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desc = """ |
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### Book QA |
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Chain that does question answering with Hugging Face embeddings. [](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/gatsby.ipynb) |
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(Adapted from the [LlamaIndex example](https://github.com/jerryjliu/gpt_index/blob/main/examples/gatsby/TestGatsby.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, show, HuggingFaceEmbed, OpenAI, transform |
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gatsby = datasets.load_from_disk("gatsby") |
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gatsby.add_faiss_index("embeddings") |
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@prompt(HuggingFaceEmbed("sentence-transformers/all-mpnet-base-v2")) |
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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(embedding, k=1): |
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res = gatsby.get_nearest_examples("embeddings", np.array(embedding), k) |
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return res.examples["passages"] |
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@prompt(OpenAI(), template_file="gatsby.pmpt.tpl") |
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def ask(model, query, neighbors): |
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return model(dict(question=query, docs=neighbors)) |
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def gatsby_q(query): |
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n = get_neighbors(embed(query)) |
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return ask(query, n) |
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gradio = show(gatsby_q, |
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subprompts=[ask], |
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examples=["What did Gatsby do before he met Daisy?", |
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"What did the narrator do after getting back to Chicago?"], |
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keys={"HF_KEY"}, |
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description=desc, |
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code=open("gatsby.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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