])[0]) | ||
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agent = ReactCodeAgent(tools=[search_tool]) | ||
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agent.run("How many more blocks (also denoted as layers) in BERT base encoder than the encoder from the architecture proposed in Attention is All You Need?") | ||
``` | ||
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## Display your agent run in a cool Gradio interface | ||
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You can leverage `gradio.Chatbot`to display your agent's thoughts using `stream_to_gradio`, here is an example: | ||
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```py | ||
import gradio as gr | ||
from transformers import ( | ||
load_tool, | ||
ReactCodeAgent, | ||
HfApiEngine, | ||
stream_to_gradio, | ||
) | ||
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# Import tool from Hub | ||
image_generation_tool = load_tool("m-ric/text-to-image") | ||
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llm_engine = HfApiEngine("meta-llama/Meta-Llama-3-70B-Instruct") | ||
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# Initialize the agent with the image generation tool | ||
agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine) | ||
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def interact_with_agent(task): | ||
messages = [] | ||
messages.append(gr.ChatMessage(role="user", content=task)) | ||
yield messages | ||
for msg in stream_to_gradio(agent, task): | ||
messages.append(msg) | ||
yield messages + [ | ||
gr.ChatMessage(role="assistant", content="⏳ Task not finished yet!") | ||
] | ||
yield messages | ||
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with gr.Blocks() as demo: | ||
text_input = gr.Textbox(lines=1, label="Chat Message", value="Make me a picture of the Statue of Liberty.") | ||
submit = gr.Button("Run illustrator agent!") | ||
chatbot = gr.Chatbot( | ||
label="Agent", | ||
type="messages", | ||
avatar_images=( | ||
None, | ||
"https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png", | ||
), | ||
) | ||
submit.click(interact_with_agent, [text_input], [chatbot]) | ||
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if __name__ == "__main__": | ||
demo.launch() | ||
``` |