diff --git a/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb b/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb
index 2c5cf80b..8f5608e6 100644
--- a/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb
+++ b/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb
@@ -5,6 +5,8 @@
    "id": "9570d208",
    "metadata": {},
    "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb)\n",
+    "\n",
     "# 🧪 GraphRAG: Skincare Intelligence System\n",
     "## 📖 Overview\n",
     "\n",
@@ -27,55 +29,57 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Install dependencies\n",
-    "!pip install -qU semantica networkx matplotlib plotly pandas faiss-cpu beautifulsoup4 groq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7a767b5",
-   "metadata": {},
-   "source": [
-    "## 🛠️ Phase 0: Environment & Foundation\n",
-    "We configure **Groq** as our primary LLM provider and seed the system with verified \"Ground Truth\" data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6840539f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import json\n",
-    "import pandas as pd\n",
-    "from semantica.core import Semantica, ConfigManager\n",
-    "from semantica.seed import SeedDataManager\n",
-    "from semantica.vector_store import VectorStore\n",
-    "\n",
-    "# 1. Groq Configuration\n",
-    "os.environ[\"GROQ_API_KEY\"] = \"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\"\n",
-    "\n",
-    "config_dict = {\n",
-    "    \"project_name\": \"Skincare_Intelligence\",\n",
-    "    \"embedding\": {\"provider\": \"openai\", \"model\": \"text-embedding-3-small\"}, \n",
-    "    \"extraction\": {\n",
-    "        \"provider\": \"groq\", \n",
-    "        \"model\": \"llama-3.1-8b-instant\", \n",
-    "        \"temperature\": 0.0\n",
-    "    },\n",
-    "    \"inference\": {\n",
-    "        \"provider\": \"groq\",\n",
-    "        \"model\": \"llama-3.1-70b-versatile\"\n",
-    "    },\n",
-    "    \"vector_store\": {\"provider\": \"faiss\", \"dimension\": 1536},\n",
-    "    \"knowledge_graph\": {\"backend\": \"networkx\", \"merge_entities\": True}\n",
-    "}\n",
-    "\n",
-    "config = ConfigManager().load_from_dict(config_dict)\n",
-    "core = Semantica(config=config)\n",
-    "vs = VectorStore(backend=\"faiss\", dimension=1536)\n",
+     "# Install dependencies\n",
+     "!pip install -qU semantica networkx matplotlib plotly pandas faiss-cpu beautifulsoup4 groq sentence-transformers"
+    ]
+   },
+   {
+    "cell_type": "markdown",
+    "id": "b7a767b5",
+    "metadata": {},
+    "source": [
+     "## 🛠️ Phase 0: Environment & Foundation\n",
+     "We configure **Groq** as our primary LLM provider and use **Sentence-Transformers** for local embeddings to avoid API dependencies."
+    ]
+   },
+   {
+    "cell_type": "code",
+    "execution_count": null,
+    "id": "6840539f",
+    "metadata": {},
+    "outputs": [],
+    "source": [
+     "import os\n",
+     "import json\n",
+     "import pandas as pd\n",
+     "from semantica.core import Semantica, ConfigManager\n",
+     "from semantica.seed import SeedDataManager\n",
+     "from semantica.vector_store import VectorStore\n",
+     "\n",
+     "# 1. Groq Configuration\n",
+    "import getpass\n",
+    "if \"GROQ_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter your Groq API Key: \")\n",
+     "\n",
+     "config_dict = {\n",
+     "    \"project_name\": \"Skincare_Intelligence\",\n",
+     "    \"embedding\": {\"provider\": \"sentence_transformers\", \"model\": \"all-MiniLM-L6-v2\"}, \n",
+     "    \"extraction\": {\n",
+     "        \"provider\": \"groq\", \n",
+     "        \"model\": \"llama-3.1-8b-instant\", \n",
+     "        \"temperature\": 0.0\n",
+     "    },\n",
+     "    \"inference\": {\n",
+     "        \"provider\": \"groq\",\n",
+     "        \"model\": \"llama-3.1-70b-versatile\"\n",
+     "    },\n",
+     "    \"vector_store\": {\"provider\": \"faiss\", \"dimension\": 384},\n",
+     "    \"knowledge_graph\": {\"backend\": \"networkx\", \"merge_entities\": True}\n",
+     "}\n",
+     "\n",
+     "config = ConfigManager().load_from_dict(config_dict)\n",
+     "core = Semantica(config=config)\n",
+     "vs = VectorStore(backend=\"faiss\", dimension=384)\n",
     "\n",
     "# 2. Seeding Ground Truth\n",
     "foundation_data = {\n",
diff --git a/cookbook/use_cases/advanced_rag/02_RAG_vs_GraphRAG_Comparison.ipynb b/cookbook/use_cases/advanced_rag/02_RAG_vs_GraphRAG_Comparison.ipynb
index 6f88ae41..30ba1ee9 100644
--- a/cookbook/use_cases/advanced_rag/02_RAG_vs_GraphRAG_Comparison.ipynb
+++ b/cookbook/use_cases/advanced_rag/02_RAG_vs_GraphRAG_Comparison.ipynb
@@ -5,10 +5,9 @@
    "id": "de0a7591",
    "metadata": {},
    "source": [
-    "# RAG vs. GraphRAG: Final Answer Comparison\n",
-    "\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/use_cases/advanced_rag/02_RAG_vs_GraphRAG_Comparison.ipynb)\n",
     "\n",
+    "# RAG vs. GraphRAG: Final Answer Comparison\n",
     "This notebook demonstrates the difference in final answers generated by **Standard Vector RAG** and **Semantica GraphRAG** for a complex multi-hop query. We use **FalkorDB** as our high-performance graph backend.\n",
     "\n",
     "### FalkorDB Setup\n",
@@ -53,7 +52,9 @@
     "import os\n",
     "\n",
     "# 1. Core Semantica & Vector Store\n",
-    "os.environ[\"GROQ_API_KEY\"] = \"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\"\n",
+    "import getpass\n",
+    "if \"GROQ_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"GROQ_API_KEY\"] = getpass.getpass(\"Enter your Groq API Key: \")\n",
     "\n",
     "config_dict = {\n",
     "    \"embedding\": {\"provider\": \"sentence_transformers\", \"model\": \"all-MiniLM-L6-v2\"},\n",