diff --git a/cookbook/use_cases/finance/03_Earnings_Call_Analysis.ipynb b/cookbook/use_cases/finance/03_Earnings_Call_Analysis.ipynb
index 45793817..e7dc0987 100644
--- a/cookbook/use_cases/finance/03_Earnings_Call_Analysis.ipynb
+++ b/cookbook/use_cases/finance/03_Earnings_Call_Analysis.ipynb
@@ -779,12 +779,10 @@
     "connectivity = graph_analyzer.analyze_connectivity(knowledge_graph)\n",
     "metrics = graph_analyzer.compute_metrics(knowledge_graph)\n",
     "\n",
-    "top_entities = centrality.get(\"rankings\", [])[:5]\n",
     "num_communities = len(communities.get(\"communities\", []))\n",
     "\n",
     "print(\"Graph analysis completed\")\n",
-    "print(\"Communities:\", num_communities)\n",
-    "print(\"Top entities:\", len(top_entities))\n"
+    "print(\"Communities:\", num_communities)"
    ]
   },
   {
@@ -917,16 +915,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from semantica.vector_store import VectorStore\n",
-    "from semantica.context import ContextRetriever\n",
+    "from semantica.vector_store import VectorStore \n",
+    "from semantica.context import ContextRetriever \n",
+    "import numpy as np\n",
     "\n",
-    "vector_store = VectorStore(backend=\"faiss\")\n",
+    "# Initialize vector store (dimension should match your embedder; default is 768)\n",
+    "vector_store = VectorStore(backend=\"faiss\", dimension=768)\n",
     "\n",
-    "vector_store.add(\n",
-    "    texts=[parsed_doc[\"full_text\"]],\n",
+    "# 1) Embed the full transcript\n",
+    "embedding = vector_store.embed(parsed_doc[\"full_text\"])\n",
+    "\n",
+    "# 2) Store the embedding + metadata\n",
+    "vector_store.store(\n",
+    "    vectors=[embedding],\n",
     "    metadata=[{\"source\": \"earnings_call\", \"type\": \"transcript\"}],\n",
     ")\n",
     "\n",
+    "# 3) Configure the context retriever as before\n",
     "context_retriever = ContextRetriever(\n",
     "    knowledge_graph=knowledge_graph,\n",
     "    vector_store=vector_store,\n",
@@ -952,7 +957,7 @@
     "\n",
     "print(\"Hybrid GraphRAG configured\")\n",
     "print(\"Queries processed:\", len(queries))\n",
-    "print(\"Sample results:\", len(retrieved_contexts[0]) if retrieved_contexts else 0)\n"
+    "print(\"Sample results:\", len(retrieved_contexts[0]) if retrieved_contexts else 0)"
    ]
   },
   {