# Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License from graphrag.callbacks.noop_verb_callbacks import NoopVerbCallbacks from graphrag.config.create_graphrag_config import create_graphrag_config from graphrag.config.enums import TextEmbeddingTarget from graphrag.index.config.embeddings import ( all_embeddings, ) from graphrag.index.workflows.generate_text_embeddings import ( run_workflow, ) from graphrag.utils.storage import load_table_from_storage from .util import ( create_test_context, ) async def test_generate_text_embeddings(): context = await create_test_context( storage=[ "create_final_documents", "create_final_relationships", "create_final_text_units", "create_final_entities", "create_final_community_reports", ] ) config = create_graphrag_config() config.embeddings.strategy = { "type": "mock", } config.embeddings.target = TextEmbeddingTarget.all config.snapshots.embeddings = True await run_workflow( config, context, NoopVerbCallbacks(), ) parquet_files = context.storage.keys() for field in all_embeddings: assert f"embeddings.{field}.parquet" in parquet_files # entity description should always be here, let's assert its format entity_description_embeddings = await load_table_from_storage( "embeddings.entity.description", context.storage ) assert len(entity_description_embeddings.columns) == 2 assert "id" in entity_description_embeddings.columns assert "embedding" in entity_description_embeddings.columns # every other embedding is optional but we've turned them all on, so check a random one document_text_embeddings = await load_table_from_storage( "embeddings.document.text", context.storage ) assert len(document_text_embeddings.columns) == 2 assert "id" in document_text_embeddings.columns assert "embedding" in document_text_embeddings.columns