Create app.py

app.py

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+import streamlit as st
+import os
+from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA
+from langchain_community.document_loaders import WebBaseLoader
+from langchain.embeddings import OllamaEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+import time
+import requests
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+## load the Groq API key
+os.environ['NVIDIA_API_KEY'] = os.environ.get('api_key')
+
+def vector_embedding():
+    if "vectors" not in st.session_state:
+        st.session_state.embeddings = NVIDIAEmbeddings()
+        st.session_state.loader = PyPDFDirectoryLoader("./documents")  # Data Ingestion
+        st.session_state.docs = st.session_state.loader.load()  # Document Loading
+        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=700, chunk_overlap=50)  # Chunk Creation
+        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)  # Splitting
+        print("hEllo")
+        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)  # Vector OpenAI embeddings
+
+st.title("Ayurvedic Chatbot using Nvidia NIM")
+llm = ChatNVIDIA(model="meta/llama3-70b-instruct")
+
+prompt = ChatPromptTemplate.from_template(
+"""
+Answer the questions based on the provided context only.
+Please provide the most accurate response based on the question.
+Give a detailed answer for the question.
+<context>
+{context}
+<context>
+Questions:{input}
+"""
+)
+
+prompt1 = st.text_input("Enter Your Question From related to Ayurvedic Herbs?")
+
+if st.button("Documents Embedding"):
+    vector_embedding()
+    st.write("Vector Store DB Is Ready")
+
+if prompt1:
+    # Ensure vectors are initialized before proceeding
+    if "vectors" not in st.session_state:
+        st.warning("Please embed the documents first by clicking the 'Documents Embedding' button.")
+    else:
+        document_chain = create_stuff_documents_chain(llm, prompt)
+        retriever = st.session_state.vectors.as_retriever()
+        retrieval_chain = create_retrieval_chain(retriever, document_chain)
+        start = time.process_time()
+        
+        try:
+            response = retrieval_chain.invoke({'input': prompt1})
+        except requests.exceptions.SSLError as e:
+            st.error("SSL error occurred: {}".format(e))
+            response = None
+
+        if response:
+            print("Response time:", time.process_time() - start)
+            st.write(response['answer'])
+
+            # With a streamlit expander
+            with st.expander("Document Similarity Search"):
+                # Find the relevant chunks
+                for i, doc in enumerate(response["context"]):
+                    st.write(doc.page_content)
+                    st.write("--------------------------------")