Spaces:
Runtime error
Runtime error
File size: 2,626 Bytes
01b8e8e 101be32 01b8e8e 101be32 01b8e8e 101be32 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
"""
Haystack Pipelines
"""
import tokenizers
from haystack import Pipeline
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes.retriever import DensePassageRetriever, TfidfRetriever
from haystack.nodes.preprocessor import PreProcessor
import streamlit as st
@st.cache(allow_output_mutation=True)
def keyword_search(
index='documents',
):
document_store = InMemoryDocumentStore(index=index)
keyword_retriever = TfidfRetriever(document_store=(document_store))
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(keyword_retriever, name="TfidfRetriever", inputs=["Query"])
# INDEXING PIPELINE
index_pipeline = Pipeline()
index_pipeline.add_node(processor, name="Preprocessor", inputs=["File"])
index_pipeline.add_node(keyword_retriever, name="TfidfRetriever", inputs=["Preprocessor"])
index_pipeline.add_node(
document_store, name="DocumentStore", inputs=["TfidfRetriever"]
)
return search_pipeline, index_pipeline
@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
|