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75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
import logging
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from pathlib import Path
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logging.basicConfig(format="%(levelname)s - %(name)s - %(message)s", level=logging.WARNING)
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logging.getLogger("haystack").setLevel(logging.INFO)
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from haystack.document_stores import ElasticsearchDocumentStore
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from haystack.utils import fetch_archive_from_http, print_answers, launch_es
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from haystack.nodes import FARMReader, BM25Retriever
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from haystack.nodes.file_classifier import FileTypeClassifier
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from haystack.nodes.preprocessor import PreProcessor
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from haystack.nodes.file_converter import TextConverter
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from haystack.pipelines import Pipeline
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def basic_qa_pipeline():
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# Initialize a DocumentStore
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document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
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# fetch, pre-process and write documents
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doc_dir = "data/basic_qa_pipeline"
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s3_url = "https://core-engineering.s3.eu-central-1.amazonaws.com/public/scripts/wiki_gameofthrones_txt1.zip"
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fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
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file_paths = [p for p in Path(doc_dir).glob("**/*")]
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files_metadata = [{"name": path.name} for path in file_paths]
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# Indexing Pipeline
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indexing_pipeline = Pipeline()
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# Makes sure the file is a TXT file (FileTypeClassifier node)
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classifier = FileTypeClassifier()
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indexing_pipeline.add_node(classifier, name="Classifier", inputs=["File"])
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# Converts a file into text and performs basic cleaning (TextConverter node)
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text_converter = TextConverter(remove_numeric_tables=True)
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indexing_pipeline.add_node(text_converter, name="Text_converter", inputs=["Classifier.output_1"])
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# - Pre-processes the text by performing splits and adding metadata to the text (Preprocessor node)
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preprocessor = PreProcessor(
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clean_whitespace=True,
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clean_empty_lines=True,
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split_length=100,
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split_overlap=50,
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split_respect_sentence_boundary=True,
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)
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indexing_pipeline.add_node(preprocessor, name="Preprocessor", inputs=["Text_converter"])
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# - Writes the resulting documents into the document store
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indexing_pipeline.add_node(document_store, name="Document_Store", inputs=["Preprocessor"])
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# Then we run it with the documents and their metadata as input
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indexing_pipeline.run(file_paths=file_paths, meta=files_metadata)
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# Initialize Retriever & Reader
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retriever = BM25Retriever(document_store=document_store)
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reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
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# Query Pipeline
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pipeline = Pipeline()
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pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
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pipeline.add_node(component=reader, name="Reader", inputs=["Retriever"])
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prediction = pipeline.run(
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query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
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)
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print_answers(prediction, details="minimum")
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return prediction
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if __name__ == "__main__":
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launch_es()
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basic_qa_pipeline()
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