Added Pebblo Cloud Sample App

pebblo_cloud/langchain/rag-sample/.gitignore

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+credentials
+google_token.json

pebblo_cloud/langchain/rag-sample/rag_sample.py

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+import os
+from typing import List
+
+from dotenv import load_dotenv
+from langchain.schema import Document
+from langchain_community.document_loaders import (
+    GoogleDriveLoader,
+    UnstructuredFileIOLoader,
+)
+from langchain_community.document_loaders.pebblo import PebbloSafeLoader
+from langchain_community.vectorstores.qdrant import Qdrant
+from langchain_openai.embeddings import OpenAIEmbeddings
+
+load_dotenv()
+
+#  Qdrant DB path
+QDRANT_PATH = "qdrant.db"
+#  Qdrant DB collection name
+COLLECTION_NAME = "identity-enabled-rag"
+
+
+class DataLoader:
+    def __init__(self, folder_id: str, collection_name: str = COLLECTION_NAME):
+        self.app_name = "pebblo-cloud-sample-app"
+        self.folder_id = folder_id
+        self.qdrant_collection_name = collection_name
+
+    def load_documents(self):
+        print("\nLoading RAG documents ...")
+        loader = PebbloSafeLoader(
+            GoogleDriveLoader(
+                folder_id=self.folder_id,
+                credentials_path="credentials/credentials.json",
+                token_path="./google_token.json",
+                recursive=True,
+                file_loader_cls=UnstructuredFileIOLoader,
+                file_loader_kwargs={"mode": "elements"},
+                load_auth=True,
+            ),
+            name=self.app_name,  # App name (Mandatory)
+            owner="Joe Smith",  # Owner (Optional)
+            description="SafeLoader and SafeRetrival app using Pebblo",  # Description (Optional)
+            api_key=os.environ.get("PEBBLO_API_KEY"), #Pebblo cloud API key
+        )
+        documents = loader.load()
+        unique_identities = set()
+        for doc in documents:
+            unique_identities.update(doc.metadata.get("authorized_identities"))
+
+        print(f"Authorized Identities: {list(unique_identities)}")
+        print(f"Loaded {len(documents)} documents ...\n")
+        return documents
+
+    def add_docs_to_qdrant(self, documents: List[Document]):
+        """
+        Load documents into Qdrant
+        """
+        print("\nAdding documents to Qdrant ...")
+        embeddings = OpenAIEmbeddings()
+        vectordb = Qdrant.from_documents(
+            documents,
+            embeddings,
+            path=QDRANT_PATH,
+            collection_name=self.qdrant_collection_name,
+        )
+        print(f"Added {len(documents)} documents to Qdrant ...\n")
+        return vectordb
+
+
+if __name__ == "__main__":
+    print("Loading documents to Qdrant ...")
+    def_folder_id = "<google_drive_folder_id>"
+    input_collection_name = "identity-enabled-rag"
+
+    qloader = DataLoader(def_folder_id, input_collection_name)
+
+    result_documents = qloader.load_documents()
+
+    vectordb_obj = qloader.add_docs_to_qdrant(result_documents)