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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=os.getenv("groq_api_key"))
# Function to extract text from 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)
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
def query_vector_db(query, vector_db):
docs = vector_db.similarity_search(query, k=3)
context = '\n'.join([doc.page_content for doc in docs])
chat_completion = client.chat.completions.create(
messages=[
{'role':'system', 'content': f"use the following contect : \n{context}"},
{'role':'user','content':query},
],
model = 'llama3-8b-8192'
)
return chat_completion.choices[0].message.content
#Streamlit APP
st.title("Rag Based Application")
upload_file = st.file_uploader("Upload a PDF Document", type =['pdf'])
if upload_file:
with NamedTemporaryFile(delete=False, suffix='.pdf') as temp_file:
temp_file.write(upload_file.read())
pdf_path = temp_file.name
text = extract_text_from_pdf(pdf_path)
st.write("PDF Text Extracted Successful")
chunks = chunk_text(text)
st.write("Text Chunked Successfully")
vector_db = create_embeddings_and_store(chunks)
st.write("Embeddings Generate and Store Successfully")
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)
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