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desc = """ |
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### Question Answering with Retrieval |
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Chain that answers questions with embeedding based retrieval. [](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/qa.ipynb) |
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(Adapted from [OpenAI Notebook](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb).) |
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""" |
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import datasets |
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import numpy as np |
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from minichain import prompt, transform, show, OpenAIEmbed, OpenAI |
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from manifest import Manifest |
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olympics = datasets.load_from_disk("olympics.data") |
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olympics.add_faiss_index("embeddings") |
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@prompt(OpenAIEmbed()) |
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def embed(model, inp): |
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return model(inp) |
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@transform() |
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def get_neighbors(inp, k): |
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res = olympics.get_nearest_examples("embeddings", np.array(inp), k) |
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return res.examples["content"] |
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@prompt(OpenAI(), template_file="qa.pmpt.tpl") |
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def get_result(model, query, neighbors): |
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return model(dict(question=query, docs=neighbors)) |
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def qa(query): |
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n = get_neighbors(embed(query), 3) |
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return get_result(query, n) |
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questions = ["Who won the 2020 Summer Olympics men's high jump?", |
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"Why was the 2020 Summer Olympics originally postponed?", |
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"In the 2020 Summer Olympics, how many gold medals did the country which won the most medals win?", |
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"What is the total number of medals won by France?", |
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"What is the tallest mountain in the world?"] |
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gradio = show(qa, |
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examples=questions, |
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subprompts=[embed, get_result], |
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description=desc, |
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code=open("qa.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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