| # ## Task: Question Answering for Game of Thrones | |
| # | |
| # Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex | |
| # knowledge bases or long documents ("search setting"). | |
| # | |
| # A "knowledge base" could for example be your website, an internal wiki or a collection of financial reports. | |
| # In this tutorial we will work on a slightly different domain: "Game of Thrones". | |
| # | |
| # Let's see how we can use a bunch of Wikipedia articles to answer a variety of questions about the | |
| # marvellous seven kingdoms. | |
| import logging | |
| # We configure how logging messages should be displayed and which log level should be used before importing Haystack. | |
| # Example log message: | |
| # INFO - haystack.utils.preprocessing - Converting data/tutorial1/218_Olenna_Tyrell.txt | |
| # Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily: | |
| logging.basicConfig(format="%(levelname)s - %(name)s - %(message)s", level=logging.WARNING) | |
| logging.getLogger("haystack").setLevel(logging.INFO) | |
| from haystack.document_stores import ElasticsearchDocumentStore | |
| from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http, print_answers, launch_es | |
| from haystack.nodes import FARMReader, TransformersReader, BM25Retriever | |
| def tutorial1_basic_qa_pipeline(): | |
| # ## Document Store | |
| # | |
| # Haystack finds answers to queries within the documents stored in a `DocumentStore`. The current implementations of | |
| # `DocumentStore` include `ElasticsearchDocumentStore`, `FAISSDocumentStore`, `SQLDocumentStore`, and `InMemoryDocumentStore`. | |
| # | |
| # **Here:** We recommended Elasticsearch as it comes preloaded with features like full-text queries, BM25 retrieval, | |
| # and vector storage for text embeddings. | |
| # **Alternatives:** If you are unable to setup an Elasticsearch instance, then follow the Tutorial 3 | |
| # for using SQL/InMemory document stores. | |
| # **Hint**: | |
| # This tutorial creates a new document store instance with Wikipedia articles on Game of Thrones. However, you can | |
| # configure Haystack to work with your existing document stores. | |
| # | |
| # Start an Elasticsearch server | |
| # You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in | |
| # your environment (e.g. in Colab notebooks), then you can manually download and execute Elasticsearch from source. | |
| from haystack.document_stores import InMemoryDocumentStore | |
| document_store = InMemoryDocumentStore() | |
| # ## 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 about Game of Thrones, apply a basic cleaning function, and add | |
| # them in Elasticsearch. | |
| # Let's first fetch some documents that we want to query | |
| # Here: 517 Wikipedia articles for Game of Thrones | |
| doc_dir = "data/tutorial1" | |
| s3_url = "https://aws-ml-blog.s3.amazonaws.com/artifacts/kendra-docs/amazon_help_docs.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 | |
| docs = convert_files_to_docs(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(docs) | |
| # ## Initialize Retriever & Reader | |
| # | |
| # ### Retriever | |
| # | |
| # Retrievers help narrowing down the scope for the Reader to smaller units of text where a given question | |
| # could be answered. | |
| # | |
| # They use some simple but fast algorithm. | |
| # **Here:** We use Elasticsearch's default BM25 algorithm | |
| # **Alternatives:** | |
| # - Customize the `BM25Retriever`with custom queries (e.g. boosting) and filters | |
| # - Use `EmbeddingRetriever` to find candidate documents based on the similarity of | |
| # embeddings (e.g. created via Sentence-BERT) | |
| # - Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging | |
| retriever = BM25Retriever(document_store=document_store) | |
| # Alternative: An in-memory TfidfRetriever based on Pandas dataframes for building quick-prototypes | |
| # with SQLite document store. | |
| # | |
| # from haystack.retriever.tfidf import TfidfRetriever | |
| # 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). | |
| from haystack.pipelines import ExtractiveQAPipeline | |
| pipe = ExtractiveQAPipeline(reader, retriever) | |
| ## Voilà! Ask a question! | |
| prediction = pipe.run( | |
| query="What is Amazon Music?", 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__": | |
| tutorial1_basic_qa_pipeline() | |
| # This Haystack script was made with love by deepset in Berlin, Germany | |
| # Haystack: https://github.com/deepset-ai/haystack | |
| # deepset: https://deepset.ai/ | |