Update app.py

app.py

@@ -5,8 +5,8 @@ import torch
 from tqdm.auto import tqdm
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 
-
-from langchain.vectorstores import Chroma
+# from langchain.vectorstores import Chroma
+from langchain.vectorstores import FAISS
 from langchain.embeddings import HuggingFaceInstructEmbeddings
 
 
@@ -44,7 +44,16 @@ st.markdown(f"Number of chunked texts: {len(chunked_text)}")
 embedding_model = HuggingFaceInstructEmbeddings(model_name='hkunlp/instructor-base',
                                                 model_kwargs = {'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu')})
 
-db_chunk_500 = Chroma.from_documents(documents= chunked_text,
-                           embedding= embedding_model)
+vector_database = FAISS.load_local("faiss_index", embedding_model)
+print("load done")
+
+
+
+
+query_input = st.text_input(label= 'your question')
+def retrieve_document(query_input):
+    related_doc = vector_database.similarity_search(query_input)
+    return related_doc
 
-print("load done")
+output = st.text_area(label = "Here is the relevant documents",
+                      value = retrieve_document(query_input))