bootcamp

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 eval

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

If you are not using GCP or you want to have an open-source alternative, Please check my project Milvus vector database (https://milvus.io). We've published a bunch of demo cases powered by vector database on GitHub. https://github.com/milvus-io/bootcamp We have built Milvus vector database upon ANN libraries like faiss, annoy, nsmlib, etc. We are aiming to create a cloud-scalable vector database. So Milvus comes to the crossroad of vector search and cloud database. There are many interesting system design topics in the development of Milvus 2.0. We will continue to share our experiences and thoughts on this topic.

check this out: https://github.com/milvus-io/bootcamp/blob/master/EN_solutio...

We have some test reports in https://github.com/milvus-io/milvus/tree/master/tests At this moment, the IVF indecies are based on FAISS. So the performance is the same as Faiss. IVF_SQ8H is the reconstruction from Faiss IVF SQ8. Performance is much better, but you need GPU for it. We provide benchmark test procedures and tools. Please check this: https://github.com/milvus-io/bootcamp/tree/master/EN_benchma...