chore: import upstream snapshot with attribution

packages/graphrag-llm/example_notebooks/basic_embedding_example.ipynb

@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7ee94c2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright (c) 2026 Microsoft Corporation.\n",
+    "# Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9696211",
+   "metadata": {},
+   "source": [
+    "## Basic embedding example\n",
+    "\n",
+    "This examples demonstrates how to generate text embeddings using the GraphRAG LLM library with Azure OpenAI's embedding service. It loads API credentials from environment variables, creates a ModelConfig for the Azure embedding model and configures authentication to use either API key or Azure Managed Identity. The script then creates an embedding client and processes a batch of two text strings (\"Hello world\" and \"How are you?\") to generate their vector embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ed37af8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.0021342115942388773, -0.049084946513175964, 0.020961761474609375]\n",
+      "[0.02755456231534481, -0.026555174961686134, -0.027031073346734047]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "from graphrag_llm.config.model_config import ModelConfig\n",
+    "from graphrag_llm.config.types import AuthMethod\n",
+    "from graphrag_llm.embedding import LLMEmbedding, create_embedding\n",
+    "from graphrag_llm.types import LLMEmbeddingResponse\n",
+    "\n",
+    "api_key = os.getenv(\"GRAPHRAG_API_KEY\")\n",
+    "api_base = os.getenv(\"GRAPHRAG_API_BASE\")\n",
+    "\n",
+    "embedding_config = ModelConfig(\n",
+    "    model_provider=\"azure\",\n",
+    "    model=os.getenv(\"GRAPHRAG_EMBEDDING_MODEL\", \"text-embedding-3-small\"),\n",
+    "    azure_deployment_name=os.getenv(\n",
+    "        \"GRAPHRAG_LLM_EMBEDDING_MODEL\", \"text-embedding-3-small\"\n",
+    "    ),\n",
+    "    api_base=api_base,\n",
+    "    api_version=os.getenv(\"GRAPHRAG_API_VERSION\", \"2025-04-01-preview\"),\n",
+    "    api_key=api_key,\n",
+    "    auth_method=AuthMethod.AzureManagedIdentity if not api_key else AuthMethod.ApiKey,\n",
+    ")\n",
+    "\n",
+    "llm_embedding: LLMEmbedding = create_embedding(embedding_config)\n",
+    "\n",
+    "embeddings_batch: LLMEmbeddingResponse = llm_embedding.embedding(\n",
+    "    input=[\"Hello world\", \"How are you?\"]\n",
+    ")\n",
+    "for data in embeddings_batch.data:\n",
+    "    print(data.embedding[0:3])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}