0.0.16

New features

Bulk search (#363, #373).
Conduct multiple searches with just one request. This improves search throughput in Marqo by parallelising multiple search queries in a single API call.
The average search time can be decreased up to 30%, depending on your devices and models.
Check out the usage guide here
Configurable number of index replicas (#391).
You can now configure how many replicas to make for an index in Marqo using the number_of_replicas parameter. Marqo makes 1 replica by default.
We recommend having at least one replica to prevent data loss.
See the usage guide here
Use your own vectors during searches (#381). Use your own vectors as context for your queries.
Your vectors will be incorporated into the query using a weighted sum approach,
allowing you to reduce the number of inference requests for duplicated content.
Check out the usage guide here

Fixed a bug where some Open CLIP models were unable to load checkpoints from the cache (#387).
Fixed a bug where multimodal search vectors are not combined based on expected weights (#383).
Fixed a bug where multimodal document vectors are not combined in an expected way. numpy.sum was used rather than numpy.mean. (#384).
Fixed a bug where an unexpected error is thrown when using_existing_tensor = True and documents are added with duplicate IDs (#390).
Fixed a bug where the index settings validation did not catch the model field if it is in the incorrect part of the settings json (#365).
Added missing descriptions and requirement files on our GPT-examples (#349).
Updated the instructions to start Marqo-os (#371).
Improved the Marqo start-up time by incorporating the downloading of the punkt tokenizer into the dockerfile (#346).

Thank you to our 2.5k stargazers.
Thank you to @ed-muthiah for submitting a PR (#349)
that added missing descriptions and requirement files on our GPT-examples.

Release images can be found on Docker hub