Create app.py

app.py

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+import os
+from groq import Groq
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from PyPDF2 import PdfReader
+import streamlit as st
+from tempfile import NamedTemporaryFile
+
+# Initialize Groq client
+client = Groq(api_key="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")
+
+# Function to extract text from a PDF
+def extract_text_from_pdf(pdf_file_path):
+    pdf_reader = PdfReader(pdf_file_path)
+    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, vector_db=None):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    if vector_db is None:
+        vector_db = FAISS.from_texts(chunks, embedding=embeddings)
+    else:
+        vector_db.add_texts(chunks)
+    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 Application QA")
+
+# Upload PDFs
+uploaded_files = st.file_uploader("Upload PDF documents", type=["pdf"], accept_multiple_files=True)
+
+if uploaded_files:
+    vector_db = None  # Initialize an empty vector DB
+    for uploaded_file in uploaded_files:
+        with NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
+            temp_file.write(uploaded_file.read())
+            pdf_path = temp_file.name
+
+        # Extract text
+        text = extract_text_from_pdf(pdf_path)
+        st.write(f"Text extracted from: {uploaded_file.name}")
+
+        # Chunk text
+        chunks = chunk_text(text)
+        st.write(f"Text chunked from: {uploaded_file.name}")
+
+        # Generate embeddings and store in FAISS
+        vector_db = create_embeddings_and_store(chunks, vector_db=vector_db)
+        st.write(f"Embeddings generated and stored for: {uploaded_file.name}")
+
+    # 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)