diff --git a/playground/demos/chatbot/server/server.py b/playground/demos/chatbot/server/server.py
index 7773fbf7..b718d493 100644
--- a/playground/demos/chatbot/server/server.py
+++ b/playground/demos/chatbot/server/server.py
@@ -1,14 +1,13 @@
 """ Chatbot with RAG Server """
-
 import asyncio
 import logging
 import os
-
+from urllib.parse import urlparse, urljoin
 # import torch
 from contextlib import asynccontextmanager
 from typing import AsyncIterator
 from swarms.structs.agent import Agent
-import tiktoken
+
 from dotenv import load_dotenv
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
@@ -29,7 +28,7 @@ from swarms.prompts.conversational_RAG import (
 )
 from playground.demos.chatbot.server.responses import StreamingResponse
 from playground.demos.chatbot.server.server_models import ChatRequest
-from playground.demos.chatbot.server.vector_store import VectorStorage
+from playground.demos.chatbot.server.vector_storage import RedisVectorStorage
 from swarms.models.popular_llms import OpenAIChatLLM
 
 # Explicitly specify the path to the .env file
@@ -42,13 +41,15 @@ load_dotenv(dotenv_path)
 hf_token = os.environ.get(
     "HUGGINFACEHUB_API_KEY"
 )  # Get the Huggingface API Token
+
 uploads = os.environ.get(
     "UPLOADS"
 )  # Directory where user uploads files to be parsed for RAG
-model_dir = os.environ.get("MODEL_DIR")
+
+# model_dir = os.environ.get("MODEL_DIR")
 
 # hugginface.co model (eg. meta-llama/Llama-2-70b-hf)
-model_name = os.environ.get("MODEL_NAME")
+# model_name = os.environ.get("MODEL_NAME")
 
 # Set OpenAI's API key to 'EMPTY' and API base URL to use vLLM's API server
 # or set them to OpenAI API key and base URL.
@@ -60,8 +61,6 @@ openai_api_base = (
 env_vars = [
     hf_token,
     uploads,
-    model_dir,
-    model_name,
     openai_api_key,
     openai_api_base,
 ]
@@ -77,31 +76,22 @@ useMetal = os.environ.get("USE_METAL", "False") == "True"
 use_gpu = os.environ.get("USE_GPU", "False") == "True"
 
 print(f"Uploads={uploads}")
-print(f"MODEL_DIR={model_dir}")
-print(f"MODEL_NAME={model_name}")
 print(f"USE_METAL={useMetal}")
 print(f"USE_GPU={use_gpu}")
 print(f"OPENAI_API_KEY={openai_api_key}")
 print(f"OPENAI_API_BASE={openai_api_base}")
 
-# update tiktoken to include the model name (avoids warning message)
-tiktoken.model.MODEL_TO_ENCODING.update(
-    {
-        model_name: "cl100k_base",
-    }
-)
+# # update tiktoken to include the model name (avoids warning message)
+# tiktoken.model.MODEL_TO_ENCODING.update(
+#     {
+#         model_name: "cl100k_base",
+#     }
+# )
 
 print("Logging in to huggingface.co...")
 login(token=hf_token)  # login to huggingface.co
 
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    """Initializes the vector store in a background task."""
-    asyncio.create_task(vector_store.init_retrievers())
-    yield
-
-
-app = FastAPI(title="Chatbot", lifespan=lifespan)
+app = FastAPI(title="Chatbot")
 router = APIRouter()
 
 current_dir = os.path.dirname(__file__)
@@ -125,11 +115,15 @@ if not os.path.exists(uploads):
     os.makedirs(uploads)
 
 # Initialize the vector store
-vector_store = VectorStorage(directory=uploads, use_gpu=use_gpu)
-
+# Hardcoded for Swarms documention
+URL = "https://docs.swarms.world/en/latest/"
+vector_store = RedisVectorStorage(use_gpu=use_gpu)
+vector_store.crawl(URL)
+print("Vector storage initialized.")
 
 async def create_chat(
     messages: list[Message],
+    model_name: str,
     prompt: str = QA_PROMPT_TEMPLATE_STR,
 ):
     """Creates the RAG conversational retrieval chain."""
@@ -143,7 +137,6 @@ async def create_chat(
         streaming=True,
     )
 
-    retriever = await vector_store.get_retriever("swarms")
     doc_retrieval_string = ""
     for message in messages:
         if message.role == Role.HUMAN:
@@ -151,11 +144,15 @@ async def create_chat(
         elif message.role == Role.AI:
             doc_retrieval_string += f"{Role.AI}:  {message.content}\r\n"
 
-    docs = retriever.invoke(doc_retrieval_string)
+    docs = vector_store.embed(messages[-1].content)
 
     # find {context} in prompt and replace it with the docs page_content.
     # Concatenate the content of all documents
-    context = "\n".join(doc.page_content for doc in docs)
+    context = "\n".join(doc["content"] for doc in docs)
+
+    sources = [urlparse(URL).scheme + "://" + doc["source_url"] for doc in docs]
+
+    print(f"context: {context}")
 
     # Replace {context} in the prompt with the concatenated document content
     prompt = prompt.replace("{context}", context)
@@ -188,7 +185,7 @@ async def create_chat(
         # sop="Calculate the profit for a company.",
         # sop_list=["Calculate the profit for a company."],
         user_name="RAH@EntangleIT.com",
-        docs=[doc.page_content for doc in docs],
+        # docs=[doc["content"] for doc in docs],
         # # docs_folder="docs",
         retry_attempts=3,
         # context_length=1000,
@@ -204,9 +201,17 @@ async def create_chat(
             agent.add_message_to_memory(message.content)
         elif message.role == Role.AI:
             agent.add_message_to_memory(message.content)
+    
+    # add docs to short term memory
+    # for data in [doc["content"] for doc in docs]:
+    #     agent.add_message_to_memory(role=Role.HUMAN, content=data)
 
     async for response in agent.run_async(messages[-1].content):
-        yield response
+        res = response
+        res += "\n\nSources:\n"
+        for source in sources:
+            res += source + "\n"
+        yield res
 
     # memory = ConversationBufferMemory(
     #     chat_memory=chat_memory,
@@ -260,7 +265,8 @@ async def chat(request: ChatRequest):
     """ Handles chatbot chat POST requests """
     response = create_chat(
         messages=request.messages,
-        prompt=request.prompt.strip()
+        prompt=request.prompt.strip(),
+        model_name=request.model.id
     )
     # return response
     return StreamingResponse(content=response)