Create app.py

app.py

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+import os
+import streamlit as st
+from PyPDF2 import PdfReader
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+from groq import Groq
+
+# Initialize Groq Client
+client = Groq(api_key=os.getenv("groq_Api_key"))
+
+# Load embedding model
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Initialize FAISS vector store
+dimension = 384  # Embedding dimension of the model
+index = faiss.IndexFlatL2(dimension)
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    reader = PdfReader(pdf_file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to split text into chunks
+def chunk_text(text, chunk_size=500):
+    words = text.split()
+    return [" ".join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]
+
+# Function to add embeddings to vector database
+def add_to_vector_db(chunks):
+    embeddings = embedding_model.encode(chunks)
+    index.add(np.array(embeddings, dtype="float32"))
+    return embeddings
+
+# Streamlit frontend
+st.title("RAG-based PDF Query Application")
+
+# PDF upload
+uploaded_file = st.file_uploader("Upload your PDF file", type=["pdf"])
+if uploaded_file:
+    st.write("Processing your PDF...")
+    text = extract_text_from_pdf(uploaded_file)
+    chunks = chunk_text(text)
+    add_to_vector_db(chunks)
+    st.success("PDF processed and embeddings stored in the vector database!")
+
+# Query input
+query = st.text_input("Enter your query:")
+if query:
+    # Generate embedding for query
+    query_embedding = embedding_model.encode([query])
+    
+    # Retrieve relevant chunks from FAISS
+    distances, indices = index.search(np.array(query_embedding, dtype="float32"), k=5)
+    context = "\n".join([chunks[i] for i in indices[0]])
+
+    # Interact with Groq API
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": f"Context: {context}\n\nQuery: {query}"
+            }
+        ],
+        model="llama3-8b-8192",
+        stream=False,
+    )
+    response = chat_completion.choices[0].message.content
+
+    # Display response
+    st.write("Response:")
+    st.write(response)