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app.py

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+import os
+import requests
+from io import BytesIO
+from PyPDF2 import PdfReader
+from tempfile import NamedTemporaryFile
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from groq import Groq
+import streamlit as st
+
+# Initialize Groq client
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+
+# if client:
+#     st.write(f"API KEY FOUND!!!{client}")
+# else:
+#     st.write("API KEY NOT FOUND")
+
+# Predefined list of Google Drive links
+drive_links = [
+    "https://drive.google.com/file/d/1JPf0XvDhn8QoDOlZDrxCOpu4WzKFESNz/view?usp=sharing"
+]
+
+# Function to download PDF from Google Drive
+def download_pdf_from_drive(drive_link):
+    file_id = drive_link.split('/d/')[1].split('/')[0]
+    download_url = f"https://drive.google.com/uc?id={file_id}&export=download"
+    response = requests.get(download_url)
+    if response.status_code == 200:
+        return BytesIO(response.content)
+    else:
+        raise Exception("Failed to download the PDF file from Google Drive.")
+
+# Function to extract text from a PDF
+def extract_text_from_pdf(pdf_stream):
+    pdf_reader = PdfReader(pdf_stream)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to split text into chunks
+def chunk_text(text, chunk_size=500, chunk_overlap=50):
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size, chunk_overlap=chunk_overlap
+    )
+    return text_splitter.split_text(text)
+
+# Function to create embeddings and store them in FAISS
+def create_embeddings_and_store(chunks):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_db = FAISS.from_texts(chunks, embedding=embeddings)
+    return vector_db
+
+# Function to query the vector database and interact with Groq
+def query_vector_db(query, vector_db):
+    # Retrieve relevant documents
+    docs = vector_db.similarity_search(query, k=3)
+    context = "\n".join([doc.page_content for doc in docs])
+
+    # Interact with Groq API
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": f"Use the following context:\n{context}"},
+            {"role": "user", "content": query},
+        ],
+        model="llama3-8b-8192",
+    )
+    return chat_completion.choices[0].message.content
+
+# Streamlit app
+st.title("RAG-Based ChatBot (Already having Document)")
+
+st.write("Processing the Data links...")
+
+all_chunks = []
+
+# Process each predefined Google Drive link
+for link in drive_links:
+    try:
+        # st.write(f"Processing link: {link}")
+        # Download PDF
+        pdf_stream = download_pdf_from_drive(link)
+        # st.write("PDF Downloaded Successfully!")
+
+        # Extract text
+        text = extract_text_from_pdf(pdf_stream)
+        # st.write("PDF Text Extracted Successfully!")
+
+        # Chunk text
+        chunks = chunk_text(text)
+        # st.write(f"Created {len(chunks)} text chunks.")
+        all_chunks.extend(chunks)
+    except Exception as e:
+        st.write(f"Error processing link {link}: {e}")
+
+if all_chunks:
+    # Generate embeddings and store in FAISS
+    vector_db = create_embeddings_and_store(all_chunks)
+    st.write("Data is Ready Successfully!")
+
+    # User query input
+    user_query = st.text_input("Enter your query:")
+    if user_query:
+        response = query_vector_db(user_query, vector_db)
+        st.write("Response from LLM:")
+        st.write(response)

requirements.txt

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+requests
+PyPDF2
+streamlit 
+groq
+langchain
+langchain-community
+faiss-gpu
+sentence-transformers