microsoft--graphrag/packages/graphrag-llm/example_notebooks/basic_embedding_example.ipynb

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    "# Copyright (c) 2026 Microsoft Corporation.\n",
    "# Licensed under the MIT License."
   ]
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    "## 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."
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     "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])"
   ]
  }
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