qa/app.py

# # QA

# Questions answering with embeddings.  Adapted from [OpenAI
# Notebook](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb).

import datasets
import numpy as np

from minichain import EmbeddingPrompt, TemplatePrompt, show_log, start_chain

# We use Hugging Face Datasets as the database by assigning
# a FAISS index.

olympics = datasets.load_from_disk("olympics.data")
olympics.add_faiss_index("embeddings")


# Fast KNN retieval prompt


class KNNPrompt(EmbeddingPrompt):
    def find(self, out, inp):
        res = olympics.get_nearest_examples("embeddings", np.array(out), 3)
        return {"question": inp, "docs": res.examples["content"]}


# QA prompt to ask question with examples


class QAPrompt(TemplatePrompt):
    template_file = "qa.pmpt.tpl"


with start_chain("qa") as backend:
    question = "Who won the 2020 Summer Olympics men's high jump?"
    prompt = KNNPrompt(backend.OpenAIEmbed()).chain(QAPrompt(backend.OpenAI()))
    result = prompt(question)
    print(result)

# + tags=["hide_inp"]
QAPrompt().show(
    {"question": "Who won the race?", "docs": ["doc1", "doc2", "doc3"]}, "Joe Bob"
)
# -

show_log("qa.log")