Update app.py

app.py

@@ -25,16 +25,9 @@ hf = HuggingFaceHubEmbeddings(
     huggingfacehub_api_token= HUGGINGFACEHUB_API_TOKEN,
 )
 
-EMB_SBERT_MPNET_BASE = "sentence-transformers/all-mpnet-base-v2"
-config = {"persist_directory":None,
-          "load_in_8bit":False,
-          "embedding" : EMB_SBERT_MPNET_BASE
-          }
 
 
-def create_sbert_mpnet():
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        return HuggingFaceEmbeddings(model_name=EMB_SBERT_MPNET_BASE, model_kwargs={"device": device})
+
 
 llm = HuggingFaceHub(
         repo_id='mistralai/Mistral-7B-Instruct-v0.2',
@@ -43,8 +36,7 @@ llm = HuggingFaceHub(
     
 )
 
-if config["embedding"] == EMB_SBERT_MPNET_BASE:
-    embedding = create_sbert_mpnet()
+
 
 from langchain.text_splitter import CharacterTextSplitter, TokenTextSplitter
 from langchain.vectorstores import Chroma
@@ -85,17 +77,7 @@ def main():
         embeddings = hf
         knowledge_base = FAISS.from_texts(texts, embeddings)
 
-        retriever = knowledge_base.as_retriever(search_kwargs={"k":5})
-        # retriever = FAISS.as_retriever()
-        # persist_directory = config["persist_directory"]
-        # vectordb = Chroma.from_documents(documents=texts, embedding=embedding, persist_directory=persist_directory)
-
-        # retriever = vectordb.as_retriever(search_kwargs={"k":5})
-
-        # mode = st.selectbox(
-        #     label="Select agent type",
-        #     options=("Agent with AskHuman tool", "Traditional RAG Agent","Search Agent"),
-        # )
+        retriever = knowledge_base.as_retriever(search_kwargs={"k":3})