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Add new documentation page for advanced agent usage (#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() | ||
| ``` |