File size: 3,218 Bytes
37c5884 |
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 |
import json
import streamlit as st
from bestrag import BestRAG
import os
# Streamlit app title
col1, col2 = st.columns([1, 5])
with col1:
st.image("https://github.com/user-attachments/assets/e23d11d5-2d7b-44e2-aa11-59ddcb66bebc", width=140)
with col2:
st.title("BestRAG - Hybrid Retrieval-Augmented Generation (RAG)")
st.markdown("""
[](https://github.com/samadpls/BestRAG)
[](https://pypi.org/project/bestrag/)
> **Note**: Qdrant offers a free tier with 4GB of storage. To generate your API key and endpoint, visit [Qdrant](https://qdrant.tech/).
You can use BestRAG freely by installing it with `pip install bestrag`. For more details, visit the [GitHub repository](https://github.com/samadpls/BestRAG).
Made with ❤️ by [samadpls](https://github.com/samadpls)
""")
# Input fields for BestRAG initialization
url = st.text_input("Qdrant URL", "https://YOUR_QDRANT_URL")
api_key = st.text_input("Qdrant API Key", "YOUR_API_KEY")
collection_name = st.text_input("Collection Name", "YOUR_COLLECTION_NAME")
# Initialize BestRAG only when the user clicks a button
if st.button("Initialize BestRAG"):
st.session_state['rag'] = BestRAG(url=url, api_key=api_key, collection_name=collection_name)
st.success("BestRAG initialized successfully!")
# Check if BestRAG is initialized
if 'rag' in st.session_state:
rag = st.session_state['rag']
# Tabs for different functionalities
tab1, tab2 = st.tabs(["Create Embeddings", "Search Embeddings"])
with tab1:
st.header("Create Embeddings")
# File uploader for PDF
pdf_file = st.file_uploader("Upload PDF", type=["pdf"])
if st.button("Create Embeddings"):
if pdf_file is not None:
# Save the uploaded PDF to a temporary file
temp_pdf_path = os.path.join("/tmp", pdf_file.name)
with open(temp_pdf_path, "wb") as f:
f.write(pdf_file.getbuffer())
# Use the uploaded PDF's name
pdf_name = pdf_file.name
# Store PDF embeddings
rag.store_pdf_embeddings(temp_pdf_path, pdf_name)
st.success(f"Embeddings created for {pdf_name}")
else:
st.error("Please upload a PDF file.")
with tab2:
st.header("Search Embeddings")
# Input fields for search
query = st.text_input("Search Query", "example query")
limit = st.number_input("Limit", min_value=1, max_value=20, value=5)
if st.button("Search"):
# Perform search
results = rag.search(query, limit)
# Display results
st.subheader("Search Results")
for result in results.points:
st.json({
"id": result.id,
"score": result.score,
"payload": result.payload
})
else:
st.warning("Please initialize BestRAG first.") |