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116 lines
5.4 KiB
Python
116 lines
5.4 KiB
Python
# ## Task: Build a Question Answering pipeline without Elasticsearch
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#
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# Haystack provides alternatives to Elasticsearch for developing quick prototypes.
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#
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# You can use an `InMemoryDocumentStore` or a `SQLDocumentStore`(with SQLite) as the document store.
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#
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# If you are interested in more feature-rich Elasticsearch, then please refer to the Tutorial 1.
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from haystack import Finder
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from haystack.document_store.memory import InMemoryDocumentStore
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from haystack.document_store.sql import SQLDocumentStore
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from haystack.preprocessor.cleaning import clean_wiki_text
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from haystack.preprocessor.utils import convert_files_to_dicts, fetch_archive_from_http
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from haystack.reader.farm import FARMReader
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from haystack.reader.transformers import TransformersReader
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from haystack.retriever.sparse import TfidfRetriever
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from haystack.utils import print_answers
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def tutorial3_basic_qa_pipeline_without_elasticsearch():
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# In-Memory Document Store
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document_store = InMemoryDocumentStore()
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# or, alternatively, SQLite Document Store
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# document_store = SQLDocumentStore(url="sqlite:///qa.db")
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# ## Preprocessing of documents
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#
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# Haystack provides a customizable pipeline for:
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# - converting files into texts
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# - cleaning texts
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# - splitting texts
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# - writing them to a Document Store
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# In this tutorial, we download Wikipedia articles on Game of Thrones, apply a basic cleaning function, and index
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# them in Elasticsearch.
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# Let's first get some documents that we want to query
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# Here: 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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dicts = convert_files_to_dicts(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
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# You can optionally supply a cleaning function that is applied to each doc (e.g. to remove footers)
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# It must take a str as input, and return a str.
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# Now, let's write the docs to our DB.
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document_store.write_documents(dicts)
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# ## Initalize Retriever, Reader, & Finder
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#
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# ### Retriever
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#
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# Retrievers help narrowing down the scope for the Reader to smaller units of text where
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# a given question could be answered.
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#
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# With InMemoryDocumentStore or SQLDocumentStore, you can use the TfidfRetriever. For more
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# retrievers, please refer to the tutorial-1.
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# An in-memory TfidfRetriever based on Pandas dataframes
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retriever = TfidfRetriever(document_store=document_store)
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# ### Reader
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#
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# A Reader scans the texts returned by retrievers in detail and extracts the k best answers. They are based
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# on powerful, but slower deep learning models.
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#
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# Haystack currently supports Readers based on the frameworks FARM and Transformers.
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# With both you can either load a local model or one from Hugging Face's model hub (https://huggingface.co/models).
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# **Here:** a medium sized RoBERTa QA model using a Reader based on
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# FARM (https://huggingface.co/deepset/roberta-base-squad2)
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# **Alternatives (Reader):** TransformersReader (leveraging the `pipeline` of the Transformers package)
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# **Alternatives (Models):** e.g. "distilbert-base-uncased-distilled-squad" (fast) or
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# "deepset/bert-large-uncased-whole-word-masking-squad2" (good accuracy)
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# **Hint:** You can adjust the model to return "no answer possible" with the no_ans_boost.
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# Higher values mean the model prefers "no answer possible".
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# #### FARMReader
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#
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# Load a local model or any of the QA models on
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# Hugging Face's model hub (https://huggingface.co/models)
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reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
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# #### TransformersReader
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# Alternative:
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# reader = TransformersReader(model_name_or_path="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1)
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# ### Pipeline
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#
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# With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.
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# Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.
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# 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.
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# You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd).
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from haystack.pipeline import ExtractiveQAPipeline
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pipe = ExtractiveQAPipeline(reader, retriever)
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## Voilà! Ask a question!
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prediction = pipe.run(query="Who is the father of Arya Stark?", top_k_retriever=10, top_k_reader=5)
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# prediction = pipe.run(query="Who created the Dothraki vocabulary?", top_k_reader=5)
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# prediction = pipe.run(query="Who is the sister of Sansa?", top_k_reader=5)
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print_answers(prediction, details="minimal")
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if __name__ == "__main__":
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tutorial3_basic_qa_pipeline_without_elasticsearch()
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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/ |