diff --git a/pebblo_saferetriever/langchain/identity-rag/sharepoint/msgraph_api_auth.py b/pebblo_saferetriever/langchain/identity-rag/sharepoint/msgraph_api_auth.py
new file mode 100644
index 00000000..ac57958d
--- /dev/null
+++ b/pebblo_saferetriever/langchain/identity-rag/sharepoint/msgraph_api_auth.py
@@ -0,0 +1,124 @@
+from typing import Optional
+import os
+import requests
+
+from dotenv import load_dotenv
+load_dotenv()
+
+class SharepointADHelper:
+    def __init__(
+        self,
+        client_id: Optional[str] = None,
+        client_secret: Optional[str] = None,
+        tenant_id: Optional[str] = None
+    ):
+        self.client_id = client_id or os.environ.get("O365_CLIENT_ID")
+        self.client_secret = client_secret or os.environ.get("O365_CLIENT_SECRET")
+        self.tenant_id = tenant_id or os.environ.get("O365_TENANT_ID")
+        if not all([self.client_id, self.client_secret, self.tenant_id]):
+            raise EnvironmentError(
+                "At-least one of O365_CLIENT_ID, O365_CLIENT_SECRET or O365_TENANT_ID not provided"
+            )
+        self.access_token = self.get_access_token()
+        if not self.access_token:
+            raise EnvironmentError("o365 client id/secret or tenant id is invalid."
+                            "Please check the environment variables.")
+        self.headers = { 'Authorization': 'Bearer' + self.access_token }
+
+    def get_authorized_identities(self, user_id: str):
+        """
+        Retrieves the authorized identities for a given user.
+
+        Args:
+            user_id (str): The ID of the user.
+
+        Returns:
+            list: A list of authorized identities, including associated group emails and the user ID.
+        """
+        user = self._get_users(user_id)
+        user_index_id = user.get("id")
+        if not user_index_id:
+            print(f"Could not find the user `{user_id}` information in Microsoft Graph API. Not authorized.")
+            return [user_id]
+        associated_groups = self._get_associated_groups(user_index_id)
+        associated_groups_emails = [
+            group.get("mail") for group in associated_groups["value"] if group.get("mail")
+        ]
+        return associated_groups_emails + [user_id]
+
+    def _get_associated_groups(self, user_index_id: str):
+        """
+        Retrieves the associated groups for a given user.
+
+        Args:
+            user_index_id (str): The index ID of the user.
+
+        Returns:
+            dict: A dictionary containing the associated groups information.
+
+        Raises:
+            Exception: If there is an error while making the API request.
+        """
+        url = f"https://graph.microsoft.com/v1.0/users/{user_index_id}/memberOf"
+        try:
+            response = requests.get(url=url, headers=self.headers, timeout=10)
+            response.raise_for_status()
+        except requests.exceptions.HTTPError:
+            print("Error while retrieving associated groups from Microsoft Graph API")
+            return {}
+        else:
+            return response.json()
+
+    def _get_users(self, user_id: str):
+        """
+        Retrieves information about a specific user from the Microsoft Graph API.
+
+        Args:
+            user_id (str): The ID of the user to retrieve information for.
+
+        Returns:
+            dict: A dictionary containing the user's information.
+
+        Raises:
+            Exception: If there is an error while making the API request.
+        """
+        url = f"https://graph.microsoft.com/v1.0/users/{user_id}"
+        try:
+            response = requests.get(url=url, headers=self.headers, timeout=10)
+            response.raise_for_status()
+        except requests.exceptions.HTTPError:
+            print("Error while retrieving user information from Microsoft Graph API")
+            return {}
+        else:
+            return response.json()
+
+
+    def get_access_token(self):
+        """
+        Retrieves an access token from Microsoft Graph API using client credentials.
+        Returns:
+            str: The access token.
+        Raises:
+            requests.exceptions.HTTPError: If the request to retrieve the access token fails.
+        """
+        # ToDo: This access token should be cached and refreshed when it expires
+        # It should also be stored in home directory or in a secure location 
+        url = f"https://login.microsoftonline.com/{self.tenant_id}/oauth2/v2.0/token"
+        headers = {"Content-Type": "application/x-www-form-urlencoded"}
+        data = {
+            "grant_type": "client_credentials",
+            "client_id": self.client_id,
+            "client_secret": self.client_secret,
+            "scope": "https://graph.microsoft.com/.default"
+        }
+        try:
+            response = requests.post(url, headers=headers, data=data, timeout=10)
+            response.raise_for_status()  # Raise exception if the request failed
+        except requests.exceptions.HTTPError:
+            print("Error while retrieving access token from Microsoft Graph API")
+            return ""
+        else:
+            return response.json()["access_token"]
+
+if __name__ == "__main__":
+    pass
\ No newline at end of file
diff --git a/pebblo_saferetriever/langchain/identity-rag/sharepoint/msgraph_sdk_auth.py b/pebblo_saferetriever/langchain/identity-rag/sharepoint/msgraph_sdk_auth.py
new file mode 100644
index 00000000..0b289430
--- /dev/null
+++ b/pebblo_saferetriever/langchain/identity-rag/sharepoint/msgraph_sdk_auth.py
@@ -0,0 +1,47 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+import asyncio
+import os
+from typing import Optional
+
+from msgraph import GraphServiceClient
+from azure.identity import ClientSecretCredential
+from kiota_abstractions.api_error import APIError
+
+async def get_authorized_identities(
+    user_id: str,
+    client_id: Optional[str] = None,
+    client_secret: Optional[str] = None,
+    tenant_id: Optional[str] = None
+):
+    client_id = client_id or os.environ.get("O365_CLIENT_ID")
+    client_secret = client_secret or os.environ.get("O365_CLIENT_SECRET")
+    tenant_id = tenant_id or os.environ.get("O365_TENANT_ID")
+    if not all([client_id, client_secret, tenant_id]):
+        raise EnvironmentError(
+            "atleast one of {O365_CLIENT_ID, O365_CLIENT_SECRET or O365_TENANT_ID not provided"
+        )
+    credentials = ClientSecretCredential(
+        tenant_id,
+        client_id,
+        client_secret,
+    )
+    graph_client = GraphServiceClient(credentials)
+
+    # user = graph_client.users.by_user_id(user_id)
+    try:
+        groups = await graph_client.users.by_user_id(user_id).member_of.get()
+    except APIError:
+        print(f"ms_graph API error: invalid user: {user_id}")
+        return [user_id]
+    auth_iden = [
+        group.__dict__.get("mail")
+        for group in groups.value
+        if group.__dict__.get("mail")
+    ] + [user_id]
+    return auth_iden
+
+if __name__ == "__main__":
+    print(asyncio.run(get_authorized_identities("arpit@daxaai.onmicrosoft.com")))
+
diff --git a/pebblo_saferetriever/langchain/identity-rag/sharepoint/pebblo_identity_api_rag.py b/pebblo_saferetriever/langchain/identity-rag/sharepoint/pebblo_identity_api_rag.py
new file mode 100644
index 00000000..44b5436d
--- /dev/null
+++ b/pebblo_saferetriever/langchain/identity-rag/sharepoint/pebblo_identity_api_rag.py
@@ -0,0 +1,132 @@
+# Fill-in OPENAI_API_KEY in .env file in this directory before proceeding
+from dotenv import load_dotenv
+load_dotenv()
+
+import asyncio
+import os
+from msgraph_api_auth import SharepointADHelper
+from langchain_community.chains import PebbloRetrievalQA
+from langchain_community.chains.pebblo_retrieval.models import (
+    AuthContext,
+    ChainInput,
+)
+from langchain_community.document_loaders import UnstructuredFileIOLoader
+from langchain_community.document_loaders.pebblo import PebbloSafeLoader
+from langchain_community.vectorstores.qdrant import Qdrant
+from langchain_community.document_loaders import SharePointLoader
+from langchain_openai.embeddings import OpenAIEmbeddings
+from langchain_openai.llms import OpenAI
+
+
+
+
+class PebbloIdentityRAG:
+    def __init__(self, drive_id: str, folder_path: str, collection_name: str):
+        self.loader_app_name = "pebblo-identity-loader"
+        self.retrieval_app_name = "pebblo-identity-retriever"
+        self.collection_name = collection_name
+        self.drive_id = drive_id
+        self.folder_path = folder_path
+
+        # Load documents
+        print("\nLoading RAG documents ...")
+        self.loader = PebbloSafeLoader(
+            SharePointLoader(
+                document_library_id=self.drive_id,
+                folder_path=self.folder_path or "/",
+                auth_with_token=True,
+                load_auth=True,
+                recursive=True,
+                load_extended_metadata=True,
+            ),
+            name=self.loader_app_name,  # App name (Mandatory)
+            owner="Joe Smith",  # Owner (Optional)
+            description="Identity enabled SafeLoader and SafeRetrival app using Pebblo",  # Description (Optional)
+        )
+        self.documents = self.loader.load()
+        print(self.documents[-1].metadata.get("authorized_identities"))
+        print(f"Loaded {len(self.documents)} documents ...\n")
+
+        # Load documents into VectorDB
+
+        print("Hydrating Vector DB ...")
+        self.vectordb = self.embeddings()
+        print("Finished hydrating Vector DB ...\n")
+
+        # Prepare LLM
+        self.llm = OpenAI()
+        print("Initializing PebbloRetrievalQA ...")
+        self.retrieval_chain = self.init_retrieval_chain()
+
+    def init_retrieval_chain(self):
+        """
+        Initialize PebbloRetrievalQA chain
+        """
+        return PebbloRetrievalQA.from_chain_type(
+            llm=self.llm,
+            app_name=self.retrieval_app_name,
+            owner="Joe Smith",
+            description="Identity enabled filtering using PebbloSafeLoader, and PebbloRetrievalQA",
+            chain_type="stuff",
+            retriever=self.vectordb.as_retriever(),
+            verbose=True,
+        )
+
+    def embeddings(self):
+        embeddings = OpenAIEmbeddings()
+        vectordb = Qdrant.from_documents(
+            self.documents,
+            embeddings,
+            location=":memory:",
+            collection_name=self.collection_name,
+        )
+        return vectordb
+
+    def ask(self, question: str, user_email: str, auth_identifiers: list):
+        auth_context = {
+            "user_id": user_email,
+            "user_auth": auth_identifiers,
+        }
+        auth_context = AuthContext(**auth_context)
+        chain_input = ChainInput(query=question, auth_context=auth_context)
+
+        return self.retrieval_chain.invoke(chain_input.dict())
+
+
+if __name__ == "__main__":
+    input_collection_name = "identity-enabled-rag"
+
+    print("Please enter ingestion user details for loading data...")
+    app_client_id = input("App client id : ") or os.environ.get("O365_CLIENT_ID")
+    app_client_secret = input("App client secret : ") or os.environ.get("O365_CLIENT_SECRET")
+    tenant_id = input("Tenant id : ") or os.environ.get("O365_TENANT_ID")
+    
+    drive_id = input("Drive id : ") or "b!TVvGZhXfGUuSKMdCgOucz08XRKxsDuVCojWCjzBMN-as9t-EstljQKBl332OMJnI"
+
+    rag_app = PebbloIdentityRAG(
+        drive_id = drive_id, folder_path = "/document", collection_name=input_collection_name
+    )
+    
+    while True:
+        print("Please enter end user details below")
+        end_user_email_address = input("User email address : ") or "arpit@daxaai.onmicrosoft.com"
+        prompt = input("Please provide the prompt : ") or "tell me about sample.pdf."
+        print(f"User: {end_user_email_address}.\nQuery:{prompt}\n")
+        authorized_identities = SharepointADHelper(
+                client_id = app_client_id,
+                client_secret = app_client_secret,
+                tenant_id = tenant_id,
+            ).get_authorized_identities(end_user_email_address)
+        response = rag_app.ask(prompt, end_user_email_address, authorized_identities)
+        print(f"Response:\n{response}")
+        try:
+            continue_or_exist = int(input("\n\nType 1 to continue and 0 to exit : "))
+        except ValueError:
+            print("Please provide valid input")
+            continue
+
+        if not continue_or_exist:
+            exit(0)
+
+        print("\n\n")
+
diff --git a/pebblo_saferetriever/langchain/identity-rag/sharepoint/pebblo_identity_sdk_rag.py b/pebblo_saferetriever/langchain/identity-rag/sharepoint/pebblo_identity_sdk_rag.py
new file mode 100644
index 00000000..9936083c
--- /dev/null
+++ b/pebblo_saferetriever/langchain/identity-rag/sharepoint/pebblo_identity_sdk_rag.py
@@ -0,0 +1,135 @@
+# Fill-in OPENAI_API_KEY in .env file in this directory before proceeding
+from dotenv import load_dotenv
+load_dotenv()
+
+import asyncio
+import os
+from msgraph_sdk_auth import get_authorized_identities
+from langchain_community.chains import PebbloRetrievalQA
+from langchain_community.chains.pebblo_retrieval.models import (
+    AuthContext,
+    ChainInput,
+)
+from langchain_community.document_loaders import UnstructuredFileIOLoader
+from langchain_community.document_loaders.pebblo import PebbloSafeLoader
+from langchain_community.vectorstores.qdrant import Qdrant
+from langchain_community.document_loaders import SharePointLoader
+from langchain_openai.embeddings import OpenAIEmbeddings
+from langchain_openai.llms import OpenAI
+
+
+
+
+class PebbloIdentityRAG:
+    def __init__(self, drive_id: str, folder_path: str, collection_name: str):
+        self.loader_app_name = "pebblo-identity-loader"
+        self.retrieval_app_name = "pebblo-identity-retriever"
+        self.collection_name = collection_name
+        self.drive_id = drive_id
+        self.folder_path = folder_path
+
+        # Load documents
+        print("\nLoading RAG documents ...")
+        self.loader = PebbloSafeLoader(
+            SharePointLoader(
+                document_library_id=self.drive_id,
+                folder_path=self.folder_path or "/",
+                auth_with_token=True,
+                load_auth=True,
+                recursive=True,
+                load_extended_metadata=True,
+            ),
+            name=self.loader_app_name,  # App name (Mandatory)
+            owner="Joe Smith",  # Owner (Optional)
+            description="Identity enabled SafeLoader and SafeRetrival app using Pebblo",  # Description (Optional)
+        )
+        self.documents = self.loader.load()
+        print(self.documents[-1].metadata.get("authorized_identities"))
+        print(f"Loaded {len(self.documents)} documents ...\n")
+
+        # Load documents into VectorDB
+
+        print("Hydrating Vector DB ...")
+        self.vectordb = self.embeddings()
+        print("Finished hydrating Vector DB ...\n")
+
+        # Prepare LLM
+        self.llm = OpenAI()
+        print("Initializing PebbloRetrievalQA ...")
+        self.retrieval_chain = self.init_retrieval_chain()
+
+    def init_retrieval_chain(self):
+        """
+        Initialize PebbloRetrievalQA chain
+        """
+        return PebbloRetrievalQA.from_chain_type(
+            llm=self.llm,
+            app_name=self.retrieval_app_name,
+            owner="Joe Smith",
+            description="Identity enabled filtering using PebbloSafeLoader, and PebbloRetrievalQA",
+            chain_type="stuff",
+            retriever=self.vectordb.as_retriever(),
+            verbose=True,
+        )
+
+    def embeddings(self):
+        embeddings = OpenAIEmbeddings()
+        vectordb = Qdrant.from_documents(
+            self.documents,
+            embeddings,
+            location=":memory:",
+            collection_name=self.collection_name,
+        )
+        return vectordb
+
+    def ask(self, question: str, user_email: str, auth_identifiers: list):
+        auth_context = {
+            "user_id": user_email,
+            "user_auth": auth_identifiers,
+        }
+        auth_context = AuthContext(**auth_context)
+        chain_input = ChainInput(query=question, auth_context=auth_context)
+
+        return self.retrieval_chain.invoke(chain_input.dict())
+
+
+if __name__ == "__main__":
+    input_collection_name = "identity-enabled-rag"
+
+    print("Please enter ingestion user details for loading data...")
+    app_client_id = input("App client id : ") or os.environ.get("O365_CLIENT_ID")
+    app_client_secret = input("App client secret : ") or os.environ.get("O365_CLIENT_SECRET")
+    tenant_id = input("Tenant id : ") or os.environ.get("O365_TENANT_ID")
+    
+    drive_id = input("Drive id : ") or "b!TVvGZhXfGUuSKMdCgOucz08XRKxsDuVCojWCjzBMN-as9t-EstljQKBl332OMJnI"
+
+    rag_app = PebbloIdentityRAG(
+        drive_id = drive_id, folder_path = "/document", collection_name=input_collection_name
+    )
+    loop = asyncio.get_event_loop()
+    
+    while True:
+        print("Please enter end user details below")
+        end_user_email_address = input("User email address : ") or "arpit@daxaai.onmicrosoft.com"
+        prompt = input("Please provide the prompt : ") or "tell me about sample.pdf."
+        print(f"User: {end_user_email_address}.\nQuery:{prompt}\n")
+        authorized_identities = loop.run_until_complete(
+            get_authorized_identities(
+                user_id=end_user_email_address,
+                client_id = app_client_id,
+                client_secret = app_client_secret,
+                tenant_id = tenant_id,
+            )
+        )
+        response = rag_app.ask(prompt, end_user_email_address, authorized_identities)
+        print(f"Response:\n{response}")
+        try:
+            continue_or_exist = int(input("\n\nType 1 to continue and 0 to exit : "))
+        except ValueError:
+            print("Please provide valid input")
+            continue
+
+        if not continue_or_exist:
+            exit(0)
+
+        print("\n\n")
diff --git a/pebblo_saferetriever/langchain/identity-rag/sharepoint/requirements_sdk.txt b/pebblo_saferetriever/langchain/identity-rag/sharepoint/requirements_sdk.txt
new file mode 100644
index 00000000..abcd05a7
--- /dev/null
+++ b/pebblo_saferetriever/langchain/identity-rag/sharepoint/requirements_sdk.txt
@@ -0,0 +1,12 @@
+azure-identity
+msgraph-sdk-python-core
+langchain==0.2.3
+# langchain-community==0.2.4 # For PebbloSafeLoader, PebbloRetrievalQA, VectorStore
+langchain-openai>=0.1.7 # For OpenAI LLM and OpenAIEmbeddings
+python-dotenv==1.0.0
+tiktoken
+requests==2.31.0
+Markdown==3.5
+unstructured[all-docs]
+qdrant-client==1.8.0 # for Qdrant VectorStore
+PyPDF2
\ No newline at end of file