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c102b152dc
* added hybrid search example Added an example about hybrid search for faq pipeline on covid dataset * formatted with back formatter * renamed document * fixed * fixed typos * added test added test for hybrid search * fixed withespaces * removed test for hybrid search * fixed pylint * commented logging * updated hybrid search example * release notes * Update hybrid_search_faq_pipeline.py-815df846dca7e872.yaml * Update hybrid_search_faq_pipeline.py * mention hybrid search example in release notes * reduce installed dependencies in examples test workflow * do not install cuda dependencies * skip models if API key not set; delete document indices * skip models if API key not set; delete document indices * skip models if API key not set; delete document indices * keep roberta-base model and inference extra * pylint * disable pylint no-logging-basicconfig rule --------- Co-authored-by: Julian Risch <julian.risch@deepset.ai>
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
# Disable pylint errors for logging basicConfig
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# pylint: disable=no-logging-basicconfig
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import logging
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import pandas as pd
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from haystack.document_stores import ElasticsearchDocumentStore
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from haystack.nodes import EmbeddingRetriever
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from haystack.nodes.other.docs2answers import Docs2Answers
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from haystack.pipelines import Pipeline
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from haystack.utils import fetch_archive_from_http, launch_es, print_answers
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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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def basic_faq_pipeline():
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document_store = ElasticsearchDocumentStore(
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host="localhost",
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username="",
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password="",
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index="example-document",
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embedding_field="question_emb",
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embedding_dim=384,
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excluded_meta_data=["question_emb"],
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similarity="cosine",
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)
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retriever = EmbeddingRetriever(
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document_store=document_store,
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embedding_model="sentence-transformers/all-MiniLM-L6-v2",
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use_gpu=True,
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scale_score=False,
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)
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doc_to_answers = Docs2Answers()
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doc_dir = "data/basic_faq_pipeline"
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s3_url = "https://core-engineering.s3.eu-central-1.amazonaws.com/public/scripts/small_faq_covid.csv1.zip"
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fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
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df = pd.read_csv(f"{doc_dir}/small_faq_covid.csv")
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# Minimal cleaning
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df.fillna(value="", inplace=True)
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df["question"] = df["question"].apply(lambda x: x.strip())
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print(df.head())
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# Get embeddings for our questions from the FAQs
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questions = list(df["question"].values)
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df["question_emb"] = retriever.embed_queries(queries=questions).tolist()
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df = df.rename(columns={"question": "content"})
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# Convert Dataframe to list of dicts and index them in our DocumentStore
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docs_to_index = df.to_dict(orient="records")
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document_store.write_documents(docs_to_index)
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document_store.update_embeddings(retriever)
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# Initialize a Pipeline (this time without a reader) and ask questions
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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=doc_to_answers, name="Docs2Answers", inputs=["Retriever"])
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# Ask a question
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prediction = pipeline.run(query="How is the virus spreading?", params={"Retriever": {"top_k": 10}})
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print_answers(prediction, details="medium")
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# Remove the index once we're done to save space
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document_store.delete_index(index="example-document")
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return prediction
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if __name__ == "__main__":
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launch_es()
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basic_faq_pipeline()
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