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Hande 5f8a3e24bd refactor: restructure examples and starter kit into new-examples (#1862 ) <!-- .github/pull_request_template.md --> ## Description <!-- Please provide a clear, human-generated description of the changes in this PR. DO NOT use AI-generated descriptions. We want to understand your thought process and reasoning. --> ## 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 - [x] 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. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * Documentation * Deprecated legacy examples and added a migration guide mapping old paths to new locations * Added a comprehensive new-examples README detailing configurations, pipelines, demos, and migration notes * New Features * Added many runnable examples and demos: database configs, embedding/LLM setups, permissions and access-control, custom pipelines (organizational, product recommendation, code analysis, procurement), multimedia, visualization, temporal/ontology demos, and a local UI starter * Chores * Updated CI/test entrypoints to use the new-examples layout <sub>✏️ Tip: You can customize this high-level summary in your review settings.</sub> <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: lxobr <122801072+lxobr@users.noreply.github.com>		2025-12-20 02:07:28 +01:00
..
src	improve structure, readability	2025-09-04 16:20:36 +02:00
.env.template	move to gpt5	2025-08-17 12:19:34 +02:00
.gitignore	fix: Add getting started tutorial to git (#870 )	2025-06-09 16:57:33 +02:00
pyproject.toml	feat: s3 storage (#988 )	2025-07-14 21:47:08 +02:00
README.md	refactor: restructure examples and starter kit into new-examples (#1862 )	2025-12-20 02:07:28 +01:00

README.md

⚠️ DEPRECATED - Go to `new-examples/` Instead

This starter kit is deprecated. Its examples have been integrated into the /new-examples/ folder.

Old Location	New Location
`src/pipelines/default.py`	none
`src/pipelines/low_level.py`	`new-examples/custom_pipelines/organizational_hierarchy/`
`src/pipelines/custom-model.py`	`new-examples/demos/custom_graph_model_entity_schema_definition.py`
`src/data/`	Included in `new-examples/custom_pipelines/organizational_hierarchy/data/`

Cognee Starter Kit

Welcome to the cognee Starter Repo! This repository is designed to help you get started quickly by providing a structured dataset and pre-built data pipelines using cognee to build powerful knowledge graphs.

You can use this repo to ingest, process, and visualize data in minutes.

By following this guide, you will:

Load structured company and employee data
Utilize pre-built pipelines for data processing
Perform graph-based search and query operations
Visualize entity relationships effortlessly on a graph

How to Use This Repo 🛠

Install uv if you don't have it on your system

pip install uv

Install dependencies

uv sync

Setup LLM

Add environment variables to .env file. In case you choose to use OpenAI provider, add just the model and api_key.

LLM_PROVIDER=""
LLM_MODEL=""
LLM_ENDPOINT=""
LLM_API_KEY=""
LLM_API_VERSION=""

EMBEDDING_PROVIDER=""
EMBEDDING_MODEL=""
EMBEDDING_ENDPOINT=""
EMBEDDING_API_KEY=""
EMBEDDING_API_VERSION=""

Activate the Python environment:

source .venv/bin/activate

Run the Default Pipeline

This script runs the cognify pipeline with default settings. It ingests text data, builds a knowledge graph, and allows you to run search queries.

python src/pipelines/default.py

Run the Low-Level Pipeline

This script implements its own pipeline with custom ingestion task. It processes the given JSON data about companies and employees, making it searchable via a graph.

python src/pipelines/low_level.py

Run the Custom Model Pipeline

Custom model uses custom pydantic model for graph extraction. This script categorizes programming languages as an example and visualizes relationships.

python src/pipelines/custom-model.py

Graph preview

cognee provides a visualize_graph function that will render the graph for you.

    graph_file_path = str(
        pathlib.Path(
            os.path.join(pathlib.Path(__file__).parent, ".artifacts/graph_visualization.html")
        ).resolve()
    )
    await visualize_graph(graph_file_path)

What will you build with cognee?

Expand the dataset by adding more structured/unstructured data
Customize the data model to fit your use case
Use the search API to build an intelligent assistant
Visualize knowledge graphs for better insights