qapipeline.py

@app.route('/ask_QA')
def ask_QA():
    query = request.args.get('question')
    print("Question received: ", query)
    # Connect to Elasticsearch (locally installed)
    document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="blogposts")

    retriever = ElasticsearchRetriever(document_store=document_store)
    reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)

    from haystack.pipelines import ExtractiveQAPipeline
    pipe = ExtractiveQAPipeline(reader, retriever)

    # Voilà! Ask a question!
    prediction = pipe.run(
        query, params={"Retriever": {"top_k": 5}, "Reader": {"top_k": 3}}
    )

    # Now you can either print the object directly
    print("\n\nRaw object:\n")
    pprint(prediction)

    headers: Dict[str, str] = {"Content-Type": "application/json"}
    return make_response(
        prediction,
        200,
        headers)