56427f287e
<!-- .github/pull_request_template.md --> ## Description This demo uses pydantic models and dlt to pull data from the Pokémon API and structure it into a relational format. By feeding this structured data into cognee, it makes searching across multiple tables easier and more intuitive, thanks to the relational model. ## 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 - **New Features** - Introduced a comprehensive Pokémon data processing pipeline, available as both a Python script and an interactive Jupyter Notebook. - Enabled asynchronous operations for efficient data collection and querying, including an integrated search functionality. - Improved error handling and data validation during the data fetching and processing stages for a smoother user experience. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Co-authored-by: Vasilije <8619304+Vasilije1990@users.noreply.github.com> |
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| .. | ||
| data | ||
| cognee_code_graph_demo.ipynb | ||
| cognee_demo.ipynb | ||
| cognee_graphiti_demo.ipynb | ||
| cognee_hotpot_eval.ipynb | ||
| cognee_llama_index.ipynb | ||
| cognee_multimedia_demo.ipynb | ||
| cognee_simple_demo.ipynb | ||
| graphrag_vs_rag.ipynb | ||
| hr_demo.ipynb | ||
| llama_index_cognee_integration.ipynb | ||
| pokemon_datapoints_notebook.ipynb | ||