Spaces:
Runtime error
Runtime error
File size: 3,245 Bytes
5ddad15 589047e c75f073 589047e c75f073 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
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=groq_api) # Replace with your actual Groq API key
key = os.getenv("groq_api")
client = Groq(api_key = key)
# 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 and truncate context if too long
def retrieve(query, top_k=1, max_tokens=100):
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]]
# Truncate context to max_tokens if necessary
context = top_docs[0] if hits[0] else ""
context = ' '.join(context.split()[:max_tokens]) # Limit to max_tokens words
return context
# 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)
|