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.
84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
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())
|