Fix v1 migration notebook

Author: natoverse

docs/examples_notebooks/index_migration_to_v1.ipynb

@@ -204,21 +204,29 @@
    "source": [
     "from graphrag.cache.factory import CacheFactory\n",
     "from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks\n",
-    "from graphrag.config.get_vector_store_settings import get_vector_store_settings\n",
     "from graphrag.index.workflows.generate_text_embeddings import generate_text_embeddings\n",
+    "from graphrag.language_model.manager import ModelManager\n",
+    "from graphrag.tokenizer.get_tokenizer import get_tokenizer\n",
     "\n",
     "# We only need to re-run the embeddings workflow, to ensure that embeddings for all required search fields are in place\n",
     "# We'll construct the context and run this function flow directly to avoid everything else\n",
     "\n",
-    "\n",
-    "vector_store_config = get_vector_store_settings(config)\n",
     "model_config = config.get_language_model_config(config.embed_text.model_id)\n",
     "callbacks = NoopWorkflowCallbacks()\n",
     "cache_config = config.cache.model_dump()  # type: ignore\n",
     "cache = CacheFactory().create_cache(\n",
     "    cache_type=cache_config[\"type\"],  # type: ignore\n",
     "    **cache_config,\n",
     ")\n",
+    "model = ModelManager().get_or_create_embedding_model(\n",
+    "    name=\"text_embedding\",\n",
+    "    model_type=model_config.type,\n",
+    "    config=model_config,\n",
+    "    callbacks=callbacks,\n",
+    "    cache=cache,\n",
+    ")\n",
+    "\n",
+    "tokenizer = get_tokenizer(model_config)\n",
     "\n",
     "await generate_text_embeddings(\n",
     "    documents=None,\n",
@@ -227,11 +235,12 @@
     "    entities=final_entities,\n",
     "    community_reports=final_community_reports,\n",
     "    callbacks=callbacks,\n",
-    "    cache=cache,\n",
-    "    model_config=model_config,\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
     "    batch_size=config.embed_text.batch_size,\n",
     "    batch_max_tokens=config.embed_text.batch_max_tokens,\n",
-    "    vector_store_config=vector_store_config,\n",
+    "    num_threads=model_config.concurrent_requests,\n",
+    "    vector_store_config=config.vector_store,\n",
     "    embedded_fields=config.embed_text.names,\n",
     ")"
    ]