haystack/tutorials/Tutorial3_Basic_QA_Pipeline_without_Elasticsearch.py

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# ## Task: Build a Question Answering pipeline without Elasticsearch
#
# 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.
Refactoring of the `haystack` package (#1624) * Files moved, imports all broken * Fix most imports and docstrings into * Fix the paths to the modules in the API docs * Add latest docstring and tutorial changes * Add a few pipelines that were lost in the inports * Fix a bunch of mypy warnings * Add latest docstring and tutorial changes * Create a file_classifier module * Add docs for file_classifier * Fixed most circular imports, now the REST API can start * Add latest docstring and tutorial changes * Tackling more mypy issues * Reintroduce from FARM and fix last mypy issues hopefully * Re-enable old-style imports * Fix some more import from the top-level package in an attempt to sort out circular imports * Fix some imports in tests to new-style to prevent failed class equalities from breaking tests * Change document_store into document_stores * Update imports in tutorials * Add latest docstring and tutorial changes * Probably fixes summarizer tests * Improve the old-style import allowing module imports (should work) * Try to fix the docs * Remove dedicated KnowledgeGraph page from autodocs * Remove dedicated GraphRetriever page from autodocs * Fix generate_docstrings.sh with an updated list of yaml files to look for * Fix some more modules in the docs * Fix the document stores docs too * Fix a small issue on Tutorial14 * Add latest docstring and tutorial changes * Add deprecation warning to old-style imports * Remove stray folder and import Dict into dense.py * Change import path for MLFlowLogger * Add old loggers path to the import path aliases * Fix debug output of convert_ipynb.py * Fix circular import on BaseRetriever * Missed one merge block * re-run tutorial 5 * Fix imports in tutorial 5 * Re-enable squad_to_dpr CLI from the root package and move get_batches_from_generator into document_stores.base * Add latest docstring and tutorial changes * Fix typo in utils __init__ * Fix a few more imports * Fix benchmarks too * New-style imports in test_knowledge_graph * Rollback setup.py * Rollback squad_to_dpr too Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2021-10-25 15:50:23 +02:00
from haystack.document_stores import InMemoryDocumentStore, SQLDocumentStore
from haystack.nodes import FARMReader, TransformersReader, TfidfRetriever
from haystack.utils import clean_wiki_text, convert_files_to_dicts, fetch_archive_from_http, print_answers
def tutorial3_basic_qa_pipeline_without_elasticsearch():
# In-Memory Document Store
document_store = InMemoryDocumentStore()
# or, alternatively, SQLite Document Store
# 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
dicts = convert_files_to_dicts(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(dicts)
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# ## 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
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).
Refactoring of the `haystack` package (#1624) * Files moved, imports all broken * Fix most imports and docstrings into * Fix the paths to the modules in the API docs * Add latest docstring and tutorial changes * Add a few pipelines that were lost in the inports * Fix a bunch of mypy warnings * Add latest docstring and tutorial changes * Create a file_classifier module * Add docs for file_classifier * Fixed most circular imports, now the REST API can start * Add latest docstring and tutorial changes * Tackling more mypy issues * Reintroduce from FARM and fix last mypy issues hopefully * Re-enable old-style imports * Fix some more import from the top-level package in an attempt to sort out circular imports * Fix some imports in tests to new-style to prevent failed class equalities from breaking tests * Change document_store into document_stores * Update imports in tutorials * Add latest docstring and tutorial changes * Probably fixes summarizer tests * Improve the old-style import allowing module imports (should work) * Try to fix the docs * Remove dedicated KnowledgeGraph page from autodocs * Remove dedicated GraphRetriever page from autodocs * Fix generate_docstrings.sh with an updated list of yaml files to look for * Fix some more modules in the docs * Fix the document stores docs too * Fix a small issue on Tutorial14 * Add latest docstring and tutorial changes * Add deprecation warning to old-style imports * Remove stray folder and import Dict into dense.py * Change import path for MLFlowLogger * Add old loggers path to the import path aliases * Fix debug output of convert_ipynb.py * Fix circular import on BaseRetriever * Missed one merge block * re-run tutorial 5 * Fix imports in tutorial 5 * Re-enable squad_to_dpr CLI from the root package and move get_batches_from_generator into document_stores.base * Add latest docstring and tutorial changes * Fix typo in utils __init__ * Fix a few more imports * Fix benchmarks too * New-style imports in test_knowledge_graph * Rollback setup.py * Rollback squad_to_dpr too Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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from haystack.pipelines import ExtractiveQAPipeline
pipe = ExtractiveQAPipeline(reader, retriever)
## Voilà! Ask a question!
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
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__":
tutorial3_basic_qa_pipeline_without_elasticsearch()
# This Haystack script was made with love by deepset in Berlin, Germany
# Haystack: https://github.com/deepset-ai/haystack
# deepset: https://deepset.ai/