graphrag/tests/unit/config/test_config.py

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Refactor config (#1593) * 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>
2025-01-21 15:52:06 -08:00
# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License
import os
from pathlib import Path
from unittest import mock
import pytest
from pydantic import ValidationError
import graphrag.config.defaults as defs
from graphrag.config.create_graphrag_config import create_graphrag_config
from graphrag.config.enums import AuthType, ModelType
Refactor config (#1593) * 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>
2025-01-21 15:52:06 -08:00
from graphrag.config.load_config import load_config
from tests.unit.config.utils import (
DEFAULT_EMBEDDING_MODEL_CONFIG,
DEFAULT_MODEL_CONFIG,
FAKE_API_KEY,
assert_graphrag_configs,
get_default_graphrag_config,
)
def test_missing_openai_required_api_key() -> None:
model_config_missing_api_key = {
defs.DEFAULT_CHAT_MODEL_ID: {
"type": ModelType.OpenAIChat,
"model": defs.DEFAULT_CHAT_MODEL,
Refactor config (#1593) * 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>
2025-01-21 15:52:06 -08:00
},
defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
}
# API Key required for OpenAIChat
with pytest.raises(ValidationError):
create_graphrag_config({"models": model_config_missing_api_key})
# API Key required for OpenAIEmbedding
model_config_missing_api_key[defs.DEFAULT_CHAT_MODEL_ID]["type"] = (
ModelType.OpenAIEmbedding
Refactor config (#1593) * 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>
2025-01-21 15:52:06 -08:00
)
with pytest.raises(ValidationError):
create_graphrag_config({"models": model_config_missing_api_key})
def test_missing_azure_api_key() -> None:
model_config_missing_api_key = {
defs.DEFAULT_CHAT_MODEL_ID: {
"type": ModelType.AzureOpenAIChat,
"auth_type": AuthType.APIKey,
"model": defs.DEFAULT_CHAT_MODEL,
Refactor config (#1593) * 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>
2025-01-21 15:52:06 -08:00
"api_base": "some_api_base",
"api_version": "some_api_version",
"deployment_name": "some_deployment_name",
},
defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
}
with pytest.raises(ValidationError):
create_graphrag_config({"models": model_config_missing_api_key})
# API Key not required for managed identity
model_config_missing_api_key[defs.DEFAULT_CHAT_MODEL_ID]["auth_type"] = (
AuthType.AzureManagedIdentity
Refactor config (#1593) * 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>
2025-01-21 15:52:06 -08:00
)
create_graphrag_config({"models": model_config_missing_api_key})
def test_conflicting_auth_type() -> None:
model_config_invalid_auth_type = {
defs.DEFAULT_CHAT_MODEL_ID: {
"auth_type": AuthType.AzureManagedIdentity,
"type": ModelType.OpenAIChat,
"model": defs.DEFAULT_CHAT_MODEL,
},
defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
}
with pytest.raises(ValidationError):
create_graphrag_config({"models": model_config_invalid_auth_type})
Refactor config (#1593) * 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>
2025-01-21 15:52:06 -08:00
def test_conflicting_azure_api_key() -> None:
model_config_conflicting_api_key = {
defs.DEFAULT_CHAT_MODEL_ID: {
"type": ModelType.AzureOpenAIChat,
"auth_type": AuthType.AzureManagedIdentity,
"model": defs.DEFAULT_CHAT_MODEL,
Refactor config (#1593) * 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>
2025-01-21 15:52:06 -08:00
"api_base": "some_api_base",
"api_version": "some_api_version",
"deployment_name": "some_deployment_name",
"api_key": "THIS_SHOULD_NOT_BE_SET_WHEN_USING_MANAGED_IDENTITY",
},
defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
}
with pytest.raises(ValidationError):
create_graphrag_config({"models": model_config_conflicting_api_key})
base_azure_model_config = {
"type": ModelType.AzureOpenAIChat,
"auth_type": AuthType.AzureManagedIdentity,
"model": defs.DEFAULT_CHAT_MODEL,
Refactor config (#1593) * 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>
2025-01-21 15:52:06 -08:00
"api_base": "some_api_base",
"api_version": "some_api_version",
"deployment_name": "some_deployment_name",
}
def test_missing_azure_api_base() -> None:
missing_api_base_config = base_azure_model_config.copy()
del missing_api_base_config["api_base"]
with pytest.raises(ValidationError):
create_graphrag_config({
"models": {
defs.DEFAULT_CHAT_MODEL_ID: missing_api_base_config,
defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
}
})
def test_missing_azure_api_version() -> None:
missing_api_version_config = base_azure_model_config.copy()
del missing_api_version_config["api_version"]
with pytest.raises(ValidationError):
create_graphrag_config({
"models": {
defs.DEFAULT_CHAT_MODEL_ID: missing_api_version_config,
defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
}
})
def test_missing_azure_deployment_name() -> None:
missing_deployment_name_config = base_azure_model_config.copy()
del missing_deployment_name_config["deployment_name"]
with pytest.raises(ValidationError):
create_graphrag_config({
"models": {
defs.DEFAULT_CHAT_MODEL_ID: missing_deployment_name_config,
defs.DEFAULT_EMBEDDING_MODEL_ID: DEFAULT_EMBEDDING_MODEL_CONFIG,
}
})
def test_default_config() -> None:
expected = get_default_graphrag_config()
actual = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
assert_graphrag_configs(actual, expected)
@mock.patch.dict(os.environ, {"CUSTOM_API_KEY": FAKE_API_KEY}, clear=True)
def test_load_minimal_config() -> None:
cwd = Path(__file__).parent
root_dir = (cwd / "fixtures" / "minimal_config").resolve()
expected = get_default_graphrag_config(str(root_dir))
actual = load_config(root_dir=root_dir)
assert_graphrag_configs(actual, expected)
@mock.patch.dict(os.environ, {"CUSTOM_API_KEY": FAKE_API_KEY}, clear=True)
def test_load_config_with_cli_overrides() -> None:
cwd = Path(__file__).parent
root_dir = (cwd / "fixtures" / "minimal_config").resolve()
output_dir = "some_output_dir"
expected_output_base_dir = root_dir / output_dir
expected = get_default_graphrag_config(str(root_dir))
expected.output.base_dir = str(expected_output_base_dir)
actual = load_config(
root_dir=root_dir,
cli_overrides={"output.base_dir": output_dir},
Refactor config (#1593) * 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>
2025-01-21 15:52:06 -08:00
)
assert_graphrag_configs(actual, expected)
def test_load_config_missing_env_vars() -> None:
cwd = Path(__file__).parent
root_dir = (cwd / "fixtures" / "minimal_config_missing_env_var").resolve()
with pytest.raises(KeyError):
load_config(root_dir=root_dir)