diff --git a/docs/examples_notebooks/local_search.ipynb b/docs/examples_notebooks/local_search.ipynb
index 1769c6ad..97aada0c 100644
--- a/docs/examples_notebooks/local_search.ipynb
+++ b/docs/examples_notebooks/local_search.ipynb
@@ -19,8 +19,8 @@
     "import os\n",
     "\n",
     "import pandas as pd\n",
-    "import tiktoken\n",
     "\n",
+    "from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig\n",
     "from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey\n",
     "from graphrag.query.indexer_adapters import (\n",
     "    read_indexer_covariates,\n",
@@ -102,7 +102,9 @@
     "# load description embeddings to an in-memory lancedb vectorstore\n",
     "# to connect to a remote db, specify url and port values.\n",
     "description_embedding_store = LanceDBVectorStore(\n",
-    "    collection_name=\"default-entity-description\",\n",
+    "    vector_store_schema_config=VectorStoreSchemaConfig(\n",
+    "        index_name=\"default-entity-description\"\n",
+    "    )\n",
     ")\n",
     "description_embedding_store.connect(db_uri=LANCEDB_URI)\n",
     "\n",
@@ -195,10 +197,12 @@
     "from graphrag.config.enums import ModelType\n",
     "from graphrag.config.models.language_model_config import LanguageModelConfig\n",
     "from graphrag.language_model.manager import ModelManager\n",
+    "from graphrag.tokenizer.get_tokenizer import get_tokenizer\n",
     "\n",
     "api_key = os.environ[\"GRAPHRAG_API_KEY\"]\n",
-    "llm_model = os.environ[\"GRAPHRAG_LLM_MODEL\"]\n",
-    "embedding_model = os.environ[\"GRAPHRAG_EMBEDDING_MODEL\"]\n",
+    "llm_model = \"gpt-4.1\"\n",
+    "embedding_model = \"text-embedding-3-small\"\n",
+    "tokenizer = get_tokenizer(encoding_model=\"o200k_base\")\n",
     "\n",
     "chat_config = LanguageModelConfig(\n",
     "    api_key=api_key,\n",
@@ -212,8 +216,6 @@
     "    config=chat_config,\n",
     ")\n",
     "\n",
-    "token_encoder = tiktoken.encoding_for_model(llm_model)\n",
-    "\n",
     "embedding_config = LanguageModelConfig(\n",
     "    api_key=api_key,\n",
     "    type=ModelType.OpenAIEmbedding,\n",
@@ -251,7 +253,7 @@
     "    entity_text_embeddings=description_embedding_store,\n",
     "    embedding_vectorstore_key=EntityVectorStoreKey.ID,  # if the vectorstore uses entity title as ids, set this to EntityVectorStoreKey.TITLE\n",
     "    text_embedder=text_embedder,\n",
-    "    token_encoder=token_encoder,\n",
+    "    tokenizer=tokenizer,\n",
     ")"
    ]
   },
@@ -314,7 +316,7 @@
     "search_engine = LocalSearch(\n",
     "    model=chat_model,\n",
     "    context_builder=context_builder,\n",
-    "    token_encoder=token_encoder,\n",
+    "    tokenizer=tokenizer,\n",
     "    model_params=model_params,\n",
     "    context_builder_params=local_context_params,\n",
     "    response_type=\"multiple paragraphs\",  # free form text describing the response type and format, can be anything, e.g. prioritized list, single paragraph, multiple paragraphs, multiple-page report\n",
@@ -426,7 +428,7 @@
     "question_generator = LocalQuestionGen(\n",
     "    model=chat_model,\n",
     "    context_builder=context_builder,\n",
-    "    token_encoder=token_encoder,\n",
+    "    tokenizer=tokenizer,\n",
     "    model_params=model_params,\n",
     "    context_builder_params=local_context_params,\n",
     ")"
@@ -451,7 +453,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "graphrag",
    "language": "python",
    "name": "python3"
   },
@@ -465,7 +467,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,