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desc = """ |
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### Agent |
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Chain that executes different tools based on model decisions. [](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/bash.ipynb) |
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(Adapted from LangChain ) |
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""" |
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from minichain import Id, prompt, OpenAI, show, transform, Mock, Break |
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from gradio_tools.tools import StableDiffusionTool, ImageCaptioningTool, ImageToMusicTool |
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tools = [StableDiffusionTool(), ImageCaptioningTool(), ImageToMusicTool()] |
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@prompt(OpenAI(stop=["Observation:"]), |
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template_file="agent.pmpt.tpl") |
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def agent(model, query, history): |
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return model(dict(tools=[(str(tool.__class__.__name__), tool.description) |
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for tool in tools], |
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input=query, |
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agent_scratchpad=history |
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)) |
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@transform() |
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def tool_parse(out): |
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lines = out.split("\n") |
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if lines[0].split("?")[-1].strip() == "Yes": |
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tool = lines[1].split(":", 1)[-1].strip() |
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command = lines[2].split(":", 1)[-1].strip() |
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return tool, command |
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else: |
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return Break() |
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@prompt(tools) |
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def tool_use(model, usage): |
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selector, command = usage |
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for i, tool in enumerate(tools): |
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if selector == tool.__class__.__name__: |
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return model(command, tool_num=i) |
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return ("",) |
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@transform() |
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def append(history, new, observation): |
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return history + "\n" + new + "Observation: " + observation |
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def run(query): |
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history = "" |
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observations = [] |
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for i in range(3): |
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select_input = agent(query, history) |
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observations.append(tool_use(tool_parse(select_input))) |
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history = append(history, select_input, observations[i]) |
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return observations[-1] |
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gradio = show(run, |
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subprompts=[agent, tool_use] * 3, |
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examples=[ |
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"I would please like a photo of a dog riding a skateboard. " |
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"Please caption this image and create a song for it.", |
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'Use an image generator tool to draw a cat.', |
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'Caption the image https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.png from the internet'], |
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out_type="markdown", |
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description=desc, |
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show_advanced=False |
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) |
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if __name__ == "__main__": |
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gradio.queue().launch() |
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