how can i setup the Vector store as a remote endpoint - I am trying to debug a class and I

   import pinecone
   from llama_index.vector_stores.pinecone import PineconeVectorStore

   # Initialize Pinecone
   api_key = "api_key"
   pinecone.init(api_key=api_key, environment="us-west1-gcp")
   pinecone.create_index("quickstart", dimension=1536, metric="euclidean", pod_type="p1")
   index = pinecone.Index("quickstart")

   # Construct vector store
   vector_store = PineconeVectorStore(pinecone_index=index)
   

   from llama_index.vector_stores.redis import RedisVectorStore

   vector_store = RedisVectorStore(
       index_name="llm-project",
       redis_url="redis://localhost:6379",
       overwrite=True,
   )
   

   git clone git@github.com:chroma-core/chroma.git
   docker-compose up -d --build
   

   import chromadb
   from llama_index.vector_stores.chroma import ChromaVectorStore
   from llama_index.core import StorageContext, VectorStoreIndex

   # Create a client to connect to the Chroma server
   remote_db = chromadb.HttpClient()
   
   # Get or create a collection
   chroma_collection = remote_db.get_or_create_collection("quickstart")
   
   # Create a ChromaVectorStore using the remote collection
   vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
   
   # Create a storage context
   storage_context = StorageContext.from_defaults(vector_store=vector_store)
   
   # Create your index using the remote vector store
   index = VectorStoreIndex.from_documents(
       documents, storage_context=storage_context, embed_model=embed_model
   )
   

from channels.routing import ProtocolTypeRouter, URLRouter
from django.core.asgi import get_asgi_application
from . import consumers
from django.urls import re_path

application = ProtocolTypeRouter(
    {
        "http": get_asgi_application(),
        "websocket": URLRouter(
            [
                re_path(
                    r"ws/vector_store_index/$",
                    consumers.VectorStoreIndexConsumer.as_asgi(),
                ),
            ]
        ),
    }
)

   docker-compose -f docker-compose.server.example.yml up -d --build
   

import chromadb
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext, VectorStoreIndex

# Create a client to connect to your Docker service
# The exact connection details will depend on how your service is configured in the YAML file
remote_db = chromadb.HttpClient(host="localhost", port=8000)  # adjust host and port as needed

# Rest of your code to set up the vector store and index...