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## Description
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This PR removes codify, and the code graph pipeline, out of the
repository. It also introduces a Custom Pipeline interface, which can be
used in the future to define custom pipelines.
## Type of Change
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## Description
This PR introduces triplet embeddings via a new
create_triplet_embeddings memify pipeline.
The pipeline reads the graph in batches, extracts properties from graph
elements based on their datapoint types, and generates combined triplet
embeddings. These embeddings are stored in the vector database as a new
collection.
Changes in This PR:
-Added a new create_triplet_embeddings memify pipeline.
-Added a new get_triplet_datapoints memify task.
-Introduced a new triplet_completion search type.
-Added full test coverage
--Unit tests: memify task, pipeline, and retriever
--Integration tests: memify task, pipeline, and retriever
--End-to-end tests: updated session history tests and multi-DB search
tests; added tests for triplet_completion and memify pipeline execution
Acceptance Criteria and Testing
Scenario 1:
-Run default add, cognify pipelines
-Run create triplet embeddings memify pipeline
-Verify the vector DB contains a non empty Triplet_text collection.
-Use the new triplet_completion search type and confirm it works
correctly.
Scenario 2:
-Run the default add and cognify pipelines.
-Do not run the triplet embeddings memify pipeline.
-Attempt to use the triplet_completion search type.
-You should receive an error indicating that the triplet embeddings
memify pipeline must be executed first.
## Type of Change
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- [x] New feature (non-breaking change that adds functionality)
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## Summary by CodeRabbit
* **New Features**
* Triplet-based search with LLM-powered completions (TRIPLET_COMPLETION)
* Batch triplet retrieval and a triplet embeddings pipeline for
extraction, indexing, and optional background processing
* Context retrieval from triplet embeddings with optional caching and
conversation-history support
* New Triplet data type exposed for indexing and search
* **Examples**
* End-to-end example demonstrating triplet embeddings extraction and
TRIPLET_COMPLETION search
* **Tests**
* Unit and integration tests covering triplet extraction, retrieval,
embedding pipeline, and completion flows
<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: Pavel Zorin <pazonec@yandex.ru>
…oviders support it
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## Description
Enable multi user mode by default for supported graph and vector DBs
## Type of Change
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## Description
This PR introduces a new memify pipeline to save cache sessions in
cognee. The QA sessions are added to the main knowledge base as separate
documents.
## Type of Change
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## Screenshots/Videos (if applicable)
None
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## Description
Add run_custom_pipeline to have a way to execute a custom collection of tasks in Cognee
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## Description
Adds the concept of now to the qa for temporal queries
## Type of Change
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## Description
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As per COG-3050:
1. Remove insights search type and clean up any orphaned code
2. Replace callsites with default search type - `GRAPH_COMPLETION` -
where applicable
## Type of Change
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… db migration
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## Description
Add return value of creating role and tenant, add detailed permissions
example to Cognee
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---------
Co-authored-by: Boris <boris@topoteretes.com>
Co-authored-by: Hande <159312713+hande-k@users.noreply.github.com>
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
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## Description
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Falkordb adapter didn't work on main repo, but we have it working on
community. Decision was to remove it from main repo, so it is removed.
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## Description
Enhance search results of relational db data by adding more information
on data type and content
PR also includes schema migration done by contributor ( Geoff-Robin )
from contribute to win competition
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- [x] Performance improvement
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Until now, limit=0 in vector search meant that there is no limit and we
should return everything. This caused confusion and errors, so now it is
reworked so that limit=None means no limit on the search. If someone
puts limit=0, there will be no results returned, as it makes more sense
and is less error prone.
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