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c644338bae
* Refactor config
- Add new ModelConfig to represent LLM settings
- Combines LLMParameters, ParallelizationParameters, encoding_model, and async_mode
- Add top level models config that is a list of available LLM ModelConfigs
- Remove LLMConfig inheritance and delete LLMConfig
- Replace the inheritance with a model_id reference to the ModelConfig listed in the top level models config
- Remove all fallbacks and hydration logic from create_graphrag_config
- This removes the automatic env variable overrides
- Support env variables within config files using Templating
- This requires "$" to be escaped with extra "$" so ".*\\.txt$" becomes ".*\\.txt$$"
- Update init content to initialize new config file with the ModelConfig structure
* Use dict of ModelConfig instead of list
* Add model validations and unit tests
* Fix ruff checks
* Add semversioner change
* Fix unit tests
* validate root_dir in pydantic model
* Rename ModelConfig to LanguageModelConfig
* Rename ModelConfigMissingError to LanguageModelConfigMissingError
* Add validationg for unexpected API keys
* Allow skipping pydantic validation for testing/mocking purposes.
* Add default lm configs to verb tests
* smoke test
* remove config from flows to fix llm arg mapping
* Fix embedding llm arg mapping
* Remove timestamp from smoke test outputs
* Remove unused "subworkflows" smoke test properties
* Add models to smoke test configs
* Update smoke test output path
* Send logs to logs folder
* Fix output path
* Fix csv test file pattern
* Update placeholder
* Format
* Instantiate default model configs
* Fix unit tests for config defaults
* Fix migration notebook
* Remove create_pipeline_config
* Remove several unused config models
* Remove indexing embedding and input configs
* Move embeddings function to config
* Remove skip_workflows
* Remove skip embeddings in favor of explicit naming
* fix unit test spelling mistake
* self.models[model_id] is already a language model. Remove redundant casting.
* update validation errors to instruct users to rerun graphrag init
* instantiate LanguageModelConfigs with validation
* skip validation in unit tests
* update verb tests to use default model settings instead of skipping validation
* test using llm settings
* cleanup verb tests
* remove unsafe default model config
* remove the ability to skip pydantic validation
* remove None union types when default values are set
* move vector_store from embeddings to top level of config and delete resolve_paths
* update vector store settings
* fix vector store and smoke tests
* fix serializing vector_store settings
* fix vector_store usage
* fix vector_store type
* support cli overrides for loading graphrag config
* rename storage to output
* Add --force flag to init
* Remove run_id and resume, fix Drift config assignment
* Ruff
---------
Co-authored-by: Nathan Evans <github@talkswithnumbers.com>
Co-authored-by: Alonso Guevara <alonsog@microsoft.com>
84 lines
2.5 KiB
Python
84 lines
2.5 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.enums import LLMType
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from graphrag.index.operations.summarize_communities.community_reports_extractor.community_reports_extractor import (
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CommunityReportResponse,
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FindingModel,
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)
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from graphrag.index.workflows.create_final_community_reports import (
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run_workflow,
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workflow_name,
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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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compare_outputs,
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create_test_context,
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load_test_table,
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)
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MOCK_RESPONSES = [
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CommunityReportResponse(
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title="<report_title>",
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summary="<executive_summary>",
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rating=2,
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rating_explanation="<rating_explanation>",
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findings=[
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FindingModel(
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summary="<insight_1_summary>", explanation="<insight_1_explanation"
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),
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FindingModel(
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summary="<insight_2_summary>", explanation="<insight_2_explanation"
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),
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],
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)
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]
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async def test_create_final_community_reports():
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expected = load_test_table(workflow_name)
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context = await create_test_context(
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storage=[
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"create_final_nodes",
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"create_final_covariates",
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"create_final_relationships",
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"create_final_entities",
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"create_final_communities",
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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.community_reports.model_id
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).model_dump()
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llm_settings["type"] = LLMType.StaticResponse
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llm_settings["responses"] = MOCK_RESPONSES
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llm_settings["parse_json"] = True
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config.community_reports.strategy = {
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"type": "graph_intelligence",
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"llm": llm_settings,
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}
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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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actual = await load_table_from_storage(workflow_name, context.storage)
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assert len(actual.columns) == len(expected.columns)
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# only assert a couple of columns that are not mock - most of this table is LLM-generated
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compare_outputs(actual, expected, columns=["community", "level"])
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# assert a handful of mock data items to confirm they get put in the right spot
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assert actual["rank"][:1][0] == 2
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assert actual["rank_explanation"][:1][0] == "<rating_explanation>"
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