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"""
Haystack Pipelines
"""
import tokenizers
from haystack import Pipeline
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes.retriever import DensePassageRetriever
from haystack.nodes.preprocessor import PreProcessor
import streamlit as st
@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None, tokenizers.AddedToken: lambda _: None}, allow_output_mutation=True)
def dense_passage_retrieval(
index='documents',
query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
):
document_store = InMemoryDocumentStore(index=index)
dpr_retriever = DensePassageRetriever(
document_store=document_store,
query_embedding_model=query_embedding_model,
passage_embedding_model=passage_embedding_model,
)
processor = PreProcessor(
clean_empty_lines=True,
clean_whitespace=True,
clean_header_footer=True,
split_by="word",
split_length=100,
split_respect_sentence_boundary=True,
split_overlap=0,
)
# SEARCH PIPELINE
search_pipeline = Pipeline()
search_pipeline.add_node(dpr_retriever, name="DPRRetriever", inputs=["Query"])
# INDEXING PIPELINE
index_pipeline = Pipeline()
index_pipeline.add_node(processor, name="Preprocessor", inputs=["File"])
index_pipeline.add_node(dpr_retriever, name="DPRRetriever", inputs=["Preprocessor"])
index_pipeline.add_node(
document_store, name="DocumentStore", inputs=["DPRRetriever"]
)
return search_pipeline, index_pipeline |