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import gc
import streamlit as st
from langchain_qdrant import Qdrant
from qdrant_client import QdrantClient
import google.generativeai as genai
from src.utils import get_pdf_text, get_text_chunks, get_vector_store, get_conversational_chain
from src.embeddings import get_embeddings
st.set_page_config("RAG | Qdrant & Gemini")
st.header("RAG with Qdrant 🚀 + Gemini ❇️ ")
GEMINI_API_KEY = st.sidebar.text_input("Enter Gemini ❇️ API:")
QDRANT_API_KEY = st.sidebar.text_input("Enter Qdrant 🚀 API:")
QDRANT_HOST = st.sidebar.text_input("Enter Qdrant 🚀 endpoint:")
genai.configure(api_key=GEMINI_API_KEY)
client = QdrantClient(url=QDRANT_HOST, api_key=QDRANT_API_KEY)
def user_input(user_question):
response = st.session_state.conversation({'question': user_question})
st.session_state.chatHistory = response['chat_history']
for i, message in enumerate(st.session_state.chatHistory):
if i % 2 == 0:
st.write("You 🐼 : ", message.content)
else:
st.write("Gemini ✨: ", message.content)
def create_database():
chunk_size = st.sidebar.slider("Select the chunk size:", min_value=512, max_value=1500)
chunk_overlap = st.sidebar.slider("Select the overlap:", min_value=20, max_value=500)
pdf_docs = st.file_uploader("Select PDF Files & Click on the Submit Button 😉", type=".pdf", accept_multiple_files=True)
target_collection = st.text_input("Enter the Collection name 📒:")
if st.button("Submit"):
with st.spinner("Processing⌛..."):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text, chunk_size=chunk_size, chunk_overlap=chunk_overlap)
get_vector_store(text_chunks, target_collection, url=QDRANT_HOST, api_key=QDRANT_API_KEY)
st.success("Done")
del pdf_docs, raw_text, text_chunks
gc.collect()
def rag():
if 'collections' not in st.session_state:
st.session_state.collections = []
if 'selected_collection' not in st.session_state:
st.session_state.selected_collection = None
with st.sidebar:
if st.button("Show Collections in VectorDB 🔎"):
collections = client.get_collections().collections
st.session_state.collections = [collection.name for collection in collections]
if st.session_state.selected_collection not in st.session_state.collections:
st.session_state.selected_collection = st.session_state.collections[0] if st.session_state.collections else None
gc.collect()
if st.session_state.collections:
st.session_state.selected_collection = st.selectbox(
"Choose the collection: 🤔",
st.session_state.collections,
index=st.session_state.collections.index(st.session_state.selected_collection) if st.session_state.selected_collection else 0
)
if st.session_state.selected_collection:
st.sidebar.write(f"Selected collection: {st.session_state.selected_collection}")
vector_store = Qdrant(client=client, collection_name=st.session_state.selected_collection, embeddings=get_embeddings())
gc.collect()
if "conversation" not in st.session_state or st.session_state.conversation is None:
st.session_state.conversation = get_conversational_chain(vector_store, google_api_key=GEMINI_API_KEY)
st.title("Information Retrieval 🕵")
user_question = st.text_input("Ask me a Question 😄")
gc.collect()
if "chatHistory" not in st.session_state:
st.session_state.chatHistory = []
if user_question:
user_input(user_question)
st.text("Tip💡: Make Sure to clear the input before changing the collection name!!")
option = st.sidebar.selectbox("Choose an action ✅", ("Create a Database", "Information Retrieval"))
gc.collect()
if option == "Create a Database":
create_database()
elif option == "Information Retrieval":
rag() |