.. _relnotes_v0.10.1:

v0.10.1
=======

.. _relnotes_v0.10.1_Upgrade Notes:

Upgrade Notes
-------------

- ``ExternalSearchIndex.from_faiss_index`` no longer takes ``vectors``, so
  passing them raises ``TypeError``. For indexes that need the exact 
  vectors for reconstruction, pass them to the constructor instead: 
  ``ExternalSearchIndex(index, vectors)``.

- ``ExternalSearchIndex.from_faiss_index`` now raises ``ValueError`` for an
  IVF index that has no direct map. Call
  ``faiss.extract_index_ivf(index).make_direct_map()`` first, or pass the
  vectors to ``ExternalSearchIndex(index, vectors)``.

- Loading an ``InMemoryImageEmbeddingStore`` file saved by an earlier version
  raises ``ValueError``. Build the store from its images again and save it.

- ``InMemoryImageEmbeddingStore.save_to_disk`` ignores ``embeddings`` on a
  store on an HNSW graph and emits a ``FutureWarning``, since the file holds
  the graph with the embeddings in it.


.. _relnotes_v0.10.1_Performance:

Performance
-----------

- Building or updating a ``BruteForceIndex`` no longer copies the gallery
  first, which halves the extra peak memory and saves 14 to 17 % of the
  time. For 500 000 vectors of dimension 512 the peak went from 1985 MB to
  1008 MB, and the build from 726 ms to 605 ms.
  
  Specs: 16 threads, 30 GB RAM, Python 3.14.3, NumPy 2.5.3.

- ``ExternalSearchIndex.from_faiss_index`` no longer keeps a copy of the
  gallery and reads the vectors from the FAISS index instead. For 500 000
  vectors of dimension 512 the adapter's peak memory went from 1952 MB to
  0 MB and its steady memory from 977 MB to 0 MB.
  
  Specs: 16 threads, 30 GB RAM, Python 3.14.3, NumPy 2.5.3, faiss-cpu 1.15.0.

- Building or updating an ``HnswIndex`` no longer copies the gallery first,
  which saves one gallery of peak memory. For 500 000 vectors of dimension
  512 the extra peak went from 2097 MB to 1120 MB.
  
  Specs: 16 threads, 30 GB RAM, Python 3.14.3, NumPy 2.5.3.

- A store on an HNSW graph saves the graph and loads it back without building
  it again. Loading a store of 100 000 embeddings of dimension 512 went from
  22.7 s to 0.2 s, and saving it from 2.4 s to 0.2 s.
  
  Specs: 16 threads, 30 GB RAM, Python 3.14.3,
  NumPy 2.4.6, safetensors 0.8.0.


.. _relnotes_v0.10.1_Bug Fixes:

Bug Fixes
---------

- A loaded HNSW store returns the same neighbors as the store that was saved.
  Before, the graph was built again on load, which with several threads could
  return different neighbors.