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# + tags=["hide_inp"]
desc = """
### Book QA
Chain that does question answering with Hugging Face embeddings. [[Code](https://github.com/srush/MiniChain/blob/main/examples/gatsby.py)]
(Adapted from the [LlamaIndex example](https://github.com/jerryjliu/gpt_index/blob/main/examples/gatsby/TestGatsby.ipynb).)
"""
# -
# $
import datasets
import numpy as np
from minichain import prompt, show, HuggingFaceEmbed, OpenAI
# Load data with embeddings (computed beforehand)
gatsby = datasets.load_from_disk("gatsby")
gatsby.add_faiss_index("embeddings")
# Fast KNN retieval prompt
@prompt(HuggingFaceEmbed("sentence-transformers/all-mpnet-base-v2"))
def get_neighbors(model, inp, k=1):
embedding = model(inp)
res = olympics.get_nearest_examples("embeddings", np.array(embedding), k)
return res.examples["passages"]
@prompt(OpenAI(),
template_file="gatsby.pmpt.tpl")
def ask(model, query, neighbors):
return model(dict(question=query, docs=neighbors))
def gatsby(query):
n = get_neighbors(query)
return ask(query, n)
# $
gradio = show(gatsby,
subprompts=[get_neighbors, ask],
examples=["What did Gatsby do before he met Daisy?",
"What did the narrator do after getting back to Chicago?"],
keys={"HF_KEY"},
description=desc,
code=open("gatsby.py", "r").read().split("$")[1].strip().strip("#").strip()
)
if __name__ == "__main__":
gradio.launch()
# + tags=["hide_inp"]
# QAPrompt().show({"question": "Who was Gatsby?", "docs": ["doc1", "doc2", "doc3"]}, "")
# # -
# show_log("gatsby.log")
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