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Build a QA System Without Elasticsearch

Open In Colab

Haystack provides alternatives to Elasticsearch for developing quick prototypes.

You can use an InMemoryDocumentStore or a SQLDocumentStore(with SQLite) as the document store.

If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.

Prepare environment

Colab: Enable the GPU runtime

Make sure you enable the GPU runtime to experience decent speed in this tutorial. Runtime -> Change Runtime type -> Hardware accelerator -> GPU

# Make sure you have a GPU running
!nvidia-smi
# Install the latest release of Haystack in your own environment 
#! pip install farm-haystack

# Install the latest master of Haystack
!pip install --upgrade pip
!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]
from haystack.utils import clean_wiki_text, convert_files_to_dicts, fetch_archive_from_http, print_answers
from haystack.nodes import FARMReader, TransformersReader

Document Store

# In-Memory Document Store
from haystack.document_stores import InMemoryDocumentStore
document_store = InMemoryDocumentStore()
# SQLite Document Store
# from haystack.document_stores import SQLDocumentStore
# document_store = SQLDocumentStore(url="sqlite:///qa.db")

Preprocessing of documents

Haystack provides a customizable pipeline for:

  • converting files into texts
  • cleaning texts
  • splitting texts
  • writing them to a Document Store

In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index them in Elasticsearch.

# Let's first get some documents that we want to query
# Here: 517 Wikipedia articles for Game of Thrones
doc_dir = "data/article_txt_got"
s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
fetch_archive_from_http(url=s3_url, output_dir=doc_dir)

# convert files to dicts containing documents that can be indexed to our datastore
# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
# It must take a str as input, and return a str.
dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)

# We now have a list of dictionaries that we can write to our document store.
# If your texts come from a different source (e.g. a DB), you can of course skip convert_files_to_dicts() and create the dictionaries yourself.
# The default format here is: {"name": "<some-document-name>, "text": "<the-actual-text>"}

# Let's have a look at the first 3 entries:
print(dicts[:3])
# Now, let's write the docs to our DB.
document_store.write_documents(dicts)

Initalize Retriever, Reader & Pipeline

Retriever

Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question could be answered.

With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more retrievers, please refer to the tutorial-1.

# An in-memory TfidfRetriever based on Pandas dataframes

from haystack.nodes import TfidfRetriever
retriever = TfidfRetriever(document_store=document_store)

Reader

A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based on powerful, but slower deep learning models.

Haystack currently supports Readers based on the frameworks FARM and Transformers. With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).

Here: a medium sized RoBERTa QA model using a Reader based on FARM (https://huggingface.co/deepset/roberta-base-squad2)

Alternatives (Reader): TransformersReader (leveraging the pipeline of the Transformers package)

Alternatives (Models): e.g. "distilbert-base-uncased-distilled-squad" (fast) or "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)

Hint: You can adjust the model to return "no answer possible" with the no_ans_boost. Higher values mean the model prefers "no answer possible"

FARMReader

# Load a  local model or any of the QA models on
# Hugging Face's model hub (https://huggingface.co/models)

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

TransformersReader

# Alternative:
# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)

Pipeline

With a Haystack Pipeline you can stick together your building blocks to a search pipeline. Under the hood, Pipelines are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases. To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the ExtractiveQAPipeline that combines a retriever and a reader to answer our questions. You can learn more about Pipelines in the docs.

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

Voilà! Ask a question!

# You can configure how many candidates the reader and retriever shall return
# The higher top_k for retriever, the better (but also the slower) your answers.
prediction = pipe.run(
    query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
)
# prediction = pipe.run(query="Who created the Dothraki vocabulary?", params={"Reader": {"top_k": 5}})
# prediction = pipe.run(query="Who is the sister of Sansa?", params={"Reader": {"top_k": 5}})
# Now you can either print the object directly...
from pprint import pprint

pprint(prediction)

# Sample output:    
# {
#     'answers': [ <Answer: answer='Eddard', type='extractive', score=0.9919578731060028, offsets_in_document=[{'start': 608, 'end': 615}], offsets_in_context=[{'start': 72, 'end': 79}], document_id='cc75f739897ecbf8c14657b13dda890e', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,
#                  <Answer: answer='Ned', type='extractive', score=0.9767240881919861, offsets_in_document=[{'start': 3687, 'end': 3801}], offsets_in_context=[{'start': 18, 'end': 132}], document_id='9acf17ec9083c4022f69eb4a37187080', meta={'name': '454_Music_of_Game_of_Thrones.txt'}}, context='...' >,
#                  ...
#                ]
#     'documents': [ <Document: content_type='text', score=0.8034909798951382, meta={'name': '332_Sansa_Stark.txt'}, embedding=None, id=d1f36ec7170e4c46cde65787fe125dfe', content='\n===\'\'A Game of Thrones\'\'===\nSansa Stark begins the novel by being betrothed to Crown ...'>,
#                    <Document: content_type='text', score=0.8002150354529785, meta={'name': '191_Gendry.txt'}, embedding=None, id='dd4e070a22896afa81748d6510006d2', 'content='\n===Season 2===\nGendry travels North with Yoren and other Night's Watch recruits, including Arya ...'>,
#                    ...
#                  ],
#     'no_ans_gap':  11.688868522644043,
#     'node_id': 'Reader',
#     'params': {'Reader': {'top_k': 5}, 'Retriever': {'top_k': 5}},
#     'query': 'Who is the father of Arya Stark?',
#     'root_node': 'Query'
# }
# ...or use a util to simplify the output
# Change `minimum` to `medium` or `all` to raise the level of detail
print_answers(prediction, details="minimum")

About us

This Haystack notebook was made with love by deepset in Berlin, Germany

We bring NLP to the industry via open source!
Our focus: Industry specific language models & large scale QA systems.

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