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Vertex embeddings module #1435
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Memgraph 3.6.1
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Vertex embeddings module #1435
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Oct 15, 2025
The issue is that during Dockerfile build, CPU version gets installed, when memgraph starts, it loads the CPU version, and then it's not possible to reload the loaded module using the `CALL mg.load_all()`. Should we only install the GPU version -> https://stackoverflow.com/questions/58511598/can-both-the-gpu-and-cpu-versions-of-pytorch-be-installed-in-the-same-conda-envi? -> NO, because of the size, take a look at the comment below ### Description Added: - `embeddings` module to compute vertex sentence embeddings. - CUDA-enabled build of MAGE for daily and RC builds. ### Pull request type - [ ] Bugfix - [x] Algorithm/Module - [ ] Feature - [ ] Code style update (formatting, renaming) - [ ] Refactoring (no functional changes, no api changes) - [ ] Build related changes - [ ] Documentation content changes - [ ] Other (please describe): ###################################### ### Reviewer checklist (the reviewer checks this part) #### Module/Algorithm - [ ] Core algorithm/module implementation - [ ] [Query module](https://memgraph.com/docs/memgraph/reference-guide/query-modules) implementation - [ ] Tests provided (unit / e2e) - [ ] Code documentation - [ ] README short description ### Documentation checklist - [x] Add the documentation label tag - [x] Add the bug / feature label tag - [x] Add the milestone for which this feature is intended - If not known, set for a later milestone - [x] Write a release note, including added/changed clauses - Added `embedding` module, allowing users to compute sentence embeddings for vertices on CPU or Nvidia GPU. [#662](#662). - [x] Link the documentation PR here - memgraph/documentation#1435 --------- Co-authored-by: Dr Matt James <[email protected]>
matea16
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Oct 17, 2025
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Release note
Added
embeddingmodule, allowing users to compute sentence embeddings for vertices on CPU or Nvidia GPU. memgraph/mage#662.Related product PRs
PRs from product repo this doc page is related to:
memgraph/mage#662
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bugfixorfeaturelabel, based on the product PR type you're documenting