ChocoluvH

anno scorsodiscuss

Here's my process:

1. Use ChatGPT-4o, upload a picture (for me I had a Titanic poster, you know..) with prompt "convert this photo to studio ghibli style anime."

2. Visit Movii.ai, log in with Google, upload the generated image as the first frame, pick a model (e.g. Wan/Vidu), choose 720p resolution, and set the duration to 5 seconds. I generated over 10 videos, totally free, no cc needed

3. Wait about a minute and voilà!

Check out what I got! (was very excited about this!!) =]

https://movii1s1.mai-cdn.com/projectt/video/655635988205062_...

I have tried AI tools like Freepik and Pollo.ai in the last couple months, Movii.ai is very new but feels cleaner and has no hidden tricks.

LGTM so far.

ChocoluvH

3 anni fadiscuss

What does Chroma lack? Their APIs seem pretty much the same to me.

ChocoluvH

3 anni fadiscuss

Always wondering pros/cons of Chroma and Qdrant. Can someone tell me?

ChocoluvH

3 anni fadiscuss

haha. That case you might actually wanna consider FAISS/Milvus instead of Redis.

ChocoluvH

3 anni fadiscuss

Cool webUI. Why is it not on Motif main site? https://motif.land/

ChocoluvH

3 anni fadiscuss

There's no such thing as open internet

ChocoluvH

3 anni fadiscuss

Damn. Time to HODL?

ChocoluvH

3 anni fadiscuss

Don't start with Milvus clustered version, not unless you have like 100million vectors.

Try Milvus standalone instead, much simpler. I also just found their python version (https://github.com/milvus-io/embd-milvus), which is quite neat.

ChocoluvH

3 anni fadiscuss

Open source software nowadays are very easy to use.

If your guy couldn't get a single open source software straight, you had the wrong guy :(

I can only see managed service useful when I had 100X traffic and when strong SLA is required.

ChocoluvH

3 anni fadiscuss

Certainly!

The thing is to make ElasticSearch scores "comparable" to Milvus scores. Lots of ways to do this, but there's no single good solution. For example you could calculate BM25 score offline, or use TF-IDF score to do some kind of filtering. Again there's no single perfect answer. You'd have to do a lot of experiment according to your own use case and your own data to get the best results.

Also a lot of tuning needs to be done during all phases: 1) query pre-processing 2) query tokenizing 3) retrieval 4) ranking and reranking

I personally would not trust any universal "hybird-search" solutions. All toy demos.

It usually takes 5-10 good engineers to build a decent search engine/system for any real use case. It also requires a lot of turning, tricks, hand-written rules to make things work.

ChocoluvH

3 anni fadiscuss

IMO vector databases should not mess with ElasticSearch.

The real focus should be to improve the recall of vector search. Pity that nobody is doing real AI research here. Money wasted in marketing and branding.

ChocoluvH

3 anni fadiscuss

I personally like Milvus very much.

My point is I only trust stuff that focuses their own business. Especially for small startups.

ChocoluvH

3 anni fadiscuss

Totally agree. The thing is that ElasticSearch does not meet our requirements in vector searching.

I am currently running with Milvus + ElasticSearch, works perfect. The latest Milvus version is super fast and scalable (>50M vectors). Haven't tried Zilliz Cloud. Have to find out what the cost is.

I am old school. IMO ElasticSearch is only good for keyword search and these so called "vector databases" products are only good for vector search.

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