mirror of
https://github.com/microsoft/graphrag.git
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94f1e62e5c
* Rename pipeline_storage file * Add runtime storage option to context * Fix import * Switch to memory storage for runtime * Infra for workflow runtime storage * Migrate base_text_units to runtime storage * Fix comment * Semver * Remove whitespace * Remove subflow smoke tests and ignore transient artifacts * Remove entity graph from transient list (not yet implemented) * Increase smoke runtime allotment for create_base_entity_graph * Revert format fix * Remove noqa
118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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"""Utility functions for the GraphRAG run module."""
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import logging
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from string import Template
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from typing import Any
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import pandas as pd
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from datashaper import (
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WorkflowCallbacks,
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)
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from graphrag.callbacks.factories import create_pipeline_reporter
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from graphrag.index.cache.memory_pipeline_cache import InMemoryCache
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from graphrag.index.cache.pipeline_cache import PipelineCache
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from graphrag.index.config.cache import (
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PipelineBlobCacheConfig,
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PipelineFileCacheConfig,
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)
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from graphrag.index.config.input import PipelineInputConfigTypes
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from graphrag.index.config.pipeline import PipelineConfig
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from graphrag.index.config.reporting import (
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PipelineBlobReportingConfig,
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PipelineFileReportingConfig,
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PipelineReportingConfigTypes,
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)
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from graphrag.index.config.storage import (
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PipelineBlobStorageConfig,
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PipelineFileStorageConfig,
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)
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from graphrag.index.context import PipelineRunContext, PipelineRunStats
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from graphrag.index.input import load_input
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from graphrag.index.storage.memory_pipeline_storage import MemoryPipelineStorage
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from graphrag.index.storage.pipeline_storage import PipelineStorage
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from graphrag.logging import ProgressReporter
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log = logging.getLogger(__name__)
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def _create_reporter(
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config: PipelineReportingConfigTypes | None, root_dir: str
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) -> WorkflowCallbacks | None:
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"""Create the reporter for the pipeline."""
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return create_pipeline_reporter(config, root_dir) if config else None
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async def _create_input(
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config: PipelineInputConfigTypes | None,
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progress_reporter: ProgressReporter | None,
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root_dir: str,
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) -> pd.DataFrame | None:
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"""Load the input for the pipeline."""
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if config is None:
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return None
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return await load_input(config, progress_reporter, root_dir)
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def _validate_dataset(dataset: Any):
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"""Validate the dataset for the pipeline.
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Args:
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- dataset - The dataset to validate
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"""
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if not isinstance(dataset, pd.DataFrame):
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msg = "Dataset must be a pandas dataframe!"
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raise TypeError(msg)
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def _apply_substitutions(config: PipelineConfig, run_id: str) -> PipelineConfig:
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"""Apply the substitutions to the configuration."""
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substitutions = {"timestamp": run_id}
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if (
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isinstance(
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config.storage, PipelineFileStorageConfig | PipelineBlobStorageConfig
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)
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and config.storage.base_dir
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):
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config.storage.base_dir = Template(config.storage.base_dir).substitute(
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substitutions
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)
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if (
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isinstance(config.cache, PipelineFileCacheConfig | PipelineBlobCacheConfig)
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and config.cache.base_dir
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):
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config.cache.base_dir = Template(config.cache.base_dir).substitute(
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substitutions
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)
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if (
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isinstance(
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config.reporting, PipelineFileReportingConfig | PipelineBlobReportingConfig
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)
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and config.reporting.base_dir
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):
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config.reporting.base_dir = Template(config.reporting.base_dir).substitute(
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substitutions
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)
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return config
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def create_run_context(
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storage: PipelineStorage | None,
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cache: PipelineCache | None,
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stats: PipelineRunStats | None,
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) -> PipelineRunContext:
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"""Create the run context for the pipeline."""
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return PipelineRunContext(
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stats=stats or PipelineRunStats(),
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cache=cache or InMemoryCache(),
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storage=storage or MemoryPipelineStorage(),
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runtime_storage=MemoryPipelineStorage(),
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)
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