mirror of
https://github.com/microsoft/graphrag.git
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7bdeaee94a
* Base structure * Add fnllm providers and Mock LLM * Remove fnllm coupling, introduce llm providers * Ruff + Tests fix * Spellcheck * Semver * Format * Default MockChat params * Fix more tests * Fix embedding smoke test * Fix embeddings smoke test * Fix MockEmbeddingLLM * Rename LLM to model. Package organization * Fix prompt tuning * Oops * Oops II
74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
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from graphrag.config.create_graphrag_config import create_graphrag_config
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from graphrag.config.embeddings import (
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all_embeddings,
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)
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from graphrag.config.enums import ModelType, TextEmbeddingTarget
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from graphrag.index.operations.embed_text.embed_text import TextEmbedStrategyType
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from graphrag.index.workflows.generate_text_embeddings import (
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run_workflow,
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)
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from graphrag.utils.storage import load_table_from_storage
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from .util import (
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DEFAULT_MODEL_CONFIG,
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create_test_context,
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)
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async def test_generate_text_embeddings():
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context = await create_test_context(
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storage=[
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"documents",
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"relationships",
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"text_units",
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"entities",
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"community_reports",
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]
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)
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config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
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llm_settings = config.get_language_model_config(
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config.embed_text.model_id
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).model_dump()
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llm_settings["type"] = ModelType.MockEmbedding
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config.embed_text.strategy = {
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"type": TextEmbedStrategyType.openai,
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"llm": llm_settings,
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}
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config.embed_text.target = TextEmbeddingTarget.all
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config.snapshots.embeddings = True
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await run_workflow(
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config,
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context,
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NoopWorkflowCallbacks(),
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)
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parquet_files = context.storage.keys()
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for field in all_embeddings:
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assert f"embeddings.{field}.parquet" in parquet_files
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# entity description should always be here, let's assert its format
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entity_description_embeddings = await load_table_from_storage(
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"embeddings.entity.description", context.storage
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)
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assert len(entity_description_embeddings.columns) == 2
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assert "id" in entity_description_embeddings.columns
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assert "embedding" in entity_description_embeddings.columns
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# every other embedding is optional but we've turned them all on, so check a random one
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document_text_embeddings = await load_table_from_storage(
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"embeddings.document.text", context.storage
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)
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assert len(document_text_embeddings.columns) == 2
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assert "id" in document_text_embeddings.columns
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assert "embedding" in document_text_embeddings.columns
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