bootcamp VS txtai

Metadata Filtering with Zilliz Cloud Pipelines This tutorial discuss scalar or metadata filtering and how you can perform metadata filtering in Zilliz Cloud. This blog continues on the previous blog on Getting started with RAG in just 5 minutes. You can find its code in this notebook and scroll down to Cell #27.

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.

Although it's common to use vector databases in conjunction with LLMs, I like to talk about vector databases in the context of unstructured data, i.e. any data that you can vectorize with (or without) an ML model. Yes, this includes text, but it also includes things like visual data, molecular structures, and geospatial data.

For folks who want to learn a bit more, there are examples of vector database use cases beyond semantic text search in our bootcamp: https://github.com/milvus-io/bootcamp

Easy to get started: Here are some tutorials for Milvus in a Jupyter Notebook that I wrote - reverse image search, semantic text search

I think you can just store your vector embeddings in the vector store somewhere and then query with your second document. I created a short tutorial on this that shows how to get the top 2 vector embeddings from a text query

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

I tend to agree with this sentiment. Many junior devs and/or those in college want to contribute. Then they feel entitled to merge a PR that they worked hard on often without guidance. I'm all for working with people but projects have standards and not all ideas make sense. In many cases, especially with commercial open source, the project is the base of a companies identity. So it's not just for drive-by ideas to pad a resume or finish a school project.

For those who do want to do this, I'd recommend writing an issue and/or reaching out to the developers to engage in a dialogue. This takes work but it will increase the likelihood of a PR being merged.

Disclaimer: I'm the primary developer of txtai (https://github.com/neuml/txtai), an open-source vector database + RAG framework

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.

Bootstrapping only works if you have the runway to do it and you don't feel the need to grow fast.

With NeuML (https://neuml.com), I've went the bootstrapping route. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. It's a "live within your means" strategy.

VC funding can have a snowball effect where you need more and more. Then you're in the loop of needing funding rounds to survive. The hope is someday you're acquired or start turning a profit.

I would say both have their pros and cons. Not all ideas have the luxury of time.

I agree that in many cases people are puffing their feathers to try to be something they're not (at least not yet). Some believe in the fake it until you make it mentality.

With NeuML (https://neuml.com), the website is a simple HTML page. On social media, I'm honest about what NeuML is, that I'm in my 40s with a family and not striving to be the next Steve Jobs. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. For me, authenticity and being genuine is most important. I would say that being genuine has been way more of an asset than liability.

I'll add txtai (https://github.com/neuml/txtai) to the list.

There is still plenty of room for innovation in this space. Just need to focus on the right projects that are innovating and not the ones (re)working on problems solved in 2020/2021.

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.

Nice project! I've long used Tika for document parsing given it's maturity and wide number of formats supported. The XHTML output helps with chunking documents for RAG.

Here's a couple examples:

- https://neuml.hashnode.dev/build-rag-pipelines-with-txtai

- https://neuml.hashnode.dev/extract-text-from-documents

Disclaimer: I'm the primary author of txtai (https://github.com/neuml/txtai).