haystack/tutorials/Tutorial4_FAQ_style_QA.py

from haystack.document_store.elasticsearch import ElasticsearchDocumentStore

from haystack.retriever.dense import EmbeddingRetriever
from haystack.utils import launch_es
import pandas as pd
import requests
import logging
import subprocess
import time


def tutorial4_faq_style_qa():
    ## "FAQ-Style QA": Utilizing existing FAQs for Question Answering

    # While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
    #
    # Pros:
    # - Very fast at inference time
    # - Utilize existing FAQ data
    # - Quite good control over answers
    #
    # Cons:
    # - Generalizability: We can only answer questions that are similar to existing ones in FAQ
    #
    # In some use cases, a combination of extractive QA and FAQ-style can also be an interesting option.
    launch_es()

    ### Init the DocumentStore
    # In contrast to Tutorial 1 (extractive QA), we:
    #
    # * specify the name of our `text_field` in Elasticsearch that we want to return as an answer
    # * specify the name of our `embedding_field` in Elasticsearch where we'll store the embedding of our question and that is used later for calculating our similarity to the incoming user question
    # * set `excluded_meta_data=["question_emb"]` so that we don't return the huge embedding vectors in our search results

    document_store = ElasticsearchDocumentStore(host="localhost", username="", password="",
                                                index="document",
                                                embedding_field="question_emb",
                                                embedding_dim=384,
                                                excluded_meta_data=["question_emb"],
                                                similarity="cosine")

    ### Create a Retriever using embeddings
    # Instead of retrieving via Elasticsearch's plain BM25, we want to use vector similarity of the questions (user question vs. FAQ ones).
    # We can use the `EmbeddingRetriever` for this purpose and specify a model that we use for the embeddings.
    #
    retriever = EmbeddingRetriever(document_store=document_store, embedding_model="sentence-transformers/all-MiniLM-L6-v2", use_gpu=True)

    # Download a csv containing some FAQ data
    # Here: Some question-answer pairs related to COVID-19
    temp = requests.get("https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/faqs/faq_covidbert.csv")
    open('small_faq_covid.csv', 'wb').write(temp.content)

    # Get dataframe with columns "question", "answer" and some custom metadata
    df = pd.read_csv("small_faq_covid.csv")
    # Minimal cleaning
    df.fillna(value="", inplace=True)
    df["question"] = df["question"].apply(lambda x: x.strip())
    print(df.head())

    # Get embeddings for our questions from the FAQs
    questions = list(df["question"].values)
    df["question_emb"] = retriever.embed_queries(texts=questions)
    df = df.rename(columns={"question": "content"})

    # Convert Dataframe to list of dicts and index them in our DocumentStore
    docs_to_index = df.to_dict(orient="records")
    document_store.write_documents(docs_to_index)

    #    Initialize a Pipeline (this time without a reader) and ask questions

    from haystack.pipeline import FAQPipeline
    pipe = FAQPipeline(retriever=retriever)

    prediction = pipe.run(query="How is the virus spreading?", params={"Retriever": {"top_k": 10}})
    for a in prediction["answers"]:
        print(f"Answer: {a.answer}")
        print(f"Question: {a.meta['query']}")
        print(f"Score: {a.score}")
        print("---------------------")


if __name__ == "__main__":
    tutorial4_faq_style_qa()

# This Haystack script was made with love by deepset in Berlin, Germany
# Haystack: https://github.com/deepset-ai/haystack
# deepset: https://deepset.ai/