diff --git a/tutorials/llm_application_tracing_evaluating_and_analysis.ipynb b/tutorials/llm_application_tracing_evaluating_and_analysis.ipynb
index 50b59168e8..f540a7805e 100644
--- a/tutorials/llm_application_tracing_evaluating_and_analysis.ipynb
+++ b/tutorials/llm_application_tracing_evaluating_and_analysis.ipynb
@@ -46,26 +46,32 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -qq \"arize-phoenix[experimental,llama-index]\" \"openai>=1\" gcsfs nest_asyncio\n",
-    "\n",
-    "# Import Statements\n",
+    "!pip install -qq \"arize-phoenix[evals,llama-index]\" \"openai>=1\" gcsfs nest_asyncio llama-index-llms-openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "import os\n",
     "from getpass import getpass\n",
     "from typing import List, cast\n",
+    "from urllib.request import urlopen\n",
     "\n",
     "import pandas as pd\n",
     "import phoenix as px\n",
-    "import requests\n",
     "from gcsfs import GCSFileSystem\n",
-    "from llama_index import (\n",
+    "from llama_index.core import (\n",
     "    ServiceContext,\n",
     "    StorageContext,\n",
     "    load_index_from_storage,\n",
     "    set_global_handler,\n",
     ")\n",
-    "from llama_index.embeddings import OpenAIEmbedding\n",
-    "from llama_index.graph_stores.simple import SimpleGraphStore\n",
-    "from llama_index.llms import OpenAI\n",
+    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
+    "from llama_index.core.graph_stores import SimpleGraphStore\n",
+    "from llama_index.llms.openai import OpenAI\n",
     "from phoenix import TraceDataset\n",
     "from phoenix.trace import DocumentEvaluations, SpanEvaluations\n",
     "from phoenix.trace.utils import json_lines_to_df\n",
@@ -85,18 +91,10 @@
     "qa_correctness_eval_url = \"https://storage.googleapis.com/arize-phoenix-assets/datasets/unstructured/llm/context-retrieval/qa_correctness_eval.parquet\"\n",
     "retrieved_documents_eval_url = \"https://storage.googleapis.com/arize-phoenix-assets/datasets/unstructured/llm/context-retrieval/retrieved_documents_eval.parquet\"\n",
     "\n",
-    "response = requests.get(trace_jsonl_url)\n",
-    "\n",
-    "if response.status_code == 200:\n",
-    "    with open(\"trace.jsonl\", \"wb\") as f:\n",
-    "        f.write(response.content)\n",
-    "    json_lines = []\n",
-    "    with open(\"trace.jsonl\", \"r\") as f:\n",
-    "        json_lines = cast(List[str], f.readlines())\n",
-    "    trace_ds = TraceDataset(json_lines_to_df(json_lines))\n",
-    "    px.launch_app(trace=trace_ds)\n",
-    "else:\n",
-    "    print(f\"Failed to download the file. Status code: {response.status_code}\")\n",
+    "with urlopen(trace_jsonl_url) as response:\n",
+    "    lines = [line.decode(\"utf-8\") for line in response.readlines()]\n",
+    "trace_ds = TraceDataset(json_lines_to_df(lines))\n",
+    "px.launch_app(trace=trace_ds)\n",
     "\n",
     "hallucination_eval_df = pd.read_parquet(hallucination_eval_url)\n",
     "qa_correctness_eval_df = pd.read_parquet(qa_correctness_eval_url)\n",
@@ -244,7 +242,7 @@
     "# Generating the Hallucination & Q&A Eval\n",
     "\n",
     "import nest_asyncio\n",
-    "from phoenix.experimental.evals import (\n",
+    "from phoenix.evals import (\n",
     "    HALLUCINATION_PROMPT_RAILS_MAP,\n",
     "    HALLUCINATION_PROMPT_TEMPLATE,\n",
     "    QA_PROMPT_RAILS_MAP,\n",
@@ -258,7 +256,7 @@
     "# Creating Hallucination Eval which checks if the application hallucinated\n",
     "hallucination_eval = llm_classify(\n",
     "    dataframe=queries_df,\n",
-    "    model=OpenAIModel(\"gpt-4\", temperature=0.0),\n",
+    "    model=OpenAIModel(model=\"gpt-4\", temperature=0.0),\n",
     "    template=HALLUCINATION_PROMPT_TEMPLATE,\n",
     "    rails=list(HALLUCINATION_PROMPT_RAILS_MAP.values()),\n",
     "    provide_explanation=True,  # Makes the LLM explain its reasoning\n",
@@ -271,7 +269,7 @@
     "# Creating Q&A Eval which checks if the application answered the question correctly\n",
     "qa_correctness_eval = llm_classify(\n",
     "    dataframe=queries_df,\n",
-    "    model=OpenAIModel(\"gpt-4\", temperature=0.0),\n",
+    "    model=OpenAIModel(model=\"gpt-4\", temperature=0.0),\n",
     "    template=QA_PROMPT_TEMPLATE,\n",
     "    rails=list(QA_PROMPT_RAILS_MAP.values()),\n",
     "    provide_explanation=True,  # Makes the LLM explain its reasoning\n",
@@ -326,7 +324,7 @@
    "source": [
     "# Generating Retrieval Relevance Eval\n",
     "\n",
-    "from phoenix.experimental.evals import (\n",
+    "from phoenix.evals import (\n",
     "    RAG_RELEVANCY_PROMPT_RAILS_MAP,\n",
     "    RAG_RELEVANCY_PROMPT_TEMPLATE,\n",
     "    OpenAIModel,\n",
@@ -335,7 +333,7 @@
     "\n",
     "retrieved_documents_eval = llm_classify(\n",
     "    dataframe=retrieved_documents_df,\n",
-    "    model=OpenAIModel(\"gpt-4\", temperature=0.0),\n",
+    "    model=OpenAIModel(model=\"gpt-4\", temperature=0.0),\n",
     "    template=RAG_RELEVANCY_PROMPT_TEMPLATE,\n",
     "    rails=list(RAG_RELEVANCY_PROMPT_RAILS_MAP.values()),\n",
     "    provide_explanation=True,\n",