adjusted K-params

app.py

@@ -345,7 +345,7 @@ class BSIChatbot:
             if bm25_retriever is None:
                 bm25_retriever = BM25Retriever.from_documents(allDocs)
             #TODO!
-            retriever_k=15
+            retriever_k=25
             bm25_retriever.k= retriever_k
             vectordb = vectorstore.as_retriever(search_kwargs={"k":retriever_k})
             ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, vectordb], weights=[0.5, 0.5])
@@ -380,7 +380,7 @@ class BSIChatbot:
             #new print(retrieved_chunks_text)
             with torch.no_grad():
                 print("reranking chunks..")
-                retrieved_chunks_text = rerankingModel.rerank(query, retrieved_chunks_text, k=15)
+                retrieved_chunks_text = rerankingModel.rerank(query, retrieved_chunks_text, k=20)
             #newprint("DBG:retrieved_chunks_text after rerank")
             #newprint(type(retrieved_chunks_text))
             #newprint(retrieved_chunks_text)