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92 lines
4.2 KiB
Python
Executable File
92 lines
4.2 KiB
Python
Executable File
from haystack.document_stores import FAISSDocumentStore, MilvusDocumentStore
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from haystack.utils import (
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clean_wiki_text,
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print_answers,
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launch_milvus,
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convert_files_to_dicts,
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fetch_archive_from_http,
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)
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from haystack.nodes import FARMReader, DensePassageRetriever
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def tutorial6_better_retrieval_via_dpr():
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# OPTION 1: FAISS is a library for efficient similarity search on a cluster of dense vectors.
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# The FAISSDocumentStore uses a SQL(SQLite in-memory be default) document store under-the-hood
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# to store the document text and other meta data. The vector embeddings of the text are
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# indexed on a FAISS Index that later is queried for searching answers.
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# The default flavour of FAISSDocumentStore is "Flat" but can also be set to "HNSW" for
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# faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.
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# For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
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# Do not forget to install its dependencies with `pip install farm-haystack[faiss]`
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document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
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# OPTION2: Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.
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# Like FAISS it has both a "Flat" and "HNSW" mode but it outperforms FAISS when it comes to dynamic data management.
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# It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.
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# See https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md
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# Do not forget to install its dependencies with `pip install farm-haystack[milvus1]`
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# launch_milvus()
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# document_store = MilvusDocumentStore()
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# ## Preprocessing of documents
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# Let's first get some documents that we want to query
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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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# Now, let's write the docs to our DB.
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document_store.write_documents(dicts)
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### Retriever
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retriever = DensePassageRetriever(
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document_store=document_store,
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query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
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passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
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max_seq_len_query=64,
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max_seq_len_passage=256,
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batch_size=2,
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use_gpu=True,
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embed_title=True,
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use_fast_tokenizers=True,
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)
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# Important:
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# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
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# previously indexed documents and update their embedding representation.
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# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
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# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
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document_store.update_embeddings(retriever)
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### Reader
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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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### Pipeline
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from haystack.pipelines 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(
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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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# prediction = pipe.run(query="Who created the Dothraki vocabulary?", params={"Reader": {"top_k": 5}})
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# prediction = pipe.run(query="Who is the sister of Sansa?", params={"Reader": {"top_k": 5}})
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print_answers(prediction, details="minimum")
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if __name__ == "__main__":
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tutorial6_better_retrieval_via_dpr()
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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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