Merge branch 'dev' into multi-tenancy

.github/workflows/examples_tests.yml

@@ -210,6 +210,31 @@ jobs:
           EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
         run: uv run python ./examples/python/memify_coding_agent_example.py
 
+  test-custom-pipeline:
+    name: Run Custom Pipeline Example
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v4
+
+      - name: Cognee Setup
+        uses: ./.github/actions/cognee_setup
+        with:
+          python-version: '3.11.x'
+
+      - name: Run Custom Pipeline Example
+        env:
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          LLM_MODEL: ${{ secrets.LLM_MODEL }}
+          LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
+          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
+          LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
+          EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
+          EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
+          EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
+          EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
+        run: uv run python ./examples/python/run_custom_pipeline_example.py
+
   test-permissions-example:
     name: Run Permissions Example
     runs-on: ubuntu-22.04

cognee/__init__.py

@@ -19,6 +19,7 @@ from .api.v1.add import add
 from .api.v1.delete import delete
 from .api.v1.cognify import cognify
 from .modules.memify import memify
+from .modules.run_custom_pipeline import run_custom_pipeline
 from .api.v1.update import update
 from .api.v1.config.config import config
 from .api.v1.datasets.datasets import datasets

cognee/modules/run_custom_pipeline/__init__.py

@@ -0,0 +1 @@
+from .run_custom_pipeline import run_custom_pipeline

cognee/modules/run_custom_pipeline/run_custom_pipeline.py

@@ -0,0 +1,69 @@
+from typing import Union, Optional, List, Type, Any
+from uuid import UUID
+
+from cognee.shared.logging_utils import get_logger
+
+from cognee.modules.pipelines import run_pipeline
+from cognee.modules.pipelines.tasks.task import Task
+from cognee.modules.users.models import User
+from cognee.modules.pipelines.layers.pipeline_execution_mode import get_pipeline_executor
+
+logger = get_logger()
+
+
+async def run_custom_pipeline(
+    tasks: Union[List[Task], List[str]] = None,
+    data: Any = None,
+    dataset: Union[str, UUID] = "main_dataset",
+    user: User = None,
+    vector_db_config: Optional[dict] = None,
+    graph_db_config: Optional[dict] = None,
+    data_per_batch: int = 20,
+    run_in_background: bool = False,
+    pipeline_name: str = "custom_pipeline",
+):
+    """
+    Custom pipeline in Cognee, can work with already built graphs. Data needs to be provided which can be processed
+    with provided tasks.
+
+    Provided tasks and data will be arranged to run the Cognee pipeline and execute graph enrichment/creation.
+
+    This is the core processing step in Cognee that converts raw text and documents
+    into an intelligent knowledge graph. It analyzes content, extracts entities and
+    relationships, and creates semantic connections for enhanced search and reasoning.
+
+    Args:
+        tasks: List of Cognee Tasks to execute.
+        data: The data to ingest. Can be anything when custom extraction and enrichment tasks are used.
+              Data provided here will be forwarded to the first extraction task in the pipeline as input.
+        dataset: Dataset name or dataset uuid to process.
+        user: User context for authentication and data access. Uses default if None.
+        vector_db_config: Custom vector database configuration for embeddings storage.
+        graph_db_config: Custom graph database configuration for relationship storage.
+        data_per_batch: Number of data items to be processed in parallel.
+        run_in_background: If True, starts processing asynchronously and returns immediately.
+                          If False, waits for completion before returning.
+                          Background mode recommended for large datasets (>100MB).
+                          Use pipeline_run_id from return value to monitor progress.
+    """
+
+    custom_tasks = [
+        *tasks,
+    ]
+
+    # 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
+    pipeline_executor_func = get_pipeline_executor(run_in_background=run_in_background)
+
+    # Run the run_pipeline in the background or blocking based on executor
+    return await pipeline_executor_func(
+        pipeline=run_pipeline,
+        tasks=custom_tasks,
+        user=user,
+        data=data,
+        datasets=dataset,
+        vector_db_config=vector_db_config,
+        graph_db_config=graph_db_config,
+        incremental_loading=False,
+        data_per_batch=data_per_batch,
+        pipeline_name=pipeline_name,
+    )

cognee/modules/users/methods/get_default_user.py

@@ -10,7 +10,7 @@ from cognee.infrastructure.databases.relational import get_relational_engine
 from cognee.modules.users.methods.create_default_user import create_default_user
 
 
-async def get_default_user() -> SimpleNamespace:
+async def get_default_user() -> User:
     db_engine = get_relational_engine()
     base_config = get_base_config()
     default_email = base_config.default_user_email or "default_user@example.com"

examples/python/run_custom_pipeline_example.py

@@ -0,0 +1,84 @@
+import asyncio
+import cognee
+from cognee.modules.engine.operations.setup import setup
+from cognee.modules.users.methods import get_default_user
+from cognee.shared.logging_utils import setup_logging, INFO
+from cognee.modules.pipelines import Task
+from cognee.api.v1.search import SearchType
+
+# Prerequisites:
+# 1. Copy `.env.template` and rename it to `.env`.
+# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
+#    LLM_API_KEY = "your_key_here"
+
+
+async def main():
+    # Create a clean slate for cognee -- reset data and system state
+    print("Resetting cognee data...")
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata=True)
+    print("Data reset complete.\n")
+
+    # Create relational database and tables
+    await setup()
+
+    # cognee knowledge graph will be created based on this text
+    text = """
+    Natural language processing (NLP) is an interdisciplinary
+    subfield of computer science and information retrieval.
+    """
+
+    print("Adding text to cognee:")
+    print(text.strip())
+
+    # Let's recreate the cognee add pipeline through the custom pipeline framework
+    from cognee.tasks.ingestion import ingest_data, resolve_data_directories
+
+    user = await get_default_user()
+
+    # Values for tasks need to be filled before calling the pipeline
+    add_tasks = [
+        Task(resolve_data_directories, include_subdirectories=True),
+        Task(
+            ingest_data,
+            "main_dataset",
+            user,
+        ),
+    ]
+    # Forward tasks to custom pipeline along with data and user information
+    await cognee.run_custom_pipeline(
+        tasks=add_tasks, data=text, user=user, dataset="main_dataset", pipeline_name="add_pipeline"
+    )
+    print("Text added successfully.\n")
+
+    # Use LLMs and cognee to create knowledge graph
+    from cognee.api.v1.cognify.cognify import get_default_tasks
+
+    cognify_tasks = await get_default_tasks(user=user)
+    print("Recreating existing cognify pipeline in custom pipeline to create knowledge graph...\n")
+    await cognee.run_custom_pipeline(
+        tasks=cognify_tasks, user=user, dataset="main_dataset", pipeline_name="cognify_pipeline"
+    )
+    print("Cognify process complete.\n")
+
+    query_text = "Tell me about NLP"
+    print(f"Searching cognee for insights with query: '{query_text}'")
+    # Query cognee for insights on the added text
+    search_results = await cognee.search(
+        query_type=SearchType.GRAPH_COMPLETION, query_text=query_text
+    )
+
+    print("Search results:")
+    # Display results
+    for result_text in search_results:
+        print(result_text)
+
+
+if __name__ == "__main__":
+    logger = setup_logging(log_level=INFO)
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        loop.run_until_complete(main())
+    finally:
+        loop.run_until_complete(loop.shutdown_asyncgens())