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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 EmbeddingPrompt, TemplatePrompt, show_log, start_chain
# Load data with embeddings (computed beforehand)
gatsby = datasets.load_from_disk("gatsby")
gatsby.add_faiss_index("embeddings")
# Fast KNN retieval prompt
class KNNPrompt(EmbeddingPrompt):
def prompt(self, inp):
return inp["query"]
def find(self, out, inp):
res = gatsby.get_nearest_examples("embeddings", np.array(out), 1)
return {"question": inp["query"], "docs": res.examples["passages"]}
# QA prompt to ask question with examples
class QAPrompt(TemplatePrompt):
template_file = "gatsby.pmpt.tpl"
with start_chain("gatsby") as backend:
# question = "What did Gatsby do before he met Daisy?"
prompt = KNNPrompt(
backend.HuggingFaceEmbed("sentence-transformers/all-mpnet-base-v2")
).chain(QAPrompt(backend.OpenAI()))
# result = prompt(question)
# print(result)
gradio = prompt.to_gradio(fields=["query"],
examples=["What did Gatsby do before he met Daisy?",
"What did the narrator do after getting back to Chicago?"],
keys={"HF_KEY"},
description=desc)
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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