docarray VS bootcamp

For some alternatives, I know there’s DocArray where you can handle text, image and audio data. is basically a toolbox for multimodal data and then there should be Haystack which is also let you build search systems and also has to do something with Transformers and LLMs.

DocArray to manipulate data and interact with the storage backend using document store.

It's already been two weeks since the last alpha release of DocArray v2. And since then a lot has happened — we've merged features we're really proud of, and we've cried tears of joy and misery trying to coerce Python into doing what we want. If you want to learn about interesting Python edge cases or follow the advancement of DocArray v2 development then you’ve come to the right place in this blog post!

The German Fashion12k dataset is available for free use by the Jina AI community. After logging into Jina AI Cloud, you can download it directly in DocArray format:

The German Fashion12k dataset is available for free use by the Jina AI community. After logging into Jina AI Cloud, you can download it directly in DocArrayformat:

You could try spaCy. This is the brains of the operation - an open-source NLP library for advanced NLP in Python. Another is DocArray - It's built on top of NumPy and Dask, and good for preprocessing, modeling, and analysis of text data.

DocArray: Multimodal Data Library

As the description suggests on the project home page, DocArray is a library for nested, unstructured and multimodal data.

Hey reddit, we are Johannes and Sami from the DocArray team and we are super excited to announce version 2 of the DocArray library!

Document is the fundamental data structure. (This project is also an opensource project by the Linux Foundation)

Partially agree.

Vector DBs are critical components in retrieval systems. What most applications need are retrieval systems, rather than building blocks of retrieval systems. That doesn't mean the building blocks are not important.

As someone working on vector DB, I find many users struggling in building their own retrieval systems with building blocks such as embedding service (openai,cohere), logic orchestration framework (langchain/llamaindex) and vector databases, some even with reranker models. Putting them together is not as easy as it looks. A fairly changeling system work. Letting alone quality tuning and devops.

The struggle is no surprise to me, as tech companies who are experts on this (google,meta) all have dedicated teams working on retrieval system alone, making tons of optimizations and develop a whole feedback loop of evaluating and improving the quality. Most developers don't get access to such resource.

No one size fits all. I think there shall exist a service that democratize AI-powered retrieval, in simple words the know-how of using embedding+vectordb and a bunch of tricks to achieve SOTA retrieval quality.

With this idea I built a Retrieval-as-a-service solution, and here is its demo:

https://github.com/milvus-io/bootcamp/blob/master/bootcamp/R...

Curious to learn your thoughts.

For more beginner tasks associated with the Milvus vector database, you can contribute to the Bootcamp project( https://github.com/milvus-io/bootcamp), where we build a lot of data-driven solutions using ML and Milvus vector database, including reverse image search, recommender systems, etc.

Source code here: https://github.com/milvus-io/bootcamp/tree/master/solutions/reverse_image_search

Now, maintainer of a similarity search solution project: https://github.com/milvus-io/bootcamp and a contributor to Milvus vector DB project: https://github.com/milvus-io/milvus.

Here is the link to the project.

Working on a new solution for my open source project!! Bootcamp (https://github.com/milvus-io/bootcamp) is an open source project that aims to find a similarity search solution for all kinds of unstructured data.

Source code here! Deploy a reverse image search engine with ImageNet or your own dataset: https://github.com/milvus-io/bootcamp/tree/master/solutions/reverse_image_search