Add initial implementation of BestRAG library with Streamlit app and README updates

README.md

@@ -11,4 +11,39 @@ license: mit
 short_description: 'BestRAG: Hybrid Retrieval-Augmented Generation library'
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# BestRAG - Hybrid Retrieval-Augmented Generation (RAG)
+
+**BestRAG** is a Python library that leverages a hybrid Retrieval-Augmented Generation (RAG) approach to efficiently store and retrieve embeddings. By combining dense, sparse, and late interaction embeddings, BestRAG offers a robust solution for managing large datasets.
+
+## Features
+
+- 🚀 **Hybrid RAG**: Utilizes dense, sparse, and late interaction embeddings for enhanced performance.
+- 🔌 **Easy Integration**: Simple API for storing and searching embeddings.
+- 📄 **PDF Support**: Directly store embeddings from PDF documents.
+
+## How to Use
+
+1. **Initialize BestRAG**: Enter your Qdrant URL, API Key, and Collection Name, then click "Initialize BestRAG".
+2. **Create Embeddings**: Upload a PDF file and click "Create Embeddings" to store embeddings.
+3. **Search Embeddings**: Enter a search query and set the limit, then click "Search" to retrieve results.
+
+## Installation
+
+You can use BestRAG freely by installing it with:
+
+```bash
+pip install bestrag
+```
+
+For more details, visit the [GitHub repository](https://github.com/samadpls/BestRAG).
+
+[![GitHub stars](https://img.shields.io/github/stars/samadpls/BestRAG?color=red&label=stars&logoColor=black&style=social)](https://github.com/samadpls/BestRAG)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/bestrag?style=social)](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/).
+
+Made with ❤️ by [samadpls](https://github.com/samadpls)
+
+---
+
+Please like this project on [GitHub](https://github.com/samadpls/BestRAG) if you find it useful!

app.py

@@ -0,0 +1,83 @@
+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("""
+[![GitHub stars](https://img.shields.io/github/stars/samadpls/BestRAG?color=red&label=stars&logoColor=black&style=social)](https://github.com/samadpls/BestRAG)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/bestrag?style=social)](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.")

requirements.txt

@@ -0,0 +1,2 @@
+streamlit
+bestrag