mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-10-30 17:29:29 +00:00
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>
86 lines
3.3 KiB
Python
86 lines
3.3 KiB
Python
# import logging
|
|
|
|
import pandas as pd
|
|
|
|
from haystack.document_stores import ElasticsearchDocumentStore
|
|
from haystack.nodes import EmbeddingRetriever, BM25Retriever, JoinDocuments, SentenceTransformersRanker
|
|
from haystack.nodes.other.docs2answers import Docs2Answers
|
|
from haystack.utils import launch_es, print_answers, fetch_archive_from_http
|
|
from haystack.pipelines import Pipeline
|
|
|
|
# logging.basicConfig(format="%(levelname)s - %(name)s - %(message)s", level=logging.WARNING)
|
|
# logging.getLogger("haystack").setLevel(logging.INFO)
|
|
|
|
|
|
def hybrid_search_faq_pipeline():
|
|
document_store = ElasticsearchDocumentStore(
|
|
host="localhost",
|
|
username="",
|
|
password="",
|
|
index="document",
|
|
embedding_field="question_emb",
|
|
embedding_dim=384,
|
|
excluded_meta_data=["question_emb"],
|
|
similarity="cosine",
|
|
)
|
|
|
|
sparse_retriever = BM25Retriever(document_store=document_store)
|
|
dense_retriever = EmbeddingRetriever(
|
|
document_store=document_store,
|
|
embedding_model="sentence-transformers/all-MiniLM-L6-v2",
|
|
use_gpu=True,
|
|
scale_score=False,
|
|
)
|
|
join_documents = JoinDocuments(join_mode="reciprocal_rank_fusion")
|
|
rerank = SentenceTransformersRanker(model_name_or_path="cross-encoder/ms-marco-MiniLM-L-6-v2")
|
|
|
|
doc_to_answers = Docs2Answers()
|
|
|
|
doc_dir = "data/basic_faq_pipeline"
|
|
s3_url = "https://core-engineering.s3.eu-central-1.amazonaws.com/public/scripts/small_faq_covid.csv1.zip"
|
|
fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
|
|
|
|
df = pd.read_csv(f"{doc_dir}/small_faq_covid.csv")
|
|
|
|
# Minimal cleaning
|
|
df.fillna(value="", inplace=True)
|
|
df["question"] = df["question"].apply(lambda x: x.strip())
|
|
print(df.head())
|
|
|
|
# Get embeddings for our questions from the FAQs
|
|
questions = list(df["question"].values)
|
|
df["question_emb"] = dense_retriever.embed_queries(queries=questions).tolist()
|
|
df = df.rename(columns={"question": "content"})
|
|
|
|
# Convert Dataframe to list of dicts and index them in our DocumentStore
|
|
docs_to_index = df.to_dict(orient="records")
|
|
document_store.write_documents(docs_to_index)
|
|
document_store.update_embeddings(retriever=dense_retriever)
|
|
|
|
# Initialize a Pipeline (this time without a reader) and ask questions
|
|
pipeline = Pipeline()
|
|
pipeline.add_node(component=sparse_retriever, name="SparseRetriever", inputs=["Query"])
|
|
pipeline.add_node(component=dense_retriever, name="DenseRetriever", inputs=["Query"])
|
|
pipeline.add_node(component=join_documents, name="JoinDocuments", inputs=["SparseRetriever", "DenseRetriever"])
|
|
pipeline.add_node(component=rerank, name="ReRanker", inputs=["JoinDocuments"])
|
|
pipeline.add_node(component=doc_to_answers, name="Docs2Answers", inputs=["ReRanker"])
|
|
|
|
# Ask a question
|
|
prediction = pipeline.run(
|
|
query="How is the virus spreading?",
|
|
params={
|
|
"SparseRetriever": {"top_k": 10},
|
|
"DenseRetriever": {"top_k": 10},
|
|
"JoinDocuments": {"top_k_join": 15},
|
|
"ReRanker": {"top_k": 5},
|
|
},
|
|
)
|
|
|
|
print_answers(prediction, details="medium")
|
|
return prediction
|
|
|
|
|
|
if __name__ == "__main__":
|
|
launch_es()
|
|
hybrid_search_faq_pipeline()
|