diff --git a/README.md b/README.md
index 17f69b9..451b10c 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,17 @@
 
+<img src="https://github.com/user-attachments/assets/e23d11d5-2d7b-44e2-aa11-59ddcb66bebc" align=left height=150px>
+
 ![Supported python versions](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)
 [![PEP8](https://img.shields.io/badge/code%20style-pep8-black.svg)](https://www.python.org/dev/peps/pep-0008/)
 [![License](https://img.shields.io/badge/License-MIT%202.0-blue.svg)](LICENSE)
 [![Run Pytest](https://github.com/samadpls/BestRAG/actions/workflows/pytest.yml/badge.svg?branch=main)](https://github.com/samadpls/BestRAG/actions/workflows/pytest.yml)
-<img src='https://img.shields.io/github/stars/samadpls/BestRAG?color=red&label=stars&logoColor=black&style=social'>
+![GitHub stars](https://img.shields.io/github/stars/samadpls/BestRAG?color=red&label=stars&logoColor=black&style=social)
+
 
 Welcome to **BestRAG**! This Python library enables you to efficiently store and retrieve embeddings using a hybrid Retrieval-Augmented Generation (RAG) approach. It combines dense, sparse, and late interaction embeddings to provide a robust solution for handling large datasets.
 
+---
+
 ## 🚀 Installation
 
 To install **BestRAG**, simply run:
@@ -54,4 +59,4 @@ This project is licensed under the [MIT License](LICENSE).
 
 ---
 
-Created by [samadpls](https://github.com/samadpls) 🎉
\ No newline at end of file
+Created by [samadpls](https://github.com/samadpls) 🎉
diff --git a/bestrag/best_rag.py b/bestrag/best_rag.py
index 77ff6da..062a960 100644
--- a/bestrag/best_rag.py
+++ b/bestrag/best_rag.py
@@ -28,10 +28,10 @@ class BestRAG:
             interaction model to use. Defaults to "BAAI/bge-small-en-v1.5".
     """
 
-    def __init__(self, 
-                 url: str, 
-                 api_key: str, 
-                 collection_name: str, 
+    def __init__(self,
+                 url: str,
+                 api_key: str,
+                 collection_name: str,
                  late_interaction_model_name: Optional[str] = "BAAI/bge-small-en-v1.5"
                  ):
         self.collection_name = collection_name
@@ -57,9 +57,9 @@ def _create_or_use_collection(self):
                 collection_name=self.collection_name,
                 vectors_config={
                     "dense-vector": models.VectorParams(
-                        size=384, 
+                        size=384,
                         distance=Distance.COSINE
-                        ),
+                    ),
                     "output-token-embeddings": models.VectorParams(
                         size=384,
                         distance=Distance.COSINE,
@@ -180,6 +180,7 @@ def store_pdf_embeddings(self, pdf_path: str):
                 collection_name=self.collection_name,
                 points=[point]
             )
+
             print(
                 f"Stored embedding for page {page_num + 1} in collection '{self.collection_name}'.")
 
diff --git a/setup.py b/setup.py
index 4ce21ae..0a2f26c 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="bestrag",
-    version="0.1.3",
+    version="0.2.0",
     description="BestRAG (Best Retrieval Augmented) is a library for storing and"
     " searching document embeddings in a Qdrant vector database. It uses a "
     "hybrid embedding technique combining dense, late interaction and sparse representations for better performance.",
@@ -13,7 +13,7 @@
     author_email="abdulsamadsid1@gmail.com",
     long_description=long_description,
     long_description_content_type="text/markdown",
-    url="https://github.com/samadpls/bestrag",
+    url="https://github.com/samadpls/bestRAG",
     packages=find_packages(),
     install_requires=[
         "fastembed==0.4.1",