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# ## Task: Question Answering for Game of Thrones
#
# Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex
# knowledge bases or long documents ("search setting").
#
# A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports.
# In this tutorial we will work on a slightly different domain: "Game of Thrones".
#
# Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the
# marvellous seven kingdoms.
import logging
# We configure how logging messages should be displayed and which log level should be used before importing Haystack.
# Example log message:
# INFO - haystack.utils.preprocessing - Converting data/tutorial1/218_Olenna_Tyrell.txt
# Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
logging.basicConfig(format="%(levelname)s - %(name)s - %(message)s", level=logging.WARNING)
logging.getLogger("haystack").setLevel(logging.INFO)
from haystack.document_stores import ElasticsearchDocumentStore
from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http, print_answers, launch_es
from haystack.nodes import FARMReader, TransformersReader, BM25Retriever
def tutorial1_basic_qa_pipeline():
# ## Document Store
#
# Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of
# `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`.
#
# **Here:** We recommended Elasticsearch as it comes preloaded with features like full-text queries, BM25 retrieval,
# and vector storage for text embeddings.
# **Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the Tutorial 3
# for using SQL/InMemory document stores.
# **Hint**:
# This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can
# configure Haystack to work with your existing document stores.
#
# Start an Elasticsearch server
# You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in
# your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source.
from haystack.document_stores import InMemoryDocumentStore
document_store = InMemoryDocumentStore()
# ## 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 about Game of Thrones, apply a basic cleaning function, and add
# them in Elasticsearch.
# Let's first fetch some documents that we want to query
# Here: 517 Wikipedia articles for Game of Thrones
doc_dir = "data/tutorial1"
s3_url = "https://aws-ml-blog.s3.amazonaws.com/artifacts/kendra-docs/amazon_help_docs.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
docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
# 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.
# Now, let's write the docs to our DB.
document_store.write_documents(docs)
# ## Initialize Retriever & Reader
#
# ### Retriever
#
# Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question
# could be answered.
#
# They use some simple but fast algorithm.
# **Here:** We use Elasticsearch's default BM25 algorithm
# **Alternatives:**
# - Customize the `BM25Retriever`with custom queries (e.g. boosting) and filters
# - Use `EmbeddingRetriever` to find candidate documents based on the similarity of
# embeddings (e.g. created via Sentence-BERT)
# - Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging
retriever = BM25Retriever(document_store=document_store)
# Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes
# with SQLite document store.
#
# from haystack.retriever.tfidf 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](https://haystack.deepset.ai/docs/latest/pipelinesmd).
from haystack.pipelines import ExtractiveQAPipeline
pipe = ExtractiveQAPipeline(reader, retriever)
## Voilà! Ask a question!
prediction = pipe.run(
query="What is Amazon Music?", 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
print("\n\nRaw object:\n")
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'
# }
# Note that the documents contained in the above object are the documents filtered by the Retriever from
# the document store. Although the answers were extracted from these documents, it's possible that many
# answers were taken from a single one of them, and that some of the documents were not source of any answer.
# Or use a util to simplify the output
# Change `minimum` to `medium` or `all` to raise the level of detail
print("\n\nSimplified output:\n")
print_answers(prediction, details="minimum")
if __name__ == "__main__":
tutorial1_basic_qa_pipeline()
# This Haystack script was made with love by deepset in Berlin, Germany
# Haystack: https://github.com/deepset-ai/haystack
# deepset: https://deepset.ai/