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from langchain.vectorstores import FAISS
from langchain.embeddings import SentenceTransformerEmbeddings
import gradio as gr
import reranking
#from extract_keywords import init_keyword_extractor, extract_keywords
from extract_keywords import extract_keywords2
embeddings = SentenceTransformerEmbeddings(model_name="multi-qa-MiniLM-L6-cos-v1")
db = FAISS.load_local('faiss_qa_2023-08-20', embeddings)
def search_filter_function(query_keywords):
def fn(doc):
doc_keywords = extract_keywords2(doc[0].page_content)[0]
intersection_keywords = doc_keywords.intersection(query_keywords)
if len(query_keywords) == 0:
return len(doc_keywords) == 0
else:
return len(intersection_keywords) >= len(query_keywords)
return fn
def main(query):
query = query.lower()
query_keywords, query = extract_keywords2(query)
result_docs = db.similarity_search_with_score(query, k=50)
if len(query_keywords) > 0:
result_docs = list(filter(search_filter_function(query_keywords), result_docs))
if len(result_docs) == 0:
return 'Ответ не найден', 0, ''
sentences = [doc[0].page_content for doc in result_docs]
score, index = reranking.search(query, sentences)
return result_docs[index][0].metadata['answer'], score, result_docs[index][0].page_content
demo = gr.Interface(fn=main, inputs="text", outputs=[
gr.Textbox(label="Ответ, который будет показан клиенту"),
gr.Textbox(label="Score"),
gr.Textbox(label="Вопрос, по которому был найден ответ"),
])
demo.launch()
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