Spaces:
Running
Running
File size: 2,950 Bytes
4c2a969 1434337 4c2a969 1434337 4c2a969 1434337 4c2a969 1434337 4c2a969 1434337 4c2a969 1434337 4c2a969 1434337 4c2a969 1434337 4c2a969 1434337 4c2a969 35f0167 4c2a969 35f0167 1434337 4c2a969 1434337 4c2a969 1434337 |
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 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
import shutil
from haystack.document_stores import FAISSDocumentStore
from haystack.nodes import EmbeddingRetriever
from haystack.pipelines import Pipeline
import streamlit as st
from app_utils.entailment_checker import EntailmentChecker
from app_utils.config import (
STATEMENTS_PATH,
INDEX_DIR,
RETRIEVER_MODEL,
RETRIEVER_MODEL_FORMAT,
NLI_MODEL,
)
@st.cache()
def load_statements():
"""Load statements from file"""
with open(STATEMENTS_PATH) as fin:
statements = [
line.strip() for line in fin.readlines() if not line.startswith("#")
]
return statements
# cached to make index and models load only at start
@st.cache(
hash_funcs={"builtins.SwigPyObject": lambda _: None}, allow_output_mutation=True
)
def start_haystack():
"""
load document store, retriever, reader and create pipeline
"""
shutil.copy(f"{INDEX_DIR}/faiss_document_store.db", ".")
document_store = FAISSDocumentStore(
faiss_index_path=f"{INDEX_DIR}/my_faiss_index.faiss",
faiss_config_path=f"{INDEX_DIR}/my_faiss_index.json",
)
print(f"Index size: {document_store.get_document_count()}")
retriever = EmbeddingRetriever(
document_store=document_store,
embedding_model=RETRIEVER_MODEL,
model_format=RETRIEVER_MODEL_FORMAT,
)
entailment_checker = EntailmentChecker(model_name_or_path=NLI_MODEL, use_gpu=False)
pipe = Pipeline()
pipe.add_node(component=retriever, name="retriever", inputs=["Query"])
pipe.add_node(component=entailment_checker, name="ec", inputs=["retriever"])
return pipe
pipe = start_haystack()
# the pipeline is not included as parameter of the following function,
# because it is difficult to cache
@st.cache(persist=True, allow_output_mutation=True)
def query(statement: str, retriever_top_k: int = 5):
"""Run query and verify statement"""
params = {"retriever": {"top_k": retriever_top_k}}
results = pipe.run(statement, params=params)
scores, agg_con, agg_neu, agg_ent = 0, 0, 0, 0
for i, doc in enumerate(results["documents"]):
scores += doc.score
ent_info = doc.meta["entailment_info"]
con, neu, ent = (
ent_info["contradiction"],
ent_info["neutral"],
ent_info["entailment"],
)
agg_con += con * doc.score
agg_neu += neu * doc.score
agg_ent += ent * doc.score
# if in the first 3 documents there is a strong evidence of entailment/contradiction,
# there is non need to consider less relevant documents
if i == 2 and max(agg_con, agg_ent) / scores > 0.5:
results["documents"] = results["documents"][: i + 1]
break
results["agg_entailment_info"] = {
"contradiction": round(agg_con / scores, 2),
"neutral": round(agg_neu / scores, 2),
"entailment": round(agg_ent / scores, 2),
}
return results
|