Tutorial: Build Your First Question Answering System

We’ve modified this first tutorial to make it simpler to start with. If you’re looking for a Question Answering tutorial that uses a DocumentStore such as Elasticsearch, go to our new Build a Scalable Question Answering System tutorial.

• Level: Beginner
• Time to complete: 15 minutes
• Nodes Used: InMemoryDocumentStore, BM25Retriever, FARMReader
• Goal: After completing this tutorial, you will have learned about the Reader and Retriever, and built a question answering pipeline that can answer questions about the Game of Thrones series.

Overview

Learn how to build a question answering system using Haystack’s DocumentStore, Retriever, and Reader. Your system will use Game of Thrones files and will be able to answer questions like “Who is the father of Arya Stark?”. But you can use it to run on any other set of documents, such as your company’s internal wikis or a collection of financial reports.

To help you get started quicker, we simplified certain steps in this tutorial. For example, Document preparation and pipeline initialization are handled by ready-made classes that replace lines of initialization code. But don’t worry! This doesn’t affect how well the question answering system performs.

Preparing the Colab Environment

• Enable GPU Runtime in Colab
• Set logging level to INFO

Installing Haystack

To start, let’s install the latest release of Haystack with pip:

%%bash

pip install --upgrade pip
pip install farm-haystack[colab,inference]

Enabling Telemetry

Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See Telemetry for more details.

from haystack.telemetry import tutorial_running

tutorial_running(1)

Set the logging level to INFO:

import logging

logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
logging.getLogger("haystack").setLevel(logging.INFO)

Initializing the DocumentStore

We’ll start creating our question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, we’re using the InMemoryDocumentStore, which is the simplest DocumentStore to get started with. It requires no external dependencies and it’s a good option for smaller projects and debugging. But it doesn’t scale up so well to larger Document collections, so it’s not a good choice for production systems. To learn more about the DocumentStore and the different types of external databases that we support, see DocumentStore.

Let’s initialize the the DocumentStore:

from haystack.document_stores import InMemoryDocumentStore

document_store = InMemoryDocumentStore(use_bm25=True)

The DocumentStore is now ready. Now it’s time to fill it with some Documents.

Preparing Documents

1. Download 517 articles from the Game of Thrones Wikipedia. You can find them in data/build_your_first_question_answering_system as a set of .txt files.

from haystack.utils import fetch_archive_from_http

doc_dir = "data/build_your_first_question_answering_system"

fetch_archive_from_http(
    url="https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt1.zip",
    output_dir=doc_dir,
)

2. Use TextIndexingPipeline to convert the files you just downloaded into Haystack Document objects and write them into the DocumentStore:

import os
from haystack.pipelines.standard_pipelines import TextIndexingPipeline

files_to_index = [doc_dir + "/" + f for f in os.listdir(doc_dir)]
indexing_pipeline = TextIndexingPipeline(document_store)
indexing_pipeline.run_batch(file_paths=files_to_index)

The code in this tutorial uses the Game of Thrones data, but you can also supply your own .txt files and index them in the same way.

As an alternative, you can cast your text data into Document objects and write them into the DocumentStore using DocumentStore.write_documents().

Initializing the Retriever

Our search system will use a Retriever, so we need to initialize it. A Retriever sifts through all the Documents and returns only the ones relevant to the question. This tutorial uses the BM25 algorithm. For more Retriever options, see Retriever.

Let’s initialize a BM25Retriever and make it use the InMemoryDocumentStore we initialized earlier in this tutorial:

from haystack.nodes import BM25Retriever

retriever = BM25Retriever(document_store=document_store)

The Retriever is ready but we still need to initialize the Reader.

Initializing the Reader

A Reader scans the texts it received from the Retriever and extracts the top answer candidates. Readers are based on powerful deep learning models but are much slower than Retrievers at processing the same amount of text. In this tutorial, we’re using a FARMReader with a base-sized RoBERTa question answering model called deepset/roberta-base-squad2. It’s a strong all-round model that’s good as a starting point. To find the best model for your use case, see Models.

Let’s initialize the Reader:

from haystack.nodes import FARMReader

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

We’ve initalized all the components for our pipeline. We’re now ready to create the pipeline.

Creating the Retriever-Reader Pipeline

In this tutorial, we’re using a ready-made pipeline called ExtractiveQAPipeline. It connects the Reader and the Retriever. The combination of the two speeds up processing because the Reader only processes the Documents that the Retriever has passed on. To learn more about pipelines, see Pipelines.

To create the pipeline, run:

from haystack.pipelines import ExtractiveQAPipeline

pipe = ExtractiveQAPipeline(reader, retriever)

The pipeline’s ready, you can now go ahead and ask a question!

Asking a Question

1. Use the pipeline run() method to ask a question. The query argument is where you type your question. Additionally, you can set the number of documents you want the Reader and Retriever to return using the top-k parameter. To learn more about setting arguments, see Arguments. To understand the importance of the top-k parameter, see Choosing the Right top-k Values.

prediction = pipe.run(
    query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
)

Here are some questions you could try out:

• Who is the father of Arya Stark?
• Who created the Dothraki vocabulary?
• Who is the sister of Sansa?

2. Print out the answers the pipeline returned:

from pprint import pprint

pprint(prediction)

3. Simplify the printed answers:

from haystack.utils import print_answers

print_answers(prediction, details="minimum")  ## Choose from `minimum`, `medium`, and `all`

And there you have it! Congratulations on building your first machine learning based question answering system!

Next Steps

Check out Build a Scalable Question Answering System to learn how to make a more advanced question answering system that uses an Elasticsearch backed DocumentStore and makes more use of the flexibility that pipelines offer.