[agents] remove agents 🧹 (#37368)
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Joao Gante
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<!--Copyright 2023 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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# Agents & Tools
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<Tip warning={true}>
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Transformers Agents is an experimental API which is subject to change at any time. Results returned by the agents
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can vary as the APIs or underlying models are prone to change.
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</Tip>
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To learn more about agents and tools make sure to read the [introductory guide](../transformers_agents). This page
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contains the API docs for the underlying classes.
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## Agents
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We provide two types of agents, based on the main [`Agent`] class:
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- [`CodeAgent`] acts in one shot, generating code to solve the task, then executes it at once.
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- [`ReactAgent`] acts step by step, each step consisting of one thought, then one tool call and execution. It has two classes:
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- [`ReactJsonAgent`] writes its tool calls in JSON.
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- [`ReactCodeAgent`] writes its tool calls in Python code.
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### Agent
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[[autodoc]] Agent
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### CodeAgent
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[[autodoc]] CodeAgent
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### React agents
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[[autodoc]] ReactAgent
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[[autodoc]] ReactJsonAgent
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[[autodoc]] ReactCodeAgent
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### ManagedAgent
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[[autodoc]] ManagedAgent
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## Tools
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### load_tool
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[[autodoc]] load_tool
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### tool
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[[autodoc]] tool
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### Tool
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[[autodoc]] Tool
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### Toolbox
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[[autodoc]] Toolbox
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### PipelineTool
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[[autodoc]] PipelineTool
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### launch_gradio_demo
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[[autodoc]] launch_gradio_demo
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### stream_to_gradio
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[[autodoc]] stream_to_gradio
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### ToolCollection
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[[autodoc]] ToolCollection
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## Engines
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You're free to create and use your own engines to be usable by the Agents framework.
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These engines have the following specification:
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1. Follow the [messages format](../chat_templating.md) for its input (`List[Dict[str, str]]`) and return a string.
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2. Stop generating outputs *before* the sequences passed in the argument `stop_sequences`
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### TransformersEngine
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For convenience, we have added a `TransformersEngine` that implements the points above, taking a pre-initialized `Pipeline` as input.
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```python
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, TransformersEngine
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>>> model_name = "HuggingFaceTB/SmolLM-135M-Instruct"
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>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
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>>> model = AutoModelForCausalLM.from_pretrained(model_name)
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>>> pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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>>> engine = TransformersEngine(pipe)
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>>> engine([{"role": "user", "content": "Ok!"}], stop_sequences=["great"])
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"What a "
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```
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[[autodoc]] TransformersEngine
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### HfApiEngine
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The `HfApiEngine` is an engine that wraps an [HF Inference API](https://huggingface.co/docs/api-inference/index) client for the execution of the LLM.
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```python
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>>> from transformers import HfApiEngine
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>>> messages = [
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... {"role": "user", "content": "Hello, how are you?"},
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... {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
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... {"role": "user", "content": "No need to help, take it easy."},
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... ]
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>>> HfApiEngine()(messages, stop_sequences=["conversation"])
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"That's very kind of you to say! It's always nice to have a relaxed "
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```
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[[autodoc]] HfApiEngine
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## Agent Types
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Agents can handle any type of object in-between tools; tools, being completely multimodal, can accept and return
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text, image, audio, video, among other types. In order to increase compatibility between tools, as well as to
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correctly render these returns in ipython (jupyter, colab, ipython notebooks, ...), we implement wrapper classes
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around these types.
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The wrapped objects should continue behaving as initially; a text object should still behave as a string, an image
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object should still behave as a `PIL.Image`.
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These types have three specific purposes:
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- Calling `to_raw` on the type should return the underlying object
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- Calling `to_string` on the type should return the object as a string: that can be the string in case of an `AgentText`
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but will be the path of the serialized version of the object in other instances
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- Displaying it in an ipython kernel should display the object correctly
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### AgentText
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[[autodoc]] transformers.agents.agent_types.AgentText
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### AgentImage
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[[autodoc]] transformers.agents.agent_types.AgentImage
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### AgentAudio
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[[autodoc]] transformers.agents.agent_types.AgentAudio
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