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desc = """ |
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### Chat |
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A chat-like example for multi-turn chat with state. [](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/chat.ipynb) |
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(Adapted from [LangChain](https://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html)'s version of this [blog post](https://www.engraved.blog/building-a-virtual-machine-inside/).) |
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""" |
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from dataclasses import dataclass, replace |
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from typing import List, Tuple |
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from minichain import OpenAI, prompt, show, transform, Mock |
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MEMORY = 2 |
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@dataclass |
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class State: |
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memory: List[Tuple[str, str]] |
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human_input: str = "" |
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def push(self, response: str) -> "State": |
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memory = self.memory if len(self.memory) < MEMORY else self.memory[1:] |
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return State(memory + [(self.human_input, response)]) |
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def __str__(self): |
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return self.memory[-1][-1] |
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@prompt(OpenAI(), template_file="chat.pmpt.tpl") |
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def chat_response(model, state: State) -> State: |
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return model.stream(state) |
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@transform() |
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def update(state, chat_output): |
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result = chat_output.split("Assistant:")[-1] |
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return state.push(result) |
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def chat(command, state): |
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state = replace(state, human_input=command) |
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return update(state, chat_response(state)) |
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examples = [ |
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"ls ~", |
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"cd ~", |
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"{Please make a file jokes.txt inside and put some jokes inside}", |
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"""echo -e "x=lambda y:y*5+3;print('Result:' + str(x(6)))" > run.py && python3 run.py""", |
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"""echo -e "print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])" > run.py && python3 run.py""", |
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"""echo -e "echo 'Hello from Docker" > entrypoint.sh && echo -e "FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image""", |
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"nvidia-smi" |
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] |
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print(chat("ls", State([])).run()) |
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gradio = show(chat, |
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initial_state=State([]), |
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subprompts=[chat_response], |
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examples=examples, |
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out_type="json", |
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description=desc, |
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code=open("chat.py", "r").read().split("$")[1].strip().strip("#").strip(), |
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) |
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if __name__ == "__main__": |
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gradio.queue().launch() |
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