Update app.py

app.py

@@ -47,29 +47,29 @@ def get_text_chunks(text):
     return chunks
 
 
-def get_vectorstore(text_chunks):
-    # embeddings = OpenAIEmbeddings()
+# def get_vectorstore(text_chunks):
+#     # embeddings = OpenAIEmbeddings()
 
-    #  pip install InstructorEmbedding
-    #  pip install sentence-transformers==2.2.2
-    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+#     #  pip install InstructorEmbedding
+#     #  pip install sentence-transformers==2.2.2
+#     embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
 
-    #  from InstructorEmbedding import INSTRUCTOR
-    # model = INSTRUCTOR('hkunlp/instructor-xl')
-    # sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
-    # instruction = "Represent the Science title:"
-    # embeddings = model.encode([[instruction, sentence]])
+#     #  from InstructorEmbedding import INSTRUCTOR
+#     # model = INSTRUCTOR('hkunlp/instructor-xl')
+#     # sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
+#     # instruction = "Represent the Science title:"
+#     # embeddings = model.encode([[instruction, sentence]])
 
-    # embeddings = model.encode(text_chunks)
-    print('have Embeddings:   ')
+#     # embeddings = model.encode(text_chunks)
+#     print('have Embeddings:   ')
 
-    # text_chunks="this is a test"
-    #   FAISS,  Chroma and other vector databases
-    #
-    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    print('FAISS succeeds:   ')
+#     # text_chunks="this is a test"
+#     #   FAISS,  Chroma and other vector databases
+#     #
+#     vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+#     print('FAISS succeeds:   ')
 
-    return vectorstore
+#     return vectorstore
 
 # def get_conversation_chain(vectorstore):
 #     # llm = ChatOpenAI()