Marqo

New features
- Support for custom CLIP models using the OpenAI and OpenCLIP architectures (https://github.com/marqo-ai/marqo/pull/286). Read about usage [here](https://docs.marqo.ai/0.0.13/Models-Reference/dense_retrieval/#generic-clip-models).
- Concurrency throttling (https://github.com/marqo-ai/marqo/pull/304). Configure the number of allowed concurrent indexing and search threads. Read about usage [here](https://docs.marqo.ai/0.0.13/Advanced-Usage/configuration/#configuring-throttling).
- Configurable logging levels (https://github.com/marqo-ai/marqo/pull/314). Adjust log output for your debugging/log storage needs. See how to configure log level [here](https://docs.marqo.ai/0.0.13/Advanced-Usage/configuration/#configuring-log-level).
- New array datatype (https://github.com/marqo-ai/marqo/pull/312). You can use these arrays as a collection of tags to filter on! See usage [here](https://docs.marqo.ai/0.0.13/Advanced-Usage/document_fields/#array).
- Boost tensor fields during search (https://github.com/marqo-ai/marqo/pull/300). Weight fields as higher and lower relative to each other during search. Use this to get a mix of results that suits your use case. See usage [here](https://docs.marqo.ai/0.0.13/API-Reference/search/#boost).
- Weighted multimodal queries (https://github.com/marqo-ai/marqo/pull/307). You can now search with a dictionary of weighted queries. If searching an image index, these queries can be a weighted mix of image URLs and text. See usage [here](https://docs.marqo.ai/0.0.13/API-Reference/search/#query-q).
- New GPT-Marqo integration [example](https://github.com/marqo-ai/marqo/tree/mainline/examples/GPT-examples) and [article](https://www.marqo.ai/blog/from-iron-manual-to-ironman-augmenting-gpt-with-marqo-for-fast-editable-memory-to-enable-context-aware-question-answering). Turn your boring user manual into a question-answering bot, with an optional persona, with GPT + Marqo!
- Added new OpenCLIP models to Marqo (https://github.com/marqo-ai/marqo/pull/299)

Optimisations
- Concurrent image downloads (https://github.com/marqo-ai/marqo/pull/281, https://github.com/marqo-ai/marqo/pull/311)
- Blazingly fast `fp16` ViT CLIP models (https://github.com/marqo-ai/marqo/pull/286). See usage [here](https://docs.marqo.ai/0.0.13/Models-Reference/dense_retrieval/#openai-float16)
- Reduction of data transfer between Marqo and Marqo-os (https://github.com/marqo-ai/marqo/pull/300)
- We see a 3.0x indexing speedup, and a 1.7x search speedup, using the new `fp16/ViT-L/14` CLIP model, compared to the previous release using `ViT-L/14`.

Bug fixes
- Fixed 500 error when creating an index while only specifying `number_of_shards`(https://github.com/marqo-ai/marqo/pull/293)
- Fixed model cache management no parsing reranker model properties properly (https://github.com/marqo-ai/marqo/pull/308)

Contributor shout-outs
- Thank you to our 2.3k stargazers
- Thank you to [codebrain](https://github.com/codebrain) and others for raising issues.

New features
- Multilingual CLIP (https://github.com/marqo-ai/marqo/pull/267). Search images in the language you want! Marqo now incorporates [open source multilingual CLIP models](https://github.com/FreddeFrallan/Multilingual-CLIP). A list of available multilingual CLIP models are available [here](https://docs.marqo.ai/0.0.12/Models-Reference/dense_retrieval/#multilingual-clip).
- Exact text matching (https://github.com/marqo-ai/marqo/pull/243, https://github.com/marqo-ai/marqo/pull/288). Search for specific words and phrases using double quotes (`" "`) in lexical search. See usage [here](https://docs.marqo.ai/0.0.12/API-Reference/search/#lexical-search-exact-matches).

Optimisations
- Search speed-up (https://github.com/marqo-ai/marqo/pull/278). Latency reduction from Marqo-os indexes reconfigurations.

Contributor shout-outs
Thank you to our 2.2k stargazers and 80+ forkers!

New features
- Pagination (https://github.com/marqo-ai/marqo/pull/251). Navigate through pages of results. Provide an extensive end-user search experience without having to keep results in memory! See usage [here](https://docs.marqo.ai/0.0.11/API-Reference/search/#search-result-pagination)
- The `/models` endpoint (https://github.com/marqo-ai/marqo/pull/239). View what models are loaded, and on what device. This lets Marqo admins examine loaded models and prune unneeded ones. See usage [here](https://docs.marqo.ai/0.0.11/API-Reference/models/)
- The `/device` endpoint (https://github.com/marqo-ai/marqo/pull/239). See resource usage for the machine Marqo is running on. This helps Marqo admins manage resources on remote Marqo instances. See usage [here](https://docs.marqo.ai/0.0.11/API-Reference/device/)
- The index settings endpoint (`/indexes/{index_name}/settings`)(https://github.com/marqo-ai/marqo/pull/248). See the model and parameters used by each index. See usage [here](https://docs.marqo.ai/0.0.11/API-Reference/settings/).
- Latency log outputs (https://github.com/marqo-ai/marqo/pull/242). Marqo admins have better transparency about the latencies for each step of the Marqo indexing and search request pipeline
- ONNX CLIP models are now available (https://github.com/marqo-ai/marqo/pull/245). Index and search images in Marqo with CLIP models in the faster, and open, ONNX format - created by Marqo's machine learning team. These ONNX CLIP models give Marqo up to a 35% speedup over standard CLIP models. These ONNX CLIP models are open sourced by Marqo. Read about usage [here](https://docs.marqo.ai/0.0.11/Models-Reference/dense_retrieval/#onnx-clip).
- New simple [image search](https://github.com/marqo-ai/marqo/blob/mainline/examples/ImageSearchGuide/ImageSearchGuide.md) guide (https://github.com/marqo-ai/marqo/pull/253, https://github.com/marqo-ai/marqo/pull/263).


Contributor shout-outs
- ⭐️ We've just hit over 2.1k GitHub stars! ⭐️ So an extra special thanks to our stargazers and contributors who make Marqo possible.

New features
- Generic model support (https://github.com/marqo-ai/marqo/pull/179). Create an index with your favourite SBERT-type models from HuggingFace! Read about usage [here](https://marqo.pages.dev/0.0.10/Models-Reference/dense_retrieval/#generic-models)
- Visual search update 2. (https://github.com/marqo-ai/marqo/pull/214). Search-time image reranking and open-vocabulary localization, based on users' queries, is now available with the Owl-ViT model. **Locate the part of the image corresponding to your query!** Read about usage [here](https://docs.marqo.ai/0.0.10/Models-Reference/reranking/)
- Visual search update 1. (https://github.com/marqo-ai/marqo/pull/214). Better image patching. In addition to faster-rcnn, you can now use yolox or attention based (DINO) region proposal as a patching method at indexing time. This allows localization as the sub patches of the image can be searched. Read about usage [here](https://docs.marqo.ai/0.0.10/Preprocessing/Images/).

Check out [this article](https://medium.com/jesse_894/image-search-with-localization-and-open-vocabulary-reranking-using-marqo-yolox-clip-and-owl-vit-9c636350bf66) about how this update makes image search awesome.

Bug fixes
- Fixed imports and outdated Python client usage in Wikipedia demo (https://github.com/marqo-ai/marqo/pull/216)

Contributor shout-outs
- Thank you to [georgewritescode](https://github.com/georgewritescode) for debugging and updating the Wikipedia demo
- Thank you to our 1.8k stargazers and 60+ forkers!

Optimisations
- Set k to limit to for Marqo-os search queries (https://github.com/marqo-ai/marqo/pull/219)
- Reduced the amount of metadata returned from Marqo-os, on searches (https://github.com/marqo-ai/marqo/pull/218)

Non-breaking data model changes
- Set default kNN m value to 16 (https://github.com/marqo-ai/marqo/pull/222)

Bug fixes
- Better error messages when downloading an image fails (https://github.com/marqo-ai/marqo/pull/198)
- Bug where filtering wouldn't work on fields with spaces (https://github.com/marqo-ai/marqo/pull/213), resolving https://github.com/marqo-ai/marqo/issues/115

New features
- Get indexes endpoint: `GET /indexes` ([181](https://github.com/marqo-ai/marqo/pull/181)). Use this endpoint to inspect
existing Marqo indexes.
Read about usage [here](https://docs.marqo.ai/API-Reference/indexes/#list-indexes).
- Non-tensor fields([161](https://github.com/marqo-ai/marqo/pull/161)).
During the indexing phase, mark fields as non-tensor to prevent tensors being created for them.
This helps speed up indexing and reduce storage for fields where keyword search is good enough. For example: email, name
and categorical fields. These fields can still be used for filtering.
Read about usage [here](https://docs.marqo.ai/API-Reference/documents/#query-parameters).
- Configurable preloaded models([155](https://github.com/marqo-ai/marqo/pull/155)).
Specify which machine learning model to load as Marqo starts. This prevents a delay during initial search and index commands after
Marqo starts. Read about usage [here](https://docs.marqo.ai/Advanced-Usage/configuration/#preloading-models).
- New [example](https://github.com/marqo-ai/marqo/tree/mainline/examples/GPT3NewsSummary)
and [article](https://medium.com/creator-fund/building-search-engines-that-think-like-humans-e019e6fb6389):
use Marqo to provide context for up-to-date GPT3 news summary generation
([171](https://github.com/marqo-ai/marqo/pull/171), [#174](https://github.com/marqo-ai/marqo/pull/174)).
Special thanks to [iain-mackie](https://github.com/iain-mackie) for this example.

Bug fixes and minor changes
- Updated developer guide ([164](https://github.com/marqo-ai/marqo/pull/164))
- Updated requirements which prevented Marqo being built as an arm64 image ([173](https://github.com/marqo-ai/marqo/pull/173))
- Backend updated to use marqo-os:0.0.3 ([183](https://github.com/marqo-ai/marqo/pull/183))
- Default request timeout has been increased from 2 to 75 seconds ([184](https://github.com/marqo-ai/marqo/pull/184))

Contributor shout-outs
- For work on the GPT3 news summary generation example: [iain-mackie](https://github.com/iain-mackie)
- For contributing the non-tensor fields feature: [jeadie](https://github.com/jeadie)
- Thank you to our users who raise issues and give us valuable feeback
- Thank you to our 1.4k+ star gazers and 50+ forkers!