diff --git a/trulens_eval/examples/expositional/frameworks/llama_index/llama_index_hybrid_retriever.ipynb b/trulens_eval/examples/expositional/frameworks/llama_index/llama_index_hybrid_retriever.ipynb
index 82be47f23..fb49ff90c 100644
--- a/trulens_eval/examples/expositional/frameworks/llama_index/llama_index_hybrid_retriever.ipynb
+++ b/trulens_eval/examples/expositional/frameworks/llama_index/llama_index_hybrid_retriever.ipynb
@@ -26,7 +26,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# ! pip install trulens_eval==0.24.0 llama_index==0.10.11 openai pypdf torch sentence-transformers"
+    "# ! pip install trulens_eval==0.24.0 llama_index==0.10.11 llama-index-readers-file llama-index-llms-openai llama-index-retrievers-bm25 openai pypdf torch sentence-transformers"
    ]
   },
   {
@@ -82,37 +82,31 @@
    "outputs": [],
    "source": [
     "from llama_index.core import (\n",
-    "    VectorStoreIndex,\n",
-    "    StorageContext,\n",
     "    SimpleDirectoryReader,\n",
+    "    StorageContext,\n",
+    "    VectorStoreIndex,\n",
     ")\n",
-    "from llama_index.legacy import ServiceContext\n",
+    "from llama_index.retrievers.bm25 import BM25Retriever\n",
+    "from llama_index.core.retrievers import VectorIndexRetriever\n",
+    "from llama_index.core.node_parser import SentenceSplitter\n",
     "from llama_index.llms.openai import OpenAI\n",
     "\n",
+    "splitter = SentenceSplitter(chunk_size=1024)\n",
+    "\n",
     "# load documents\n",
     "documents = SimpleDirectoryReader(\n",
     "    input_files=[\"IPCC_AR6_WGII_Chapter03.pdf\"]\n",
     ").load_data()\n",
     "\n",
-    "# initialize service context (set chunk size)\n",
-    "# -- here, we set a smaller chunk size, to allow for more effective re-ranking\n",
-    "llm = OpenAI(model=\"gpt-3.5-turbo\")\n",
-    "service_context = ServiceContext.from_defaults(chunk_size=256, llm=llm)\n",
-    "nodes = service_context.node_parser.get_nodes_from_documents(documents)\n",
+    "nodes = splitter.get_nodes_from_documents(documents)\n",
     "\n",
     "# initialize storage context (by default it's in-memory)\n",
     "storage_context = StorageContext.from_defaults()\n",
-    "storage_context.docstore.add_documents(nodes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "storage_context.docstore.add_documents(nodes)\n",
+    "\n",
     "index = VectorStoreIndex(\n",
-    "    nodes, storage_context=storage_context, service_context=service_context\n",
+    "    nodes=nodes,\n",
+    "    storage_context=storage_context,\n",
     ")"
    ]
   },
@@ -129,13 +123,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_index.retrievers import BM25Retriever\n",
-    "\n",
     "# retireve the top 10 most similar nodes using embeddings\n",
-    "vector_retriever = index.as_retriever(similarity_top_k=10)\n",
+    "vector_retriever = VectorIndexRetriever(index)\n",
     "\n",
     "# retireve the top 10 most similar nodes using bm25\n",
-    "bm25_retriever = BM25Retriever.from_defaults(nodes=nodes, similarity_top_k=10)"
+    "bm25_retriever = BM25Retriever.from_defaults(nodes=nodes, similarity_top_k=2)"
    ]
   },
   {
@@ -151,7 +143,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_index.retrievers import BaseRetriever\n",
+    "from llama_index.core.retrievers import BaseRetriever\n",
     "\n",
     "class HybridRetriever(BaseRetriever):\n",
     "    def __init__(self, vector_retriever, bm25_retriever):\n",
@@ -190,7 +182,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_index.postprocessor import SentenceTransformerRerank\n",
+    "from llama_index.core.postprocessor import SentenceTransformerRerank\n",
     "\n",
     "reranker = SentenceTransformerRerank(top_n=4, model=\"BAAI/bge-reranker-base\")"
    ]
@@ -201,12 +193,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from llama_index.query_engine import RetrieverQueryEngine\n",
+    "from llama_index.core.query_engine import RetrieverQueryEngine\n",
     "\n",
     "query_engine = RetrieverQueryEngine.from_args(\n",
     "    retriever=hybrid_retriever,\n",
-    "    node_postprocessors=[reranker],\n",
-    "    service_context=service_context,\n",
+    "    node_postprocessors=[reranker]\n",
     ")"
    ]
   },
@@ -216,7 +207,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tru.start_dashboard()"
+    "tru.run_dashboard()"
    ]
   },
   {
@@ -307,9 +298,7 @@
    "outputs": [],
    "source": [
     "with tru_recorder as recording:\n",
-    "    response = query_engine.query(\n",
-    "    \"What is the impact of climate change on the ocean?\"\n",
-    ")"
+    "    response = query_engine.query(\"What is the impact of climate change on the ocean?\")"
    ]
   },
   {