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This PR introduces wide vector and graph structure filtering
capabilities. With these changes, the graph completion retriever and all
retrievers that inherit from it will now filter relevant vector elements
and subgraphs based on the query. This improvement significantly
increases search speed for large graphs while maintaining—and in some
cases slightly improving—accuracy.
Changes in This PR:
-Introduced new wide_search_top_k parameter: Controls the initial search
space size
-Added graph adapter level filtering method: Enables relevant subgraph
filtering while maintaining backward compatibility. For community or
custom graph adapters that don't implement this method, the system
gracefully falls back to the original search behavior.
-Updated modal dashboard and evaluation framework: Fixed compatibility
issues.
Added comprehensive unit tests: Introduced unit tests for
brute_force_triplet_search (previously untested) and expanded the
CogneeGraph test suite.
Integration tests: Existing integration tests verify end-to-end search
functionality (no changes required).
Acceptance Criteria and Testing
To verify the new search behavior, run search queries with different
wide_search_top_k parameters while logging is enabled:
None: Triggers a full graph search (default behavior)
1: Projects a minimal subgraph (demonstrates maximum filtering)
Custom values: Test intermediate levels of filtering
Internal Testing and results:
Performance and accuracy benchmarks are available upon request. The
implementation demonstrates measurable improvements in query latency for
large graphs without sacrificing result quality.
## Type of Change
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- [ ] 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
- [x] Performance improvement
- [ ] Other (please specify):
## Screenshots/Videos (if applicable)
None
## Pre-submission Checklist
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- [x] **I have tested my changes thoroughly before submitting this PR**
- [x] **This PR contains minimal changes necessary to address the
issue/feature**
- [x] My code follows the project's coding standards and style
guidelines
- [x] I have added tests that prove my fix is effective or that my
feature works
- [x] I have added necessary documentation (if applicable)
- [x] All new and existing tests pass
- [x] I have searched existing PRs to ensure this change hasn't been
submitted already
- [x] I have linked any relevant issues in the description
- [x] 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.
---------
Co-authored-by: Pavel Zorin <pazonec@yandex.ru>
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## Description
This PR fixes the failing CI tests related to the new ontology api
endpoint.
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## Type of Change
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- [x] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
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## Screenshots/Videos (if applicable)
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## Pre-submission Checklist
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issue/feature**
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feature works
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## Description
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## Type of Change
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## Description
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## Type of Change
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## Description
This PR resolves#1446 by adding support to upload ontology files and
refer to them in the cognee POST request.
Implementation Details:
- New endpoint: POST /api/v1/ontologies for ontology file upload with a
simple key parameter
that can be referenced in POST cognify requests
- File storage: Ontology files are stored in /tmp/ontologies/{user_id}/
with metadata
management
- New service: OntologyService created for file management and metadata
handling
- Resolver: RDFLibOntologyResolver modified to handle file-like objects.
## Type of Change
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- [X] New feature (non-breaking change that adds functionality)
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## Description
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Create a dedicated CSV ingestion path with a custom loader and custom
chunker that preserves row-column relationships in the produced chunks.
#1348
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## Description
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Added a variable to control which instructor mode we use. The defaults
for each adapter are used, but a user can override this if the set the
`LLM_INSTRUCTOR_MODE` env variable.
## Type of Change
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## Description
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