[agents] remove agents 🧹 (#37368)
This commit is contained in:
Joao Gante
@@ -23,10 +23,6 @@
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title: 使用🤗 PEFT加载和训练adapters
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- local: model_sharing
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title: 分享您的模型
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- local: agents
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title: 智能体和工具
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- local: agents_advanced
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title: 智能体，超强版 - 多智能体、外部工具等
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- local: llm_tutorial
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title: 使用LLMs进行生成
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title: 教程
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@@ -105,8 +101,6 @@
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title: 概念指南
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- sections:
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- sections:
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- local: main_classes/agent
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title: 智能体和工具
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- local: main_classes/callback
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title: Callbacks
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- local: main_classes/configuration
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@@ -1,427 +0,0 @@
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<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# 智能体和工具
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[[在colab里打开]]
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### 什么是智能体 (Agent)？
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大型语言模型（LLM）经过 [因果语言建模训练](./tasks/language_modeling) 可以应对各种任务，但在一些基本任务（如逻辑推理、计算和搜索）上常常表现不佳。当它们被用在自己不擅长的领域时，往往无法生成我们期望的答案。
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为了解决这个问题，可以创建**智能体**.
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智能体是一个系统，它使用 LLM 作为引擎，并且能够访问称为**工具**的功能。
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这些**工具**是执行任务的函数，包含所有必要的描述信息，帮助智能体正确使用它们。
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智能体可以被编程为：
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- 一次性设计一系列工具并同时执行它们，像 [`CodeAgent`]
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- 一次执行一个工具，并等待每个工具的结果后再启动下一个，像 [`ReactJsonAgent`]
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### 智能体类型
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#### 代码智能体
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此智能体包含一个规划步骤，然后生成 Python 代码一次性执行所有任务。它原生支持处理不同输入和输出类型，因此推荐用于多模态任务。
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#### 推理智能体
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这是解决推理任务的首选代理，因为 ReAct 框架 ([Yao et al., 2022](https://huggingface.co/papers/2210.03629)) 使其在基于之前观察进行推理时非常高效。
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我们实现了两种版本的 ReactJsonAgent：
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- [`ReactJsonAgent`] 将工具调用作为 JSON 格式输出。
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- [`ReactCodeAgent`] 是 ReactJsonAgent 的一种新型，生成工具调用的代码块，对于具备强大编程能力的 LLM 非常适用。
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> [TIP]
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> 阅读 [Open-source LLMs as LangChain Agents](https://huggingface.co/blog/open-source-llms-as-agents) 博文，了解更多关于推理智能体的信息。
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<div class="flex justify-center">
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<img
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class="block dark:hidden"
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Agent_ManimCE.gif"
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/>
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<img
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class="hidden dark:block"
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src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Agent_ManimCE.gif"
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/>
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</div>
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以下是一个推理代码智能体如何处理以下问题的示例：
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```py3
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>>> agent.run(
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... "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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... )
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=====New task=====
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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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====Agent is executing the code below:
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bert_blocks = search(query="number of blocks in BERT base encoder")
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print("BERT blocks:", bert_blocks)
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====
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Print outputs:
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BERT blocks: twelve encoder blocks
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====Agent is executing the code below:
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attention_layer = search(query="number of layers in Attention is All You Need")
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print("Attention layers:", attention_layer)
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====
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Print outputs:
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Attention layers: Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position- 2 Page 3 Figure 1: The Transformer - model architecture.
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====Agent is executing the code below:
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bert_blocks = 12
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attention_layers = 6
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diff = bert_blocks - attention_layers
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print("Difference in blocks:", diff)
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final_answer(diff)
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====
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Print outputs:
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Difference in blocks: 6
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Final answer: 6
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```
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### 如何构建智能体？
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要初始化一个智能体，您需要以下参数：
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- **一个 LLM** 来驱动智能体——智能体本身并不是 LLM，而是一个使用 LLM 作为引擎的程序。
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- **一个系统提示**：告诉 LLM 引擎应该如何生成输出。
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- **一个工具箱**，智能体可以从中选择工具执行。
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- **一个解析器**，从 LLM 输出中提取出哪些工具需要调用，以及使用哪些参数。
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在智能体系统初始化时，工具属性将生成工具描述，并嵌入到智能体的系统提示中，告知智能体可以使用哪些工具，并且为什么使用它们。
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**安装依赖**
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首先，您需要安装**智能体**所需的额外依赖：
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```bash
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pip install transformers[agents]
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```
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**创建LLM引擎**
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定义一个 `llm_engine` 方法，该方法接受一系列[消息](./chat_templating)并返回文本。该 `callable` 还需要接受一个 `stop` 参数，用于指示何时停止生成输出。
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```python
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from huggingface_hub import login, InferenceClient
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login("<YOUR_HUGGINGFACEHUB_API_TOKEN>")
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client = InferenceClient(model="meta-llama/Meta-Llama-3-70B-Instruct")
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def llm_engine(messages, stop_sequences=["Task"]) -> str:
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response = client.chat_completion(messages, stop=stop_sequences, max_tokens=1000)
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answer = response.choices[0].message.content
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return answer
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```
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您可以使用任何符合以下要求的 `llm_engine` 方法：
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1. [输入格式](./chat_templating)为 (`List[Dict[str, str]]`)，并且返回一个字符串。
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2. 它在 `stop_sequences` 参数传递的序列处停止生成输出。
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此外，`llm_engine` 还可以接受一个 `grammar` 参数。如果在智能体初始化时指定了 `grammar`，则该参数将传递给 `llm_engine` 的调用，以允许[受限生成](https://huggingface.co/docs/text-generation-inference/conceptual/guidance)，以强制生成格式正确的智能体输出。
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您还需要一个 `tools` 参数，它接受一个 `Tools` 列表 —— 可以是空列表。您也可以通过定义可选参数 `add_base_tools=True` 来将默认工具箱添加到工具列表中。
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现在，您可以创建一个智能体，例如 [`CodeAgent`]，并运行它。您还可以创建一个 [`TransformersEngine`]，使用 `transformers` 在本地机器上运行预初始化的推理管道。 为了方便起见，由于智能体行为通常需要更强大的模型，例如 `Llama-3.1-70B-Instruct`，它们目前较难在本地运行，我们还提供了 [`HfApiEngine`] 类，它在底层初始化了一个 `huggingface_hub.InferenceClient`。
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```python
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from transformers import CodeAgent, HfApiEngine
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llm_engine = HfApiEngine(model="meta-llama/Meta-Llama-3-70B-Instruct")
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agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True)
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agent.run(
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"Could you translate this sentence from French, say it out loud and return the audio.",
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sentence="Où est la boulangerie la plus proche?",
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)
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```
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当你急需某个东西时这将会很有用!
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您甚至可以将 `llm_engine` 参数留空，默认情况下会创建一个 [`HfApiEngine`]。
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```python
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from transformers import CodeAgent
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agent = CodeAgent(tools=[], add_base_tools=True)
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agent.run(
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"Could you translate this sentence from French, say it out loud and give me the audio.",
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sentence="Où est la boulangerie la plus proche?",
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)
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```
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请注意，我们使用了额外的 `sentence` 参数：您可以将文本作为附加参数传递给模型。
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您还可以使用这个来指定本地或远程文件的路径供模型使用：
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```py
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from transformers import ReactCodeAgent
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agent = ReactCodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True)
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agent.run("Why does Mike not know many people in New York?", audio="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/recording.mp3")
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```
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系统提示和输出解析器会自动定义，但您可以通过调用智能体的 `system_prompt_template` 来轻松查看它们。
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```python
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print(agent.system_prompt_template)
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```
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尽可能清楚地解释您要执行的任务非常重要。 每次 [`~Agent.run`] 操作都是独立的，并且由于智能体是由 LLM 驱动的，提示中的细微变化可能会导致完全不同的结果。
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您还可以连续运行多个任务，每次都会重新初始化智能体的 `agent.task` 和 `agent.logs` 属性。
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#### 代码执行
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Python 解释器在一组输入和工具上执行代码。 这应该是安全的，因为只能调用您提供的工具（特别是 Hugging Face 的工具）和 print 函数，因此您已经限制了可以执行的操作。
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Python 解释器默认不允许导入不在安全列表中的模块，因此大多数明显的攻击问题应该不成问题。 您仍然可以通过在 [`ReactCodeAgent`] 或 [`CodeAgent`] 初始化时通过 `additional_authorized_imports` 参数传递一个授权的模块列表来授权额外的导入：
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```py
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>>> from transformers import ReactCodeAgent
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>>> agent = ReactCodeAgent(tools=[], additional_authorized_imports=['requests', 'bs4'])
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>>> agent.run("Could you get me the title of the page at url 'https://huggingface.co/blog'?")
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(...)
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'Hugging Face – Blog'
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```
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如果有任何代码尝试执行非法操作，或者生成的代码出现常规 Python 错误，执行将停止。
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> [!WARNING]
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> 在使用大语言模型（LLM）生成代码时，生成的代码会被执行，避免导入或使用任何不安全的库或模块。
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### 系统提示
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智能体，或者说驱动智能体的 LLM，根据系统提示生成输出。系统提示可以定制并根据目标任务进行调整。例如，检查 [`ReactCodeAgent`] 的系统提示（以下版本经过简化）。
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```text
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You will be given a task to solve as best you can.
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You have access to the following tools:
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<<tool_descriptions>>
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To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences.
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At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task, then the tools that you want to use.
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Then in the 'Code:' sequence, you should write the code in simple Python. The code sequence must end with '/End code' sequence.
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During each intermediate step, you can use 'print()' to save whatever important information you will then need.
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These print outputs will then be available in the 'Observation:' field, for using this information as input for the next step.
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In the end you have to return a final answer using the `final_answer` tool.
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Here are a few examples using notional tools:
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---
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{examples}
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Above example were using notional tools that might not exist for you. You only have access to those tools:
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<<tool_names>>
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You also can perform computations in the python code you generate.
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Always provide a 'Thought:' and a 'Code:\n```py' sequence ending with '```<end_code>' sequence. You MUST provide at least the 'Code:' sequence to move forward.
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Remember to not perform too many operations in a single code block! You should split the task into intermediate code blocks.
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Print results at the end of each step to save the intermediate results. Then use final_answer() to return the final result.
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Remember to make sure that variables you use are all defined.
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Now Begin!
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```
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系统提示包括：
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- 解释智能体应该如何工作以及工具的**介绍**。
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- 所有工具的描述由 `<<tool_descriptions>>` 标记在运行时动态替换，这样智能体就知道可以使用哪些工具及其用途。
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- 工具的描述来自工具的属性,`name`、`description`、`inputs` 和 `output_type`，以及一个简单的 `jinja2` 模板，您可以根据需要进行调整。
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- 期望的输出格式。
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您可以通过向 `system_prompt` 参数传递自定义提示来最大程度地提高灵活性，从而覆盖整个系统提示模板。
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```python
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from transformers import ReactJsonAgent
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from transformers.agents import PythonInterpreterTool
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agent = ReactJsonAgent(tools=[PythonInterpreterTool()], system_prompt="{your_custom_prompt}")
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```
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> [WARNING]
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> 必须在`template`中定义 `<<tool_descriptions>>` 这个变量，以便智能体能够正确地识别并使用可用的工具
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### 检查智能体的运行
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以下是检查运行后发生了什么的一些有用属性：
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- `agent.logs` 存储了智能体的详细日志。每一步的所有内容都会存储在一个字典中，然后附加到 `agent.logs`。
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- 运行 `agent.write_inner_memory_from_logs()` 会从日志中创建智能体的内存，以便 LLM 查看，作为一系列聊天消息。此方法会遍历日志的每个步骤，只保存其感兴趣的消息：例如，它会单独保存系统提示和任务，然后为每个步骤保存 LLM 输出的消息，以及工具调用输出的消息。如果您想要更高层次的查看发生了什么，可以使用此方法 —— 但并不是每个日志都会被此方法转录。
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## 工具
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工具是智能体使用的基本功能。
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例如，您可以检查 [`PythonInterpreterTool`]：它有一个名称、描述、输入描述、输出类型和 `__call__` 方法来执行该操作。
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当智能体初始化时，工具属性会用来生成工具描述，然后将其嵌入到智能体的系统提示中，这让智能体知道可以使用哪些工具以及为什么使用它们。
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### 默认工具箱
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Transformers 提供了一个默认工具箱，用于增强智能体，您可以在初始化时通过 `add_base_tools=True` 参数将其添加到智能体中：
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- **文档问答**：给定一个文档（如图像格式的 PDF），回答关于该文档的问题([Donut](./model_doc/donut))
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- **图像问答**：给定一张图片，回答关于该图像的问题([VILT](./model_doc/vilt))
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- **语音转文本**：给定一个人讲述的音频录音，将其转录为文本（Whisper）
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- **文本转语音**：将文本转换为语音([SpeechT5](./model_doc/speecht5))
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- **翻译**：将给定的句子从源语言翻译为目标语言
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- **DuckDuckGo 搜索**：使用 `DuckDuckGo` 浏览器进行网络搜索
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- **Python 代码解释器**：在安全环境中运行 LLM 生成的 Python 代码。只有在初始化 [`ReactJsonAgent`] 时将 `add_base_tools=True` 时，代码智能体才会添加此工具，因为基于代码的智能体已经能够原生执行 Python 代码
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您可以通过调用 [`load_tool`] 函数来手动使用某个工具并执行任务。
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|
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```python
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from transformers import load_tool
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tool = load_tool("text-to-speech")
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audio = tool("This is a text to speech tool")
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```
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### 创建新工具
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||||
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您可以为 `Hugging Face` 默认工具无法涵盖的用例创建自己的工具。
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例如，假设我们要创建一个返回在 `Hugging Face Hub` 上某个任务中下载次数最多的模型的工具。
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您将从以下代码开始：
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```python
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from huggingface_hub import list_models
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task = "text-classification"
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model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
|
||||
print(model.id)
|
||||
```
|
||||
|
||||
这段代码可以很快转换为工具，只需将其包装成一个函数，并添加 `tool` 装饰器：
|
||||
|
||||
|
||||
```py
|
||||
from transformers import tool
|
||||
|
||||
@tool
|
||||
def model_download_tool(task: str) -> str:
|
||||
"""
|
||||
This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub.
|
||||
It returns the name of the checkpoint.
|
||||
|
||||
Args:
|
||||
task: The task for which
|
||||
"""
|
||||
model = next(iter(list_models(filter="text-classification", sort="downloads", direction=-1)))
|
||||
return model.id
|
||||
```
|
||||
|
||||
该函数需要：
|
||||
- 一个清晰的名称。名称通常描述工具的功能。由于代码返回某个任务中下载次数最多的模型，因此我们将其命名为 `model_download_tool`。
|
||||
- 对输入和输出进行类型提示
|
||||
- 描述，其中包括 "`Args`:" 部分，描述每个参数（这次不需要类型指示，它会从类型提示中获取）。
|
||||
|
||||
所有这些将自动嵌入到智能体的系统提示中，因此请尽量使它们尽可能清晰！
|
||||
|
||||
> [TIP]
|
||||
> 这个定义格式与 apply_chat_template 中使用的工具模式相同，唯一的区别是添加了 tool 装饰器：可以在我们的工具使用 API 中[了解更多](https://huggingface.co/blog/unified-tool-use#passing-tools-to-a-chat-template).
|
||||
|
||||
然后，您可以直接初始化您的智能体：
|
||||
```py
|
||||
from transformers import CodeAgent
|
||||
agent = CodeAgent(tools=[model_download_tool], llm_engine=llm_engine)
|
||||
agent.run(
|
||||
"Can you give me the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?"
|
||||
)
|
||||
```
|
||||
|
||||
您将得到以下输出：
|
||||
```text
|
||||
======== New task ========
|
||||
Can you give me the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?
|
||||
==== Agent is executing the code below:
|
||||
most_downloaded_model = model_download_tool(task="text-to-video")
|
||||
print(f"The most downloaded model for the 'text-to-video' task is {most_downloaded_model}.")
|
||||
====
|
||||
```
|
||||
|
||||
输出：
|
||||
`"The most downloaded model for the 'text-to-video' task is ByteDance/AnimateDiff-Lightning."`
|
||||
|
||||
### 管理智能体的工具箱
|
||||
|
||||
如果您已经初始化了一个智能体，但想添加一个新的工具，重新初始化智能体会很麻烦。借助 Transformers，您可以通过添加或替换工具来管理智能体的工具箱。
|
||||
|
||||
让我们将 `model_download_tool` 添加到一个仅初始化了默认工具箱的现有智能体中。
|
||||
|
||||
```python
|
||||
from transformers import CodeAgent
|
||||
|
||||
agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True)
|
||||
agent.toolbox.add_tool(model_download_tool)
|
||||
```
|
||||
现在，我们可以同时使用新工具和之前的文本到语音工具：
|
||||
|
||||
```python
|
||||
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 and return the audio?"
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
| **Audio** |
|
||||
|------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| <audio controls><source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/damo.wav" type="audio/wav"/> |
|
||||
|
||||
|
||||
> [WARNING]
|
||||
> 当向一个已经运行良好的代理添加工具时要小心，因为这可能会导致选择偏向你的工具，或者选择已经定义的工具之外的其他工具。
|
||||
|
||||
|
||||
使用 agent.toolbox.update_tool() 方法可以替换智能体工具箱中的现有工具。
|
||||
如果您的新工具完全替代了现有工具，这非常有用，因为智能体已经知道如何执行该特定任务。
|
||||
只需确保新工具遵循与替换工具相同的 API，或者调整系统提示模板，以确保所有使用替换工具的示例都得到更新。
|
||||
|
||||
|
||||
### 使用工具集合
|
||||
|
||||
您可以通过使用 ToolCollection 对象来利用工具集合，指定您想要使用的工具集合的 slug。
|
||||
然后将这些工具作为列表传递给智能体进行初始化，并开始使用它们！
|
||||
|
||||
```py
|
||||
from transformers import ToolCollection, ReactCodeAgent
|
||||
|
||||
image_tool_collection = ToolCollection(collection_slug="huggingface-tools/diffusion-tools-6630bb19a942c2306a2cdb6f")
|
||||
agent = ReactCodeAgent(tools=[*image_tool_collection.tools], add_base_tools=True)
|
||||
|
||||
agent.run("Please draw me a picture of rivers and lakes.")
|
||||
```
|
||||
|
||||
为了加速启动，工具仅在智能体调用时加载。
|
||||
|
||||
这将生成如下图像：
|
||||
|
||||
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rivers_and_lakes.png">
|
||||
@@ -1,250 +0,0 @@
|
||||
<!--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.
|
||||
-->
|
||||
# 智能体，超强版 - 多智能体、外部工具等
|
||||
|
||||
[[open-in-colab]]
|
||||
|
||||
### 什么是智能体？
|
||||
|
||||
> [!TIP]
|
||||
> 如果你是 `transformers.agents` 的新手，请先阅读主文档 [智能体文档 ](./agents).
|
||||
在本页面中，我们将重点介绍 `transformers.agents` 的几种高级用法。
|
||||
|
||||
## 多智能体
|
||||
|
||||
多智能体功能是微软框架 [Autogen](https://huggingface.co/papers/2308.08155) 中引入的。
|
||||
它的意思是让多个智能体一起工作来解决任务，而不是只有一个智能体。
|
||||
经验表明，在大多数基准测试中，这种方法能带来更好的性能。之所以有更好的性能，原因很简单：对于许多任务，通常我们更愿意让多个单独的单元专注于子任务，而不是让一个系统做所有事情。这里，拥有不同工具集和记忆的多个智能体可以实现高效的专业化。
|
||||
|
||||
你可以轻松地用 `transformers.agents` 构建层次化的多智能体系统。
|
||||
|
||||
为此，需要将智能体封装在 [`ManagedAgent`] 对象中。这个对象需要 `agent`、`name` 和 `description` 这几个参数，这些信息会嵌入到管理智能体的系统提示中，帮助它知道如何调用这个管理的智能体，就像我们对工具所做的那样。
|
||||
|
||||
下面是一个通过使用我们的 [`DuckDuckGoSearchTool`] 创建一个管理特定网络搜索智能体的示例：
|
||||
|
||||
|
||||
```py
|
||||
from transformers.agents import ReactCodeAgent, HfApiEngine, DuckDuckGoSearchTool, ManagedAgent
|
||||
|
||||
llm_engine = HfApiEngine()
|
||||
|
||||
web_agent = ReactCodeAgent(tools=[DuckDuckGoSearchTool()], llm_engine=llm_engine)
|
||||
|
||||
managed_web_agent = ManagedAgent(
|
||||
agent=web_agent,
|
||||
name="web_search",
|
||||
description="Runs web searches for you. Give it your query as an argument."
|
||||
)
|
||||
|
||||
manager_agent = ReactCodeAgent(
|
||||
tools=[], llm_engine=llm_engine, managed_agents=[managed_web_agent]
|
||||
)
|
||||
|
||||
manager_agent.run("Who is the CEO of Hugging Face?")
|
||||
```
|
||||
|
||||
> [!TIP]
|
||||
> 如果你想深入了解如何高效地实现多智能体系统，请查看 [how we pushed our multi-agent system to the top of the GAIA leaderboard](https://huggingface.co/blog/beating-gaia).
|
||||
|
||||
## 高级工具使用
|
||||
|
||||
### 通过子类化 Tool 来直接定义工具，并将其共享到 Hub
|
||||
|
||||
让我们再次使用主文档中的工具示例，我们已经实现了一个 `tool` 装饰器。
|
||||
|
||||
如果你需要添加一些变化，比如为工具自定义属性，可以按照更细粒度的方法构建工具：构建一个继承自 [`Tool`] 超类的类。
|
||||
|
||||
自定义工具需要：
|
||||
- `name` 属性：表示工具本身的名称，通常描述工具的作用。由于代码返回了针对任务下载量最多的模型，我们将其命名为 model_download_counter。
|
||||
- `description` 属性：用于填充智能体的系统提示。
|
||||
- `inputs` 属性：这是一个包含 "type" 和 "description" 键的字典。它包含了有助于 Python 解释器做出选择的输入信息。
|
||||
- `output_type` 属性：指定输出类型。
|
||||
- `forward` 方法：其中包含执行推理代码。
|
||||
|
||||
`inputs` 和 `output_type` 的类型应当是 [Pydantic 格式](https://docs.pydantic.dev/latest/concepts/json_schema/#generating-json-schema)。
|
||||
|
||||
```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 returns the name of the checkpoint."""
|
||||
|
||||
inputs = {
|
||||
"task": {
|
||||
"type": "string",
|
||||
"description": "the task category (such as text-classification, depth-estimation, etc)",
|
||||
}
|
||||
}
|
||||
output_type = "string"
|
||||
|
||||
def forward(self, task: str):
|
||||
model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
|
||||
return model.id
|
||||
```
|
||||
|
||||
现在，自定义的 `HfModelDownloadsTool` 类已经准备好，可以将其保存到名为 `model_downloads.py` 的文件中，并导入使用。
|
||||
|
||||
|
||||
```python
|
||||
from model_downloads import HFModelDownloadsTool
|
||||
|
||||
tool = HFModelDownloadsTool()
|
||||
```
|
||||
|
||||
你还可以通过调用 [`~Tool.push_to_hub`] 将自定义工具推送到 Hub。确保你已经为该工具创建了一个仓库，并使用具有读取访问权限的许可。
|
||||
|
||||
```python
|
||||
tool.push_to_hub("{your_username}/hf-model-downloads")
|
||||
```
|
||||
|
||||
通过 [`~Tool.load_tool`] 函数加载工具，并将其传递给智能体的 tools 参数。
|
||||
|
||||
```python
|
||||
from transformers import load_tool, CodeAgent
|
||||
|
||||
model_download_tool = load_tool("m-ric/hf-model-downloads")
|
||||
```
|
||||
|
||||
### 将 Space 导入为工具 🚀
|
||||
|
||||
你可以直接通过 [`Tool.from_space`] 方法将 Hub 上的 Space 导入为工具！
|
||||
|
||||
只需要提供 Space 在 Hub 上的 ID、名称和描述，帮助智能体理解工具的作用。在幕后，这将使用 [`gradio-client`](https://pypi.org/project/gradio-client/) 库来调用 Space。
|
||||
|
||||
例如，下面是从 Hub 导入 `FLUX.1-dev` Space 并用其生成图像的示例：
|
||||
|
||||
```
|
||||
from transformers import Tool
|
||||
image_generation_tool = Tool.from_space(
|
||||
"black-forest-labs/FLUX.1-dev",
|
||||
name="image_generator",
|
||||
description="Generate an image from a prompt")
|
||||
image_generation_tool("A sunny beach")
|
||||
```
|
||||
看！这就是你生成的图像！🏖️
|
||||
|
||||
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/sunny_beach.webp">
|
||||
|
||||
然后，你可以像使用其他工具一样使用这个工具。例如，改进提示 `穿宇航服的兔子` 并生成其图像：
|
||||
|
||||
```python
|
||||
from transformers import ReactCodeAgent
|
||||
|
||||
agent = ReactCodeAgent(tools=[image_generation_tool])
|
||||
|
||||
agent.run(
|
||||
"Improve this prompt, then generate an image of it.", prompt='A rabbit wearing a space suit'
|
||||
)
|
||||
```
|
||||
|
||||
```text
|
||||
=== Agent thoughts:
|
||||
improved_prompt could be "A bright blue space suit wearing rabbit, on the surface of the moon, under a bright orange sunset, with the Earth visible in the background"
|
||||
Now that I have improved the prompt, I can use the image generator tool to generate an image based on this prompt.
|
||||
>>> Agent is executing the code below:
|
||||
image = image_generator(prompt="A bright blue space suit wearing rabbit, on the surface of the moon, under a bright orange sunset, with the Earth visible in the background")
|
||||
final_answer(image)
|
||||
```
|
||||
|
||||
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit_spacesuit_flux.webp">
|
||||
|
||||
这真酷吧？🤩
|
||||
|
||||
### 使用 gradio-tools
|
||||
|
||||
[gradio-tools](https://github.com/freddyaboulton/gradio-tools) 是一个强大的库，允许使用 Hugging Face Spaces 作为工具。它支持许多现有的 Spaces，也支持自定义 Spaces。
|
||||
|
||||
transformers 支持通过 [`Tool.from_gradio`] 方法使用 `gradio_tools`。例如，下面是如何使用来自 `gradio-tools` 工具包的 [`StableDiffusionPromptGeneratorTool`](https://github.com/freddyaboulton/gradio-tools/blob/main/gradio_tools/tools/prompt_generator.py) 来改进提示，以生成更好的图像：
|
||||
|
||||
导入和实例化工具，并将其传递给 `Tool.from_gradio` 方法:
|
||||
|
||||
```python
|
||||
from gradio_tools import StableDiffusionPromptGeneratorTool
|
||||
from transformers import Tool, load_tool, CodeAgent
|
||||
|
||||
gradio_prompt_generator_tool = StableDiffusionPromptGeneratorTool()
|
||||
prompt_generator_tool = Tool.from_gradio(gradio_prompt_generator_tool)
|
||||
```
|
||||
|
||||
> [!WARNING]
|
||||
> gradio-tools 需要 **文本** 输入和输出，即使在处理像图像和音频这样的不同模态时也是如此。目前，图像和音频的输入输出与此不兼容。
|
||||
### 使用 LangChain 工具
|
||||
|
||||
我们很喜欢 LangChain，并认为它有一套非常有吸引力的工具。
|
||||
要从 LangChain 导入工具，可以使用 `from_langchain()` 方法。
|
||||
|
||||
例如，下面是如何使用它来重新创建上面介绍的搜索结果，使用一个 LangChain 网络搜索工具。该工具需要 `pip install google-search-results` 来正常工作。
|
||||
|
||||
```python
|
||||
from langchain.agents import load_tools
|
||||
from transformers import Tool, ReactCodeAgent
|
||||
|
||||
search_tool = Tool.from_langchain(load_tools(["serpapi"])[0])
|
||||
|
||||
agent = ReactCodeAgent(tools=[search_tool])
|
||||
|
||||
agent.run("How many more blocks (also denoted as layers) are in BERT base encoder compared to the encoder from the architecture proposed in Attention is All You Need?")
|
||||
```
|
||||
|
||||
## 在酷炫的 Gradio 界面中展示智能体运行
|
||||
|
||||
你可以利用 `gradio.Chatbot` 来展示智能体的思考过程，通过 `stream_to_gradio`，下面是一个示例：
|
||||
|
||||
```py
|
||||
import gradio as gr
|
||||
from transformers import (
|
||||
load_tool,
|
||||
ReactCodeAgent,
|
||||
HfApiEngine,
|
||||
stream_to_gradio,
|
||||
)
|
||||
|
||||
# Import tool from Hub
|
||||
image_generation_tool = load_tool("m-ric/text-to-image")
|
||||
|
||||
llm_engine = HfApiEngine("meta-llama/Meta-Llama-3-70B-Instruct")
|
||||
|
||||
# Initialize the agent with the image generation tool
|
||||
agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
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])
|
||||
|
||||
if __name__ == "__main__":
|
||||
demo.launch()
|
||||
```
|
||||
@@ -1,26 +0,0 @@
|
||||
<!--Copyright 2023 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和工具
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
The Agents framework has significantly changed in version v4.41.0.
|
||||
This document has been removed as it was referencing an older API.
|
||||
|
||||
We eagerly welcome new contributions for the updated API.
|
||||
|
||||
</Tip>
|
||||
Reference in New Issue
Block a user