adding faiss

app.py

@@ -1,5 +1,24 @@
 
 import streamlit as st
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from flashrank import Ranker, RerankRequest
+
+
+@st.cache
+def get_embeddings():
+    model_name = "BAAI/bge-large-en-v1.5"
+    model_kwargs = {'device': 'cpu',"trust_remote_code":True}
+    encode_kwargs = {'normalize_embeddings': True} # set True to compute caosine similarity
+    model = HuggingFaceEmbeddings(
+        model_name=model_name,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs,)
+    return model
+
+baai_embeddings = get_embeddings()
+kadhal_Server = FAISS.from_local("./",baai_embeddings)
+ranker = Ranker(model_name="ms-marco-MiniLM-L-12-v2", cache_dir="/opt")
 
 
 st.header('kadhalTensor', divider='red')
@@ -11,11 +30,22 @@ with st.form("my_form"):
    st.write("What do want to know about sangam era's love?")
    
    question_input = st.text_input("")
+
+   
    
 
    # Every form must have a submit button.
    submitted = st.form_submit_button("Submit")
    if submitted:
-       st.write("slider", question_input,)
+       
+       docs = kadhal_Server.similarity_search(question_input)
+       tobeReranked = [{"text":doc.page_content , "metadata":doc.metadata} for doc in docs]
+       rerankInput = RerankRequest(
+                        passages=tobeReranked,
+                        query=" take care of our loved ones accorting to thirukkural?",)
+       reranked = ranker.rerank(rerankInput)
+
+       reranked_top = reranked[0:2]
+       st.write(reranked_top)
 
 st.write("Outside the form")