diff --git a/examples/pipelines/rag/llamaindex_ollama_pipeline.py b/examples/pipelines/rag/llamaindex_ollama_pipeline.py
index efafe672..0b7765c0 100644
--- a/examples/pipelines/rag/llamaindex_ollama_pipeline.py
+++ b/examples/pipelines/rag/llamaindex_ollama_pipeline.py
@@ -10,28 +10,48 @@
 
 from typing import List, Union, Generator, Iterator
 from schemas import OpenAIChatMessage
+import os
+
+from pydantic import BaseModel
 
 
 class Pipeline:
+
+    class Valves(BaseModel):
+        LLAMAINDEX_OLLAMA_BASE_URL: str
+        LLAMAINDEX_MODEL_NAME: str
+        LLAMAINDEX_EMBEDDING_MODEL_NAME: str
+
     def __init__(self):
         self.documents = None
         self.index = None
 
+        self.valves = self.Valves(
+            **{
+                "LLAMAINDEX_OLLAMA_BASE_URL": os.getenv("LLAMAINDEX_OLLAMA_BASE_URL", "http://localhost:11434"),
+                "LLAMAINDEX_MODEL_NAME": os.getenv("LLAMAINDEX_MODEL_NAME", "llama3"),
+                "LLAMAINDEX_EMBEDDING_MODEL_NAME": os.getenv("LLAMAINDEX_EMBEDDING_MODEL_NAME", "nomic-embed-text"),
+            }
+        )
+
     async def on_startup(self):
         from llama_index.embeddings.ollama import OllamaEmbedding
         from llama_index.llms.ollama import Ollama
         from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
 
         Settings.embed_model = OllamaEmbedding(
-            model_name="nomic-embed-text",
-            base_url="http://localhost:11434",
+            model_name=self.valves.LLAMAINDEX_EMBEDDING_MODEL_NAME,
+            base_url=self.valves.LLAMAINDEX_OLLAMA_BASE_URL,
+        )
+        Settings.llm = Ollama(
+            model=self.valves.LLAMAINDEX_MODEL_NAME,
+            base_url=self.valves.LLAMAINDEX_OLLAMA_BASE_URL,
         )
-        Settings.llm = Ollama(model="llama3")
 
         # This function is called when the server is started.
         global documents, index
 
-        self.documents = SimpleDirectoryReader("./data").load_data()
+        self.documents = SimpleDirectoryReader("/app/backend/data").load_data()
         self.index = VectorStoreIndex.from_documents(self.documents)
         pass