Update BestRAG and release versio: 0.2.1

bestrag/best_rag.py

@@ -5,7 +5,7 @@
 import uuid
 from typing import List, Optional
 from qdrant_client import QdrantClient, models
-from qdrant_client.http.models import Distance, VectorParams
+from qdrant_client.http.models import Distance
 from fastembed import TextEmbedding
 from fastembed.sparse.bm25 import Bm25
 import PyPDF2
@@ -35,6 +35,8 @@ def __init__(self,
                  late_interaction_model_name: Optional[str] = "BAAI/bge-small-en-v1.5"
                  ):
         self.collection_name = collection_name
+        self.api_key = api_key
+        self.url = url
         self.client = QdrantClient(url=url, api_key=api_key)
 
         self.dense_model = TextEmbedding()
@@ -225,3 +227,27 @@ def search(self, query: str, limit: int = 10):
         )
 
         return results
+
+    def __str__(self):
+        """
+        Return a string representation of the BestRAG object, including its parameters
+        and a message to follow on GitHub.
+        """
+        info = (
+            "\033[94m"
+            "**************************************************\n"
+            "* BestRAG Object Information                     *\n"
+            "**************************************************\n"
+            f"* URL: {self.url}\n"
+            f"* API Key: {self.api_key}\n"
+            f"* Collection Name: {self.collection_name}\n"
+            "**************************************************\033[0m"
+        )
+        message = (
+            "\033[92m"
+            "**************************************************\n"
+            "* Made with ❤️ by samadpls                       *\n"
+            "* Visit: https://github.com/samadpls             *\n"
+            "**************************************************\033[0m"
+        )
+        return f"{info}\n{message}"

requirements.txt

@@ -2,7 +2,7 @@ fastembed==0.4.1
 streamlit
 pytest
 flake8
-PyPDF2
+PyPDF2==3.0.1
 qdrant-client
 onnxruntime==1.19.2
 pytest

setup.py

@@ -5,10 +5,8 @@
 
 setup(
     name="bestrag",
-    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.",
+    version="0.2.1",
+    description="bestrag: Library for storing and searching document embeddings in a Qdrant vector database using hybrid embedding techniques.",
     author="samadpls",
     author_email="[email protected]",
     long_description=long_description,
@@ -17,10 +15,7 @@
     packages=find_packages(),
     install_requires=[
         "fastembed==0.4.1",
-        "streamlit",
-        "pytest",
-        "flake8",
-        "PyPDF2",
+        "PyPDF2==3.0.1",
         "qdrant-client",
         "onnxruntime==1.19.2",
     ],