Create app.py

app.py

@@ -0,0 +1,74 @@
+import os
+import streamlit as st
+from sentence_transformers import SentenceTransformer, util
+from groq import Groq
+from PyPDF2 import PdfReader
+
+# Initialize the retriever and Groq client
+retriever = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+client = Groq(api_key="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")
+
+# Knowledge base (documents) and embeddings
+documents = [
+    "Retrieval-Augmented Generation (RAG) is an AI framework that combines the strengths of retrieval-based and generative models.",
+    "The main components of a RAG system are the retriever and the generator.",
+    "A key benefit of Retrieval-Augmented Generation is that it can produce more accurate responses compared to standalone generative models.",
+    "The retrieval process in a RAG system often relies on embedding-based models, like Sentence-BERT or DPR.",
+    "Common use cases of RAG include chatbots, customer support systems, and knowledge retrieval for business intelligence."
+]
+document_embeddings = retriever.encode(documents, convert_to_tensor=True)
+
+# Function to retrieve top relevant document
+def retrieve(query, top_k=1):
+    query_embedding = retriever.encode(query, convert_to_tensor=True)
+    hits = util.semantic_search(query_embedding, document_embeddings, top_k=top_k)
+    top_docs = [documents[hit['corpus_id']] for hit in hits[0]]
+    return top_docs[0] if hits[0] else None
+
+# Function to generate response using Groq
+def generate_response(query, context):
+    response = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": f"Context: {context} Question: {query} Answer:"
+            }
+        ],
+        model="gemma2-9b-it"
+    )
+    return response.choices[0].message.content
+
+# Function to handle PDF upload and text extraction
+def extract_text_from_pdf(file):
+    pdf_reader = PdfReader(file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to update knowledge base with new content from PDF
+def update_knowledge_base(pdf_text):
+    global documents, document_embeddings
+    documents.append(pdf_text)
+    document_embeddings = retriever.encode(documents, convert_to_tensor=True)
+
+# Streamlit app layout
+st.title("RAG-based Question Answering App")
+st.write("Upload a PDF, ask questions based on its content, and get answers!")
+
+# Upload PDF file
+uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
+if uploaded_file:
+    pdf_text = extract_text_from_pdf(uploaded_file)
+    update_knowledge_base(pdf_text)
+    st.write("PDF content successfully added to the knowledge base.")
+
+# Question input
+question = st.text_input("Enter your question:")
+if question:
+    retrieved_context = retrieve(question)
+    if retrieved_context:
+        answer = generate_response(question, retrieved_context)
+    else:
+        answer = "I have no knowledge about this topic."
+    st.write("Answer:", answer)