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* 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>
244 lines
9.1 KiB
Python
244 lines
9.1 KiB
Python
import os
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from subprocess import Popen, PIPE, STDOUT
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from haystack.utils import fetch_archive_from_http, convert_files_to_dicts, clean_wiki_text, launch_es, print_answers
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from haystack.pipelines import Pipeline, RootNode
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from haystack.document_stores import ElasticsearchDocumentStore
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from haystack.nodes import (
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ElasticsearchRetriever,
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DensePassageRetriever,
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FARMReader,
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TransformersQueryClassifier,
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SklearnQueryClassifier,
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)
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def tutorial14_query_classifier():
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# Download and prepare data - 517 Wikipedia articles for Game of Thrones
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doc_dir = "data/article_txt_got"
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s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt.zip"
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fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
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# convert files to dicts containing documents that can be indexed to our datastore
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got_dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
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# Initialize DocumentStore and index documents
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launch_es()
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document_store = ElasticsearchDocumentStore()
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document_store.delete_documents()
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document_store.write_documents(got_dicts)
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# Initialize Sparse retriever
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es_retriever = ElasticsearchRetriever(document_store=document_store)
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# Initialize dense retriever
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dpr_retriever = DensePassageRetriever(document_store)
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document_store.update_embeddings(dpr_retriever, update_existing_embeddings=False)
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reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
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print()
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print("Sklearn keyword classifier")
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print("==========================")
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# Here we build the pipeline
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sklearn_keyword_classifier = Pipeline()
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sklearn_keyword_classifier.add_node(component=SklearnQueryClassifier(), name="QueryClassifier", inputs=["Query"])
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sklearn_keyword_classifier.add_node(
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component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"]
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)
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sklearn_keyword_classifier.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_2"])
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sklearn_keyword_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
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sklearn_keyword_classifier.draw("pipeline_classifier.png")
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# Run only the dense retriever on the full sentence query
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res_1 = sklearn_keyword_classifier.run(
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query="Who is the father of Arya Stark?",
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)
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print("\n===============================")
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print("DPR Results" + "\n" + "=" * 15)
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print_answers(res_1, details="minimum")
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# Run only the sparse retriever on a keyword based query
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res_2 = sklearn_keyword_classifier.run(
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query="arya stark father",
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)
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print("\n===============================")
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print("ES Results" + "\n" + "=" * 15)
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print_answers(res_2, details="minimum")
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# Run only the dense retriever on the full sentence query
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res_3 = sklearn_keyword_classifier.run(
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query="which country was jon snow filmed ?",
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)
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print("\n===============================")
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print("DPR Results" + "\n" + "=" * 15)
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print_answers(res_3, details="minimum")
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# Run only the sparse retriever on a keyword based query
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res_4 = sklearn_keyword_classifier.run(
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query="jon snow country",
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)
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print("\n===============================")
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print("ES Results" + "\n" + "=" * 15)
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print_answers(res_4, details="minimum")
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# Run only the dense retriever on the full sentence query
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res_5 = sklearn_keyword_classifier.run(
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query="who are the younger brothers of arya stark ?",
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)
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print("\n===============================")
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print("DPR Results" + "\n" + "=" * 15)
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print_answers(res_5, details="minimum")
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# Run only the sparse retriever on a keyword based query
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res_6 = sklearn_keyword_classifier.run(
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query="arya stark younger brothers",
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)
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print("\n===============================")
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print("ES Results" + "\n" + "=" * 15)
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print_answers(res_6, details="minimum")
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print()
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print("Transformer keyword classifier")
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print("==============================")
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# Here we build the pipeline
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transformer_keyword_classifier = Pipeline()
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transformer_keyword_classifier.add_node(
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component=TransformersQueryClassifier(), name="QueryClassifier", inputs=["Query"]
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)
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transformer_keyword_classifier.add_node(
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component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_1"]
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)
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transformer_keyword_classifier.add_node(
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component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_2"]
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)
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transformer_keyword_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
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transformer_keyword_classifier.draw("pipeline_classifier.png")
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# Run only the dense retriever on the full sentence query
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res_1 = transformer_keyword_classifier.run(
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query="Who is the father of Arya Stark?",
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)
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print("\n===============================")
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print("DPR Results" + "\n" + "=" * 15)
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print_answers(res_1, details="minimum")
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# Run only the sparse retriever on a keyword based query
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res_2 = transformer_keyword_classifier.run(
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query="arya stark father",
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)
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print("\n===============================")
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print("ES Results" + "\n" + "=" * 15)
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print_answers(res_2, details="minimum")
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# Run only the dense retriever on the full sentence query
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res_3 = transformer_keyword_classifier.run(
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query="which country was jon snow filmed ?",
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)
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print("\n===============================")
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print("DPR Results" + "\n" + "=" * 15)
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print_answers(res_3, details="minimum")
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# Run only the sparse retriever on a keyword based query
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res_4 = transformer_keyword_classifier.run(
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query="jon snow country",
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)
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print("\n===============================")
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print("ES Results" + "\n" + "=" * 15)
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print_answers(res_4, details="minimum")
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# Run only the dense retriever on the full sentence query
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res_5 = transformer_keyword_classifier.run(
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query="who are the younger brothers of arya stark ?",
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)
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print("\n===============================")
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print("DPR Results" + "\n" + "=" * 15)
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print_answers(res_5, details="minimum")
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# Run only the sparse retriever on a keyword based query
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res_6 = transformer_keyword_classifier.run(
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query="arya stark younger brothers",
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)
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print("\n===============================")
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print("ES Results" + "\n" + "=" * 15)
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print_answers(res_6, details="minimum")
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print()
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print("Transformer question classifier")
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print("===============================")
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# Here we build the pipeline
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transformer_question_classifier = Pipeline()
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transformer_question_classifier.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
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transformer_question_classifier.add_node(
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component=TransformersQueryClassifier(model_name_or_path="shahrukhx01/question-vs-statement-classifier"),
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name="QueryClassifier",
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inputs=["DPRRetriever"],
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)
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transformer_question_classifier.add_node(component=reader, name="QAReader", inputs=["QueryClassifier.output_1"])
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transformer_question_classifier.draw("question_classifier.png")
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# Run only the QA reader on the question query
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res_1 = transformer_question_classifier.run(
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query="Who is the father of Arya Stark?",
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)
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print("\n===============================")
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print("DPR Results" + "\n" + "=" * 15)
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print_answers(res_1, details="minimum")
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# Show only DPR results
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res_2 = transformer_question_classifier.run(
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query="Arya Stark was the daughter of a Lord.",
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)
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print("\n===============================")
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print("ES Results" + "\n" + "=" * 15)
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print_answers(res_2, details="minimum")
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# Here we create the keyword vs question/statement query classifier
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queries = [
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"arya stark father",
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"jon snow country",
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"who is the father of arya stark",
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"which country was jon snow filmed?",
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]
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keyword_classifier = TransformersQueryClassifier()
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for query in queries:
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result = keyword_classifier.run(query=query)
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if result[1] == "output_1":
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category = "question/statement"
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else:
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category = "keyword"
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print(f"Query: {query}, raw_output: {result}, class: {category}")
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# Here we create the question vs statement query classifier
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queries = [
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"Lord Eddard was the father of Arya Stark.",
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"Jon Snow was filmed in United Kingdom.",
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"who is the father of arya stark?",
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"Which country was jon snow filmed in?",
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]
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question_classifier = TransformersQueryClassifier(model_name_or_path="shahrukhx01/question-vs-statement-classifier")
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for query in queries:
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result = question_classifier.run(query=query)
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if result[1] == "output_1":
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category = "question"
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else:
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category = "statement"
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print(f"Query: {query}, raw_output: {result}, class: {category}")
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if __name__ == "__main__":
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tutorial14_query_classifier()
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# This Haystack script was made with love by deepset in Berlin, Germany
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# Haystack: https://github.com/deepset-ai/haystack
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# deepset: https://deepset.ai/
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