openai_text-embedding-ada-002/recursive_chunks/_agents.txt_chunk_1.txt

Now you can use it just like any other tool. For example, let's improve the prompt  a rabbit wearing a space suit.
thon
image_generation_tool = load_tool('huggingface-tools/text-to-image')
agent = CodeAgent(tools=[prompt_generator_tool, image_generation_tool], llm_engine=llm_engine)
agent.run(
    "Improve this prompt, then generate an image of it.", prompt='A rabbit wearing a space suit'
)

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)
====
Before finally generating the image:

[!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
We love Langchain and think it has a very compelling suite of tools.
To import a tool from LangChain, use the from_langchain() method.
Here is how you can use it to recreate the intro's search result using a LangChain web search tool.
thon
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) in BERT base encoder than the encoder from the architecture proposed in Attention is All You Need?")
```