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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>
85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
"""
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This script performs the same query benchmarking as `retriever.py` but with less of the loops that iterate
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over all the parameters so that it is easier to inspect what is happening
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"""
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from haystack.document_stores import MilvusDocumentStore, FAISSDocumentStore
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from haystack.nodes import DensePassageRetriever
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from retriever import prepare_data
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import datetime
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from pprint import pprint
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from milvus import IndexType
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from utils import get_document_store
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def benchmark_querying(index_type, n_docs=100_000, similarity="dot_product"):
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doc_index = "document"
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label_index = "label"
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docs, labels = prepare_data(
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data_dir="data/",
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filename_gold="nq2squad-dev.json",
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filename_negative="psgs_w100_minus_gold_100k.tsv",
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remote_url="https://ext-haystack-retriever-eval.s3-eu-west-1.amazonaws.com/",
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embeddings_filenames=["wikipedia_passages_100k.pkl"],
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embeddings_dir="embeddings/",
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n_docs=n_docs,
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add_precomputed=True,
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)
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doc_store = get_document_store(document_store_type=index_type, similarity=similarity)
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# if index_type == "milvus_flat":
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# doc_store = MilvusDocumentStore(index=doc_index, similarity=similarity)
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# elif index_type == "milvus_hnsw":
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# index_param = {"M": 64, "efConstruction": 80}
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# search_param = {"ef": 20}
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# doc_store = MilvusDocumentStore(
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# index=doc_index,
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# index_type=IndexType.HNSW,
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# index_param=index_param,
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# search_param=search_param,
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# similarity=similarity
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# )
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doc_store.write_documents(documents=docs, index=doc_index)
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doc_store.write_labels(labels=labels, index=label_index)
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retriever = DensePassageRetriever(
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document_store=doc_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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use_gpu=True,
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use_fast_tokenizers=True,
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)
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raw_results = retriever.eval(label_index=label_index, doc_index=doc_index)
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results = {
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"n_queries": raw_results["n_questions"],
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"retrieve_time": raw_results["retrieve_time"],
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"queries_per_second": raw_results["n_questions"] / raw_results["retrieve_time"],
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"seconds_per_query": raw_results["retrieve_time"] / raw_results["n_questions"],
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"recall": raw_results["recall"] * 100,
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"map": raw_results["map"] * 100,
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"top_k": raw_results["top_k"],
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"date_time": datetime.datetime.now(),
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"error": None,
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}
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pprint(results)
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doc_store.delete_all_documents(index=doc_index)
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doc_store.delete_all_documents(index=label_index)
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if __name__ == "__main__":
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similarity = "l2"
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n_docs = 1000
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benchmark_querying(index_type="milvus_flat", similarity=similarity, n_docs=n_docs)
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benchmark_querying(index_type="milvus_hnsw", similarity=similarity, n_docs=n_docs)
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benchmark_querying(index_type="faiss_flat", similarity=similarity, n_docs=n_docs)
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benchmark_querying(index_type="faiss_hnsw", similarity=similarity, n_docs=n_docs)
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