test

app.py

@@ -7,10 +7,11 @@ embeddings = SentenceTransformerEmbeddings(model_name="multi-qa-MiniLM-L6-cos-v1
 db = FAISS.load_local('faiss_qa2', embeddings)
 
 def main(query):
-    result_docs = db.similarity_search_with_score(query, k=20)
+    result_docs = db.similarity_search_with_score(query, k=100)
     sentences = [doc[0].page_content for doc in result_docs]
-    for doc in result_docs:
-        print(doc[0].metadata['articleId'], ' | ', doc[0].page_content, ' | ', doc[0].metadata['answer'])
+    # print('----------------------------------------------------------------')
+    # for doc in result_docs:
+    #     print(doc[0].metadata['articleId'], ' | ', doc[0].page_content, ' | ', doc[0].metadata['answer'])
     score, index = reranking.search(query, sentences)
     return result_docs[index][0].metadata['answer'], score, result_docs[index][0].page_content