diff --git a/rag-langchain/README.md b/rag-langchain/README.md
index a82b2c8f..ec3acecb 100644
--- a/rag-langchain/README.md
+++ b/rag-langchain/README.md
@@ -30,5 +30,5 @@ snapshot_download(repo_id="BAAI/bge-base-en-v1.5",
 Follow the instructions below to build you container image and run it locally. 
 
 * `podman build -t ragapp rag-langchain -f rag-langchain/builds/Containerfile`
-* `podman run --rm -it -p 8501:8501 -v Local/path/to/locallm/models/:/rag/models:Z -v Local/path/to/locallm/data:/rag/data:Z ragapp -- -H 10.88.0.1 -m http://10.88.0.1:8001/v1`
+* `podman run --rm -it -p 8501:8501 -v Local/path/to/locallm/models/:/rag/models:Z -v Local/path/to/locallm/data:/rag/data:Z -e MODEL_SERVICE_ENDPOINT=http://10.88.0.1:8001/v1 ragapp -- -H 10.88.0.1 `
 
diff --git a/rag-langchain/ai-studio.yaml b/rag-langchain/ai-studio.yaml
index e9e1db30..de273a5a 100644
--- a/rag-langchain/ai-studio.yaml
+++ b/rag-langchain/ai-studio.yaml
@@ -15,7 +15,7 @@ application:
       ports:
         - 8001
     - name: chromadb-server
-      contextdir:: builds/chromadb
+      contextdir: builds/chromadb
       containerfile: Containerfile
       vectordb: true
       arch:
diff --git a/rag-langchain/rag_app.py b/rag-langchain/rag_app.py
index 71399391..128bbccc 100644
--- a/rag-langchain/rag_app.py
+++ b/rag-langchain/rag_app.py
@@ -18,15 +18,21 @@
 import argparse
 import pathlib
 
+model_service = os.getenv("MODEL_SERVICE_ENDPOINT",
+                          "http://0.0.0.0:8001/v1")
+
 parser = argparse.ArgumentParser()
 parser.add_argument("-c", "--chunk_size", default=150)
 parser.add_argument("-e", "--embedding_model", default="BAAI/bge-base-en-v1.5")
 parser.add_argument("-H", "--vdb_host", default="0.0.0.0")
 parser.add_argument("-p", "--vdb_port", default="8000")
 parser.add_argument("-n", "--name", default="test_collection")
-parser.add_argument("-m", "--model_url", default="http://0.0.0.0:8001/v1")
 args = parser.parse_args()
 
+vectorDB_client = HttpClient(host=args.vdb_host,
+                    port=args.vdb_port,
+                    settings=Settings(allow_reset=True,))
+
 def clear_vdb():
     global client
     client.delete_collection(args.name)
@@ -56,10 +62,7 @@ def get_files():
 embedding_func = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=args.embedding_model)
 e = SentenceTransformerEmbeddings(model_name=args.embedding_model)
 
-client = HttpClient(host=args.vdb_host,
-                             port=args.vdb_port,
-                             settings=Settings(allow_reset=True,))
-collection = client.get_or_create_collection(args.name,
+collection = vectorDB_client.get_or_create_collection(args.name,
                                       embedding_function=embedding_func)
 if collection.count() < 1 and data != None:
     print("populating db")
@@ -87,13 +90,13 @@ def get_files():
 for msg in st.session_state.messages:
     st.chat_message(msg["role"]).write(msg["content"])
 
-db = Chroma(client=client,
+db = Chroma(client=vectorDB_client,
             collection_name=args.name,
             embedding_function=e
     )
 retriever = db.as_retriever(threshold=0.75)
 
-llm = ChatOpenAI(base_url=args.model_url, 
+llm = ChatOpenAI(base_url=model_service, 
                  api_key="EMPTY",
                  streaming=True,
                  callbacks=[StreamlitCallbackHandler(st.container(),