bee2fe3ba7
<!-- .github/pull_request_template.md --> ## Description Add run_custom_pipeline to have a way to execute a custom collection of tasks in Cognee ## Type of Change <!-- Please check the relevant option --> - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) <!-- Add screenshots or videos to help explain your changes --> ## Pre-submission Checklist <!-- Please check all boxes that apply before submitting your PR --> - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin.
69 lines
2.9 KiB
Python
69 lines
2.9 KiB
Python
from typing import Union, Optional, List, Type, Any
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from uuid import UUID
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from cognee.shared.logging_utils import get_logger
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from cognee.modules.pipelines import run_pipeline
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from cognee.modules.pipelines.tasks.task import Task
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from cognee.modules.users.models import User
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from cognee.modules.pipelines.layers.pipeline_execution_mode import get_pipeline_executor
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logger = get_logger()
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async def run_custom_pipeline(
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tasks: Union[List[Task], List[str]] = None,
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data: Any = None,
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dataset: Union[str, UUID] = "main_dataset",
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user: User = None,
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vector_db_config: Optional[dict] = None,
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graph_db_config: Optional[dict] = None,
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data_per_batch: int = 20,
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run_in_background: bool = False,
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pipeline_name: str = "custom_pipeline",
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):
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"""
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Custom pipeline in Cognee, can work with already built graphs. Data needs to be provided which can be processed
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with provided tasks.
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Provided tasks and data will be arranged to run the Cognee pipeline and execute graph enrichment/creation.
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This is the core processing step in Cognee that converts raw text and documents
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into an intelligent knowledge graph. It analyzes content, extracts entities and
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relationships, and creates semantic connections for enhanced search and reasoning.
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Args:
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tasks: List of Cognee Tasks to execute.
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data: The data to ingest. Can be anything when custom extraction and enrichment tasks are used.
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Data provided here will be forwarded to the first extraction task in the pipeline as input.
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dataset: Dataset name or dataset uuid to process.
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user: User context for authentication and data access. Uses default if None.
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vector_db_config: Custom vector database configuration for embeddings storage.
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graph_db_config: Custom graph database configuration for relationship storage.
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data_per_batch: Number of data items to be processed in parallel.
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run_in_background: If True, starts processing asynchronously and returns immediately.
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If False, waits for completion before returning.
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Background mode recommended for large datasets (>100MB).
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Use pipeline_run_id from return value to monitor progress.
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"""
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custom_tasks = [
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*tasks,
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]
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# By calling get pipeline executor we get a function that will have the run_pipeline run in the background or a function that we will need to wait for
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pipeline_executor_func = get_pipeline_executor(run_in_background=run_in_background)
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# Run the run_pipeline in the background or blocking based on executor
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return await pipeline_executor_func(
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pipeline=run_pipeline,
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tasks=custom_tasks,
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user=user,
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data=data,
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datasets=dataset,
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vector_db_config=vector_db_config,
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graph_db_config=graph_db_config,
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incremental_loading=False,
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data_per_batch=data_per_batch,
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pipeline_name=pipeline_name,
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)
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