integrated with pinecone

app.py

@@ -1,39 +1,40 @@
 # Langchain imports
-from langchain_community.vectorstores.faiss import FAISS
+from langchain_groq import ChatGroq 
 from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain_core.prompts import ChatPromptTemplate
 from langchain.chains import create_retrieval_chain
+from langchain_pinecone import PineconeVectorStore
 
-# Embedding and model imports
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from langchain_groq import ChatGroq
-
+# Embedding and model import
 # Other
 import streamlit as st
 import os
 import time
 from PyPDF2 import PdfReader
 import tempfile
+import pdfplumber
+
 
-st.title("Ask your questions from pdf(s) or website")
+st.title("Ask questions from your PDF(s) or website")
 option = None
 
 # Prompt user to choose between PDFs or website
 option = st.radio("Choose input type:", ("PDF(s)", "Website"), index=None)
 
-
 def get_pdf_processed(pdf_docs):
-    text=""
+    text = ""
     for pdf in pdf_docs:
-        pdf_reader= PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
+        with pdfplumber.open(pdf) as pdf_file:
+            for page in pdf_file.pages:
+                text += page.extract_text()
     return text
 
 def llm_model():
-    llm = ChatGroq(model="mixtral-8x7b-32768")
+    # llm = ChatGroq(model="mixtral-8x7b-32768",groq_api_key=st.secrets['GROQ_API_KEY'])
+    llm = ChatGroq(model="mixtral-8x7b-32768",groq_api_key=groq_api_key)
     prompt = ChatPromptTemplate.from_template(
     """
     Answer the question based on the provided context only.
@@ -51,36 +52,42 @@ def llm_model():
     prompt = st.text_input("Input your question here")
 
     if prompt:
-
-        start =time.process_time()
+        start = time.process_time()
         response = retrieval_chain.invoke({"input":prompt})
         st.write(response['answer'])
         st.write("Response time: ", time.process_time() - start)
 
-        with st.expander("Did not like the response? Check out more here"):
-            for i, doc in enumerate(response['context']):
-                st.write(doc.page_content)
-                st.write("-----------------------------")
+# st.session_state.embeddings =GoogleGenerativeAIEmbeddings(model = 'models/embedding-001',google_api_key=st.secrets['GOOGLE_API_KEY'])
+model_name = "all-MiniLM-L6-v2"
+st.session_state.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
 
-st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model = 'models/embedding-001')
 st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size =1000, chunk_overlap= 200)
 
+index_name = "myindex"
+st.session_state.vector = PineconeVectorStore(index_name=index_name, embedding=st.session_state.embeddings)
+
+
 if option:
     if option == "Website":
         website_link = st.text_input("Enter the website link:")
         if website_link:
-            st.session_state.loader = WebBaseLoader(website_link)
-            st.session_state.docs = st.session_state.loader.load()
-            st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
-            st.session_state.vector = FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings)
-            llm_model()
-
+            with st.spinner("Loading website content..."):
+                st.session_state.loader = WebBaseLoader(website_link)
+                st.session_state.docs = st.session_state.loader.load()
+                st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
+                st.session_state.vector = PineconeVectorStore.from_documents(st.session_state.final_documents, index_name=index_name, embedding = st.session_state.embeddings)
+            st.success("Done!")
+        llm_model()
+            
     elif option == "PDF(s)":
         pdf_files = st.file_uploader("Upload your PDF files", type=["pdf"], accept_multiple_files=True)
         if pdf_files:
-            st.session_state.docs = get_pdf_processed(pdf_files)
-            st.session_state.final_documents = st.session_state.text_splitter.split_text(st.session_state.docs)
-            st.session_state.vector = FAISS.from_texts(st.session_state.final_documents,st.session_state.embeddings)
+            with st.spinner("Loading pdf..."):
+                st.session_state.docs = get_pdf_processed(pdf_files)
+                st.session_state.final_documents = st.session_state.text_splitter.split_text(st.session_state.docs)
+                st.session_state.vector = PineconeVectorStore.from_texts(st.session_state.final_documents, index_name=index_name, embedding = st.session_state.embeddings) 
+            st.success("Done!")
+            st.empty()
             llm_model()