haystack/tutorials/Tutorial14_Query_Classifier.py
Sara Zan a59bca3661
Apply black formatting (#2115)
* Testing black on ui/

* Applying black on docstores

* Add latest docstring and tutorial changes

* Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too

* Remove comments

* Relax constraints on pydoc-markdown

* Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade

* Fix a couple of bugs

* Add a type: ignore that was missing somehow

* Give path to black

* Apply Black

* Apply Black

* Relocate a couple of type: ignore

* Update documentation

* Make Linux CI run after applying Black

* Triggering Black

* Apply Black

* Remove dependency, does not work well

* Remove manually double trailing commas

* Update documentation

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2022-02-03 13:43:18 +01:00

244 lines
9.1 KiB
Python

import os
from subprocess import Popen, PIPE, STDOUT
from haystack.utils import fetch_archive_from_http, convert_files_to_dicts, clean_wiki_text, launch_es, print_answers
from haystack.pipelines import Pipeline, RootNode
from haystack.document_stores import ElasticsearchDocumentStore
from haystack.nodes import (
ElasticsearchRetriever,
DensePassageRetriever,
FARMReader,
TransformersQueryClassifier,
SklearnQueryClassifier,
)
def tutorial14_query_classifier():
# Download and prepare data - 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
got_dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
# Initialize DocumentStore and index documents
launch_es()
document_store = ElasticsearchDocumentStore()
document_store.delete_documents()
document_store.write_documents(got_dicts)
# Initialize Sparse retriever
es_retriever = ElasticsearchRetriever(document_store=document_store)
# Initialize dense retriever
dpr_retriever = DensePassageRetriever(document_store)
document_store.update_embeddings(dpr_retriever, update_existing_embeddings=False)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
print()
print("Sklearn keyword classifier")
print("==========================")
# Here we build the pipeline
sklearn_keyword_classifier = Pipeline()
sklearn_keyword_classifier.add_node(component=SklearnQueryClassifier(), name="QueryClassifier", inputs=["Query"])
sklearn_keyword_classifier.add_node(
component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"]
)
sklearn_keyword_classifier.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_2"])
sklearn_keyword_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
sklearn_keyword_classifier.draw("pipeline_classifier.png")
# Run only the dense retriever on the full sentence query
res_1 = sklearn_keyword_classifier.run(
query="Who is the father of Arya Stark?",
)
print("\n===============================")
print("DPR Results" + "\n" + "=" * 15)
print_answers(res_1, details="minimum")
# Run only the sparse retriever on a keyword based query
res_2 = sklearn_keyword_classifier.run(
query="arya stark father",
)
print("\n===============================")
print("ES Results" + "\n" + "=" * 15)
print_answers(res_2, details="minimum")
# Run only the dense retriever on the full sentence query
res_3 = sklearn_keyword_classifier.run(
query="which country was jon snow filmed ?",
)
print("\n===============================")
print("DPR Results" + "\n" + "=" * 15)
print_answers(res_3, details="minimum")
# Run only the sparse retriever on a keyword based query
res_4 = sklearn_keyword_classifier.run(
query="jon snow country",
)
print("\n===============================")
print("ES Results" + "\n" + "=" * 15)
print_answers(res_4, details="minimum")
# Run only the dense retriever on the full sentence query
res_5 = sklearn_keyword_classifier.run(
query="who are the younger brothers of arya stark ?",
)
print("\n===============================")
print("DPR Results" + "\n" + "=" * 15)
print_answers(res_5, details="minimum")
# Run only the sparse retriever on a keyword based query
res_6 = sklearn_keyword_classifier.run(
query="arya stark younger brothers",
)
print("\n===============================")
print("ES Results" + "\n" + "=" * 15)
print_answers(res_6, details="minimum")
print()
print("Transformer keyword classifier")
print("==============================")
# Here we build the pipeline
transformer_keyword_classifier = Pipeline()
transformer_keyword_classifier.add_node(
component=TransformersQueryClassifier(), name="QueryClassifier", inputs=["Query"]
)
transformer_keyword_classifier.add_node(
component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"]
)
transformer_keyword_classifier.add_node(
component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_2"]
)
transformer_keyword_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
transformer_keyword_classifier.draw("pipeline_classifier.png")
# Run only the dense retriever on the full sentence query
res_1 = transformer_keyword_classifier.run(
query="Who is the father of Arya Stark?",
)
print("\n===============================")
print("DPR Results" + "\n" + "=" * 15)
print_answers(res_1, details="minimum")
# Run only the sparse retriever on a keyword based query
res_2 = transformer_keyword_classifier.run(
query="arya stark father",
)
print("\n===============================")
print("ES Results" + "\n" + "=" * 15)
print_answers(res_2, details="minimum")
# Run only the dense retriever on the full sentence query
res_3 = transformer_keyword_classifier.run(
query="which country was jon snow filmed ?",
)
print("\n===============================")
print("DPR Results" + "\n" + "=" * 15)
print_answers(res_3, details="minimum")
# Run only the sparse retriever on a keyword based query
res_4 = transformer_keyword_classifier.run(
query="jon snow country",
)
print("\n===============================")
print("ES Results" + "\n" + "=" * 15)
print_answers(res_4, details="minimum")
# Run only the dense retriever on the full sentence query
res_5 = transformer_keyword_classifier.run(
query="who are the younger brothers of arya stark ?",
)
print("\n===============================")
print("DPR Results" + "\n" + "=" * 15)
print_answers(res_5, details="minimum")
# Run only the sparse retriever on a keyword based query
res_6 = transformer_keyword_classifier.run(
query="arya stark younger brothers",
)
print("\n===============================")
print("ES Results" + "\n" + "=" * 15)
print_answers(res_6, details="minimum")
print()
print("Transformer question classifier")
print("===============================")
# Here we build the pipeline
transformer_question_classifier = Pipeline()
transformer_question_classifier.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
transformer_question_classifier.add_node(
component=TransformersQueryClassifier(model_name_or_path="shahrukhx01/question-vs-statement-classifier"),
name="QueryClassifier",
inputs=["DPRRetriever"],
)
transformer_question_classifier.add_node(component=reader, name="QAReader", inputs=["QueryClassifier.output_1"])
transformer_question_classifier.draw("question_classifier.png")
# Run only the QA reader on the question query
res_1 = transformer_question_classifier.run(
query="Who is the father of Arya Stark?",
)
print("\n===============================")
print("DPR Results" + "\n" + "=" * 15)
print_answers(res_1, details="minimum")
# Show only DPR results
res_2 = transformer_question_classifier.run(
query="Arya Stark was the daughter of a Lord.",
)
print("\n===============================")
print("ES Results" + "\n" + "=" * 15)
print_answers(res_2, details="minimum")
# Here we create the keyword vs question/statement query classifier
queries = [
"arya stark father",
"jon snow country",
"who is the father of arya stark",
"which country was jon snow filmed?",
]
keyword_classifier = TransformersQueryClassifier()
for query in queries:
result = keyword_classifier.run(query=query)
if result[1] == "output_1":
category = "question/statement"
else:
category = "keyword"
print(f"Query: {query}, raw_output: {result}, class: {category}")
# Here we create the question vs statement query classifier
queries = [
"Lord Eddard was the father of Arya Stark.",
"Jon Snow was filmed in United Kingdom.",
"who is the father of arya stark?",
"Which country was jon snow filmed in?",
]
question_classifier = TransformersQueryClassifier(model_name_or_path="shahrukhx01/question-vs-statement-classifier")
for query in queries:
result = question_classifier.run(query=query)
if result[1] == "output_1":
category = "question"
else:
category = "statement"
print(f"Query: {query}, raw_output: {result}, class: {category}")
if __name__ == "__main__":
tutorial14_query_classifier()
# This Haystack script was made with love by deepset in Berlin, Germany
# Haystack: https://github.com/deepset-ai/haystack
# deepset: https://deepset.ai/