Update app.py

app.py

@@ -64,12 +64,22 @@ bnb_config = BitsAndBytesConfig(
 model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-Instruct-v0.1",quantization_config=bnb_config,
 )
-new_db = FAISS.load_local("faiss_index", HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'),allow_dangerous_deserialization=True)
+dataset= load_dataset("mery22/testub")
+loader = PyPDFLoader(dataset)
+data = loader.load()
+text_splitter1 = CharacterTextSplitter(chunk_size=512, chunk_overlap=0,separator="\n\n")
+texts = text_splitter1.split_documents(data)
+db = FAISS.from_documents(texts,
+                          HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'))
+
+
 # Connect query to FAISS index using a retriever
-retriever = new_db.as_retriever(
+retriever = db.as_retriever(
     search_type="mmr",
     search_kwargs={'k': 1}
 )
+
+
 from langchain.llms import HuggingFacePipeline
 from langchain.prompts import PromptTemplate
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings