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- Improved list handling, removed `.index` logic from
`get_graph_from_model`, transitioned to fully datapoint-oriented
processing
- Streamlined datapoint iteration by introducing `_datapoints_generator`
with nested loops
- Generalized field processing to handle mixed lists: `[DataPoint,
(Edge, DataPoint), (Edge, [DataPoint])]`, allowing dynamic multiple
edges generation
- Small improvements and refactorings
- Added tests to `test_get_graph_from_model_flexible_edges()` covering
weighted edges and dynamic multiple edges
- Created `dynamic_multiple_edges_example.py` demonstrating dynamic
multiple edges
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Signed-off-by: Raj2604 <rajmandhare26@gmail.com>
Co-authored-by: Daulet Amirkhanov <damirkhanov01@gmail.com>
Co-authored-by: Hande <159312713+hande-k@users.noreply.github.com>
Co-authored-by: Igor Ilic <igorilic03@gmail.com>
Co-authored-by: Igor Ilic <30923996+dexters1@users.noreply.github.com>
Co-authored-by: Boris <boris@topoteretes.com>
Co-authored-by: Matea Pesic <80577904+matea16@users.noreply.github.com>
Co-authored-by: github-actions[bot] <github-actions@users.noreply.github.com>
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Co-authored-by: Raj Mandhare <96978537+Raj2604@users.noreply.github.com>
Co-authored-by: Pedro Thompson <thompsonp17@hotmail.com>
Co-authored-by: Pedro Henrique Thompson Furtado <pedrothompson@petrobras.com.br>
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## Description
Update rel db example with better info
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Co-authored-by: Boris Arzentar <borisarzentar@gmail.com>
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## Description
PR from user jspv, running it through our branch to test CI/CD
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Signed-off-by: Diego B Theuerkauf <diego.theuerkauf@tuebingen.mpg.de>
Co-authored-by: Boris <boris@topoteretes.com>
Co-authored-by: vasilije <vas.markovic@gmail.com>
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## Description
Adds agentic procurement example
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## Description
Update migration example for upcoming blog on main branch
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## Description
Update relational db migration example so it's more understandable
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Co-authored-by: Igor Ilic <30923996+dexters1@users.noreply.github.com>
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## Description
Removes graph metrics calculation from dynamic steps and ontology demos
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## Description
Delete by document.
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- Extracted run_tasks_base function into a new file run_tasks_base.py.
- Extracted four executors that execute core logic based on the task
type.
- Extracted a task handler/wrapper that safely executes the core logic
with logging and telemetry.
- Fixed the inconsistency with the batches of size 1.
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- Fixed get_no_summary_tasks and get_just_chunks_tasks to work with the
new tasks and pipelines
- Chore: fixed the pokemon example to work with the new tasks and
pipelines
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## Description
- Redirect all Cognee output to stderr for MCP ( as stdout is used to
communicate between MCP Client and server )
- Add test for CODE search type
- Resolve missing optional GUI dependency
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## Description
Add ability to migrate relational database to graph database
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## Description
Adds ontology demo 2
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Co-authored-by: Igor Ilic <30923996+dexters1@users.noreply.github.com>
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## Description
Introducing scructlog.
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## Description
Temporarily remove embedding env variables for code graph action so the
action can run
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<!-- This is an auto-generated comment: release notes by coderabbit.ai
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## Summary by CodeRabbit
- **Chores**
- Removed legacy secret configuration from the testing workflow to
streamline the CI process and enhance maintainability.
- **Improvements**
- Updated the argument name in the code graph pipeline for clarity.
- Enhanced the handling of results in the example script to support
asynchronous processing.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
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## Description
This PR contains the ontology feature integrated into cognify
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## Summary by CodeRabbit
- **New Features**
- Enhanced ontology management with the introduction of the
`OntologyResolver` class for improved data handling and querying.
- Expanded ontology framework now provides enriched coverage of
technology and automotive domains, including new entities and
relationships.
- Updated entity models now include a validation flag to support
improved data integrity.
- Added support for specifying an ontology file path in relevant
functions to enhance flexibility.
- **Refactor**
- Streamlined integration of ontology processing across data extraction
and workflow routines.
- **Chores**
- Updated project dependencies to include `owlready2` for advanced
ontology functionality.
- **Tests**
- Introduced a new test suite for the `OntologyResolver` class to
validate its functionality under various conditions.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
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## 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.
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## 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>