Vector Databases simply explained! (Embeddings & Indexes)
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- Опубликовано: 8 июн 2024
- Vector Databases simply explained. Learn what vector databases and vector embeddings are and how they work. Then I'll go over some use cases for it and I briefly show you different options you can use.
Resources:
- Gentle introduction: frankzliu.com/blog/a-gentle-i...
- What is a vector database: www.pinecone.io/learn/vector-...
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00:00 - Intro
00:44 - Why do we need vector databases
01:29 - Vector embeddings and indexes
02:58 - Use cases
03:45 - Different vector databases
Vector Database Options:
- Pinecone
- Weaviate
- Chroma
- Redis
- Qdrant
- Milvus
- Vespa
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I just watched an IBM explanation of vector databases and came away lost. Then I watched yours, and got it right away. Point goes to you. ;)
Same here!
Yes, a video describing available VDBs in terms of, e.g. Open/Closed, simplicity of operation, and user interaction patterns (quality/expressiveness of API) would be great!
Seconded
ruclips.net/video/Yo-AzVpWrRg/видео.html
You may find it helpful to start with the time frame of the video above!!
Also important how to extend the vdb with custom distance functions
The concise, high-level explainer that I needed. Thanks.
Yes please, more on this topic, I would appreciate it.
👆
New to this world of machine learning I have to say job well done on explaining in simple terms. This was insightful and had pleasure watching it. Thanks!
Very useful. Now I can imagine what is a vector database. Thanks
Definitely! I'd love to see comparable benchmarks for common LLM and other tasks (i.e. transfer learning, use-cases in the context of fine-tuning, etc)
Let's see the more in depth comparison! Also would love to know your take on where it will go? Are they able to automatically generate vectors for your multimodal data already? Are there known companies using vector databases currently? Are there lightweight alternatives to the services you offered? (ie. a numpy verision of a vector database?)
Excellent overview. Many thanks!
Love your work Patrick. Definitely would like to see more on vector databases, especially when you would use one over an array or other options and the pros and cons of some of the types you mentioned (I.e. Pinecone, Milvus, etc.)
Straight forward and simple. Thanks! 😊
Yes, looking forward to a more in-depth video.
Brief and to the point. Great video.
This was a very clear explanation. Thank you!
Thank you Patrick.
Thanks, describe very simply what the vector database is and its uses.🥀
It would be great if you explained how to use vector databases to give LLM's long term memory! 🙏
Yes for a comparison video.
Nice summary on Vector databases. A comparison of Graph and Vector databases with specific use cases would also help. Thank you
Thank you, nice and short overview to get an idea of what a vector db is.
Simply explained. Thanks!
Great video, thanks! Short and exactly on point -- much appreciated.
Yeah, it'd be cool to see more in-depth comparison of the dbs.
Thank you for this video - just what I needed! If you haven't done one already, please do an explainer comparing. 🙏
Thank you so much, Patrick. Would love to watch a video detailing and comparing all VDBs.
Thanks, this is what I needed to understand the overall idea of vector db.
to the point and concise explanation !!
Perfectly clear. Thanks!
thanks for a such a detailed and easily understandable knowledge
Awesome explanation! Thank you
That was very helpful! Thank you!
yup!! looking forward to a detailed analysis and comparison
Would definitely be interested in more details, especially on self hosted VDBs
Explanation On point !!! Thanks
Thank You... It's a great explanation on Vector database. Please make a in depth videos on Pinecone & Redis vector databases
Thanks for putting this together! :)
This is a really good explanation and visualization
Definitely need a comparisio video and small example code for the top 3 Vector DB's used !!
By the way ,Fantastic walk through of the concept !!.
Thank you, more please :)
Thanks for the Video. you are awesome and very easy to understand what they are. I think Pinecone is quite popular so if there is a video about it, it would be great. Cheers
Good informative video. Thanks!
This was a really good video! Thanks so much :)
Great intro to VD! Would love to see a more in-depth video on some real-world use cases :)
Super helpful!
Yes please, a VDB comparison would be great, and please include FAISS and other self-hosted options.
An in-depth comparison would be great!
Great video thank you!
Good explanation. Thumbs up 👍
Thanks for a nice video!
Would be great to learn more on how one could use Redis and PostgreSQL as vector databases.
Additionally, more examples and use cases for vector databases would be cool.
Great video… please go on with the next one
Thanks for the video 👍
I would love to see a comparison of the different Vector Databases!
I would love to see a comparison of the different VDB's and perhaps your thoughts on which one or two are the best. Thanks for a great video.
comparison video for the mentioned VDBs at the end would indeed be awesome!
Incredible video
Yes please a video about that. Liked and subscribed
nice video - thanks!
Great video! thank you!
A big YES for a Vector DB dedicated video
Btw I am happy I have found this channel, let's subscribe !
yeah nitty gritty indexing options overview and use cases for said options would be higlhy appreciated
It could be interesting to see a case of adding a vector dbase to an existing sql database, if it can replace it, or if a parallel approach might be interesting, using them side by side, each taking advantage of strenghts. etc.
I love the video. One critique would be to set up further away from the background to possibly reduce the reverb you're getting
🎯 Key Takeaways for quick navigation:
00:41 📊 Vector databases store Vector embeddings for fast retrieval and similarity search.
01:07 📝 Unstructured data like images, text, and audio can be challenging to store in relational databases, making vector databases valuable.
02:02 🔍 Vector embeddings allow for finding similar items by calculating distances and performing nearest neighbor searches.
03:10 🗂️ Vector databases have various use cases, including equipping language models with long-term memory, semantic search, similarity search, and recommendation engines.
03:50 💽 Examples of vector database options include Pinecone, Chroma, Redis, Cool, Trans, Milvus, and Vespa AI, each with its strengths and capabilities.
woud love to see detailed comparison of the vector databases
thank you
Great one
Please continue..)
Good topic 🎉
Cool, please explain more details about each vector db thanks
A breakdown of differences between vector databases would be nice. But also a comparison to graph databases like neo4j and TitanDB et al would help this n00b
This is a great explanation. But the indexing part is what I was looking for. Nearest neighbor search is already a hard problem in Computer Graphics and gaming (to detect collisions. E.g. if you ever play Madden and do a slow-mo replay, you'll see that the receiver never actually touches the ball. or E.g. cloth simulations for a cape often "clip" into the 3d model of the person wearing the cape).
Why isn't KX mentioned in this overview? They have a very strong vector database and support time-series data as well. Formula 1, manufacturing, utilities, and all the banks use them.
Very helpful animations:)
How did you do them with exalidraw, if I may ask?
This is like that scene from the Matrix where Neo stops the bullets and he sees the Matrix(humans, objects alike) as lines of code.
We are now converting objects like banana and apples into a bunch of numbers which even we can no longer understand looking at them via the vector embedding.
It would be great to see a comparison of the vector database companies
thanks, you have a video for the comparate diferences quality between?
helpful >>
Supabase also joined the vector DB club a while ago.
I would love to see.. what is the Best Vector database... ease of use vs performance. and why. This way we can stop guessing which one to try to use and just know this one is by Standard the best.
good video
Could you provide an overview on the comparison of different Vector Database providers and how to decide which is better?
i would love to know more
you can make a mor explication of diferences and optimitzacions cases :) thanks!
I remember working on a vector database in the mid 1980s. That was a Pick system, mostly used for accounting, warehouse management and the like. Re-innovation. 😁
I would enjoy seeing a comparison among these different vector databases. Today I just picked the one that’s most convenient. But there’s probably a better rationale for choosing among them. The other topic I’d like to see is sustainability. For example, if I’m adding a new vector to the database once a week what will happen after 10 years? Is that a sustainable growth when I have a 1016 element vector everyweek of the year or do I need to do something to re-index the database so that my performance doesn’t drop after a number of years?
The data I’m creating now would be relevant for many decades.
Yes please!
I say what Bob says. Thanks Bob.
Yes please, i habe to decide soon which database, redisearch is cloud only, pinecone too i think
Would love an explanation of indexing and how to use this with an LLM
Great content.I noticed the Elastic name is missing from the list of vector databases. Could you please include it in the list?
A comparison of their underlying architecture would be useful.
Yes, please.
You would need to upload ur own embeddings to these db though? Or do they calculate it for you in a multimodal way? Pinecone seems like the former? If so, why not just host locally in your Postgres?
Vector DB’s do not get around LLM context size limitations, but it seems like that’s the hot use case for them. Embeddings are not useful until they’ve been transformed though a neural network. I keep looking at these weird use cases like Langchain and I’m baffled people accept their wide margin of failure.
Please explain further, any one of the vector databases with an example for each Weaviate, Pinecone..
yes please!
Tx
I want to know how indexes work. How does the vector of the search prompt get mapped via index?
I would like to see a practical application example. Adding vector database info into a group of images and how it's searched for.
In LLM, I'm facing a token limit issue. With the vector database, will I be able to overcome token issues in llm?
Which vectors, you are explaining my vectors of my matrix?
I noticed a couple weird effects. When uploading documents to free private gpt the LLM (all of them) would hallucinate when asking simple questions like list the book titles and authors. They would come up with more titles than actually uploaded and complete fantasy titles. This does not happen with ChatGPT! Where lies the problem?